Warehouses Components

8/6/2019 Warehouses Components

1/21

Invited ReviewResearch on warehouse operation: A comprehensive review

Jinxiang Gu, Marc Goetschalckx * , Leon F. McGinnisSchool of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205, United States

Received 17 March 2005; accepted 12 February 2006Available online 3 May 2006

Abstract

An extensive review on warehouse operation planning problems is presented. The problems are classied according tothe basic warehouse functions, i.e., receiving, storage, order picking, and shipping. The literature in each category is sum-marized with an emphasis on the characteristics of various decision support models and solution algorithms. The purposeis to provide a bridge between academic researchers and warehouse practitioners, explaining what planning models andmethods are currently available for warehouse operations, and what are the future research opportunities. 2006 Elsevier B.V. All rights reserved.

Keywords: Facilities planning and design; Warehouse operation planning; Warehouse operation management; Warehouse operationdecision support models

1. Introduction

Warehouses are an essential component of anysupply chain. Their major roles include: bufferingthe material ow along the supply chain to accom-modate variability caused by factors such as prod-uct seasonality and/or batching in production andtransportation; consolidation of products from var-ious suppliers for combined delivery to customers;

and value-added-processing such as kitting, pricing,labeling, and product customization.Market competition requires continuous

improvement in the design and operation of produc-

tion-distribution networks, which in turn requireshigher performance from warehouses. The adoptionof new management philosophies such as Just-In-Time (JIT) or lean production also brings new chal-lenges for warehouse systems, including tighterinventory control, shorter response time, and agreater product variety. On the other hand, thewidespread implementation of new informationtechnologies (IT), such as bar coding, radio

frequency communications (RF), and warehousemanagement systems (WMS), provides new oppor-tunities to improve warehouse operations. Theseopportunities include, but are not limited to: real-time control of warehouse operation, easy commu-nication with the other parts of the supply chain,and high levels of automation.

A number of warehouse operation decision sup-port models have been proposed in the literature,but there remains considerable difficulty in applying

0377-2217/$ - see front matter 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.ejor.2006.02.025

*

Corresponding author. Tel.: +1 404 894 2317; fax: +1 404 8942301.

E-mail addresses: [email protected] (J. Gu), [email protected] (M. Goetschalckx), [email protected] (L.F. McGinnis).

European Journal of Operational Research 177 (2007) 121www.elsevier.com/locate/ejor
mailto:[email protected]:marc.%[email protected]:marc.%[email protected]:leon.mcginni%[email protected]:leon.mcginni%[email protected]:leon.mcginni%[email protected]:leon.mcginni%[email protected]:marc.%[email protected]:marc.%[email protected]:[email protected]


2/21

these models to guide warehouse operations. Theobjective of this paper is to classify and summarizethe prior research results, and to identify researchopportunities for the future. The intended outcomeis both a guide to practitioners on the analytical

methodologies and tools available to support betterwarehouse operation planning, and a roadmap foracademic researchers to future research oppor-tunities.

This paper presents a comprehensive review of the state-of-the-art in research on warehouse opera-tion planning. We rst present a unifying frame-work to classify the research on different butrelated warehouse problems. Within this frame-work, historical progress and major results are sum-marized with an emphasis on how the research onthese problems evolved and the relationshipsbetween various problems. Future research direc-

tions are identied and discussed. The scope of thispaper is restricted to warehouse operation-planningmethods. There are a lot of related results on perfor-mance evaluation, which we believe deserve a sepa-rate discussion since it is a key issue in warehouse

design and operation that provides the basis forintelligent decision-making. The companion paper(Gu et al., 2005) provides a detailed discussion onthis topic together with warehouse design, computa-tional systems, and case studies. Readers may alsorefer to Rowenhorst et al. (2000) for a recent surveyon the overall warehouse design and operationproblems.

2. Framework

The basic requirements in warehouse operationsare to receive Stock Keeping Units (SKUs) from sup-

Receiving(4*)

Storage(53)

OrderPicking

(67)

SKU-DepartmentAllocation

(4)

PerformanceEvaluation

Zoning(8)

Overall Structure

Shipping(4*)

EquipmentSelection

Sizing andDimensioning

Department Layout

Operation Strategy

StorageLocation

Assignment(41)

Batching(15)

Routing(48)

Sorting(4)

WarehouseDesign

WarehouseOperation

* This number represents papers on both receiving and shipping.

Fig. 1. Framework for warehouse design and operation problems.

2 J. Gu et al. / European Journal of Operational Research 177 (2007) 121


3/21

pliers, store the SKUs, receive orders from custom-ers, retrieve SKUs and assemble them for shipment,and ship the completed orders to customers. Thereare many issues involved in designing and operatinga warehouse to meet these requirements. Resources,

such as space, labor, and equipment, need to be allo-cated among the different warehouse functions, andeach function needs to be carefully implemented,operated, and coordinated in order to achieve systemrequirements in terms of capacity, throughput, andservice at the minimum resource cost.

A scheme to classify warehouse design and oper-ation planning problems and the corresponding lit-erature is shown in Fig. 1 (the numbers inparentheses represent the numbers of papersreviewed in this document for each operation plan-ning problem) and a more detailed description of each problem category identied is given in Table1. This paper will focus on the operation planning

problems, while warehouse design and performanceevaluation are discussed in Gu et al. (2005) .

Receiving and shipping are the interface of awarehouse for incoming and outgoing materialow. Incoming shipments are brought to the ware-

house, unloaded at the receiving docks, and put intostorage. Orders are picked from storage, prepared,and shipped to customers through shipping docks.Receiving and shipping operations involve, forexample, the assignment of trucks to docks andthe scheduling of loading and unloading activities.Research on receiving and shipping is very limited,and will be reviewed together in Section 3.

Storage is concerned with the organization of goods held in the warehouse in order to achieve highspace utilization and facilitate efficient materialhandling. Goods in storage can be organized intodifferent departments. The drivers of departmentorganization may be physical characteristics of the

Table 1Description of warehouse design and operation problems

Design and operation problems Decisions

Warehouse design Overall structure Material ow Department identication Relative location of departments

Sizing and dimensioning Size of the warehouse Size and dimension of departments

Department layout Pallet block-stacking pattern (for pallet storage) Aisle orientation Number, length, and width of aisles Door locations

Equipment selection Level of automation Storage equipment selection Material handling equipment selection

(order picking, sorting)Operation strategy Storage strategy selection

(e.g., random vs. dedicated) Order picking method selection

Warehouse operation Receiving and shipping Truck-dock assignment Order-truck assignment Truck dispatch schedule

Storage SKU-department assignment Assignment of items to differentwarehouse departments

Space allocationZoning Assignment of SKUs to zones

Assignment of pickers to zonesStorage location assignment Storage location assignment

Specication of storage classes(for class-based storage)

Order picking Batching Batch size Order-batch assignment

Routing and sequencing Routing and sequencing of order picking tours Dwell point selection (for AS/RS)

Sorting Order-lane assignment

J. Gu et al. / European Journal of Operational Research 177 (2007) 121 3


4/21

goods (e.g., pallet storage vs. case storage); manage-ment considerations such as a dedicated storagearea for a specic customer; or material handlingconsiderations such as a forward area for fast pick-ing. Within departments, goods may be further

organized into pick zones. A pick zone is a set of storage locations that are often arranged in closephysical proximity. A particular pick zone holds alimited subset of the SKUs, and pickers may be ded-icated to one or more zones to pick the requireditems. Because of the limited physical size of thezone, the picker achieves a high ratio of SKUextracting time to traveling time between locationsand an increased familiarity with the SKUs in thezone. Within a department/zone, goods are assignedto storage locations, and the storage location assign-ment has signicant impact on storage capacity,inventory tracking, and order picking. Differentstorage strategies can be used such as random,class-based, and dedicated storage. The selectionof which storage strategy to use is considered adesign problem and therefore is discussed in Guet al. (2005). However, the implementation of eachstorage strategy is an operational issue (e.g., usinga particular rule to assign SKUs to storage locationsfor dedicated storage), and therefore is discussed inSection 4.3.

Order picking is generally recognized as the most

expensive warehouse operation, because it tends tobe either very labor intensive or very capital inten-sive (Frazelle, 2002). Managing the order pickingprocess requires the organization of the orders tobe picked and of the material handling operationsof the picking. There are different order pickingmethods, for example, single-order picking, batch-ing with sort-while-pick, batching with sort-after-pick, sequential zone picking with single order,sequential zone picking with batching, concurrentzone picking without batching in the zones, andconcurrent zone picking with batching in the zones.The selection of an order picking method is a stra-tegic decision since it has a wide impact on manyother decisions in warehouse design and operation.For example, a downstream sortation system isneeded if sort-after-pick is used. The topic of orderpicking system selection is covered more thor-oughly in Gu et al. (2005). The focus here is atthe operational planning level of order picking.Several basic decisions need to be made at the oper-ational planning level, which include pick wave siz-ing, batching, routing, and sorting. The planning of batching, routing, and sorting will be discussed in

detail in Section 5. Research on pick wave sizingis very limited, and therefore will not be furtherdiscussed.

3. Receiving and shipping

Goods arrive to a warehouse in a carrier and areunloaded at the receiving docks. Later they areloaded into a carrier and leave the warehousethrough the shipping docks. For cross-dockingwarehouses, received goods are sent directly fromthe receiving docks to the shipping docks. For tradi-tional warehouses that hold inventory, receivedgoods are put away into storage and later pickedand shipped through shipping docks. In this case,the receiving and shipping operations are more

complex to manage since they are coupled withthe storage and order picking function. For exam-ple, the scheduling of shipping trucks may dependon how orders are batched and assigned to pickingwaves and vice versa.

The basic decisions in receiving/shipping opera-tions can be described as

Given:(1) Information about incoming shipments, such

as their arrival time and contents.(2) Information about customers demands, such

as orders and their expected shipping time.(3) Information about warehouse dock layout

and available material handling resources.

Determine:(1) The assignment of inbound and outbound car-

riers to docks, which determines the aggregateinternal material ows.

(2) The schedule of the service of carriers at eachdock. Assuming a set of carriers is assigned toa dock, the problem is similar to a machine-scheduling problem, where the arriving carri-ers are the jobs to be scheduled.

(3) The allocation and dispatching of materialhandling resources, such as labor and materialhandling equipment.

Subject to performance criteria and constraints suchas:

(1) Resources required to complete all shipping/receiving operations.

(2) Levels of service, such as the total cycle timeand the load/unload time for the carriers.



5/21

(3) Layout, or the relative location and arrange-ment of docks and storage departments.

(4) Management policies, e.g., one customer pershipping dock.

(5) Throughput requirements for all docks.

Decision making in receiving and shipping is lim-ited by the level of prior knowledge about incomingand outgoing shipments, for which the followingscenarios can be distinguished:

No knowledge, other than warehouse layout. Partial statistical knowledge of arriving and

departing processes, such as the average level of material ow from an incoming carrier to an out-going carrier.

Perfect knowledge of the content of each arrivingcarrier and each departing carrier.

In the rst scenario, not only do we have no basisfor assigning carriers to docks, we also have nobasis for assigning goods to storage locations. It isnot clear in this case if any storage assignment ruleis preferred to any other. Public warehouses mayoperate under this set of conditions. The second sce-nario is most common in company-owned or dedi-cated distribution warehouses and is the basis formost of the decision models in the literature. The

third scenario is becoming increasingly commonthrough the application of advanced informationtechnologies such as RFID, GPS, and advancedshipping notices (ASN).

The research on receiving and shipping has beenfocused on the carrier-to-dock assignment problemfor cross-docking warehouses, assuming statisticalknowledge of incoming and outgoing shipments.The cross-docking warehouse is operated as follows:inbound trucks arrive in the yard of the warehouseand proceed to the assigned receiving doors (or stripdoors) for unloading; the unloaded goods are sortedaccording to their destinations, and then loaded ontooutbound trucks at shipping doors (or stack doors)for delivery to customers. Often, each stack door isdesignated to a particular destination, and onceestablished, the designations of stack doors generallydo not change. The decisions for a cross-dockingwarehouse manager are then to designate the doorsas either strip or stack doors, assign destinations tostack doors, and assign inbound trucks to strip doorsin order to minimize the total operational cost.

Assuming the designations of doors as eitherstrip or stack doors have already been made, Tsui

and Chang (1990, 1992) formulate a bilinear modelto assign inbound and outbound trucks to stripand stack doors, respectively. Gue (1999) proposesa model to estimate the operational cost by opti-mally assigning inbound trucks to strip doors given

the specication of doors as either strip or stackdoors and the assignment of destinations to stackdoors. Based on the cost model, he uses a localsearch procedure to nd an efficient door layout.Bartholdi and Gue (2000) consider the cross-dock-ing warehouse door layout problem with the objec-tive of minimizing the total travel time and waitingtime incurred due to congestion. They model thetotal travel time and waiting time for a xed doorlayout using transportation and queuing modelsand then embed the cost model in a simulatedannealing algorithm to nd an efficient doorlayout.

In summary, very few formal models have beendeveloped for the management of shipping andreceiving operations. Most of the literature that isavailable in this area addresses shipping and receiv-ing operations and truck-to-dock assignment strate-gies for cross-docking warehouses.

4. Storage

Storage is a major warehouse function. Three

fundamental decisions shape the storage function,i.e., how much inventory should be kept in thewarehouse for an SKU; how frequently and atwhat time should the inventory for an SKU bereplenished; and where should the SKU be storedin the warehouse and distributed and movedamong the different storage areas. The rst twoquestions lead to the lot sizing and staggeringproblems, respectively, which belong to the tradi-tional inventory control area and are not furtherdiscussed here. The readers may refer to Gallegoet al. (1996) and Hariga and Jackson (1996) for adetailed review. This section will focus on the stor-age assignment question, which includes the deci-sions of assigning SKUs to various storagedepartments and scheduling of inventory movesbetween the departments, of assigning SKUs to dif-ferent zones (zoning), and of the storage locationassignment within a department/zone. The twomajor criteria in making these decisions are thestorage efficiency, which corresponds to the hold-ing capacity, and the access efficiency, which corre-sponds to the resources consumed by the insertion(store) and extraction (order picking) processes.



6/21

4.1. Assigning SKUs across departments

A SKU may be stored in more than one ware-house department. The specication of departmentsis a design decision. Once the departments are spec-

ied, one needs to determine which SKU should bestored in which department, in what quantity, andwhat are the corresponding inter-departmentalmoves for that SKU. In some cases, this decisionis straightforward. For example, if a department isdedicated to a certain customer, then all SKUs forthat customer are assigned to that department; orif a SKU will be stored and picked only in unitsof pallets, then it will be assigned only to the palletstorage department. In other cases, a SKU could beassigned to multiple departments. These depart-ments are usually different in terms of their storageand material-handling capability. Therefore, a care-ful decision needs to be made in order to balance thetradeoff between storage and material handling costand capacities.

The forward-reserve problem belongs to this cat-egory and is a well-researched problem. It is a com-mon practice in warehousing to create a separatephysically compact forward (or fast pick) areafor picking high-demand, fast-moving products.This reduces order picking costs but at the expenseof requiring additional material handling to restock

the forward area from a reserve area, and additionalspace as storage is less efficient in the forward areathan in the reserve area. Furthermore, since the sizeof the forward area usually is limited, one needs todetermine which SKUs should be stored in the for-ward area and in what quantity.

Bozer (1985) rst introduces the problem of split-ting a pallet rack into an upper reserve area and alower forward picking area. Hackman and Rosenbl-att (1990) treat the problem of deciding which SKUsto assign to the forward area, and how to allocatespace among the assigned SKUs, given the forwardarea has a xed capacity. The objective is tominimize the total material handling costs of orderpicking and replenishing. They propose a knapsack-based heuristic to solve this problem and providesufficient conditions for optimality of this heuristic.Frazelle et al. (1994) extend the problem and solu-tion method of Hackman and Rosenblatt (1990)by treating the size of the forward area as a decisionvariable. The costs in their model include the equip-ment cost of the fast pick area (modeled as a linearfunction of its size), and the material handling costfor order picking and replenishment.

The above models assume the replenishment of aSKU can be done in a single trip. van den Berg et al.(1998) consider the problem for unit-load replenish-ments, i.e., only one unit can be replenished per trip.Assuming the forward area can be replenished

instantaneously there is no need to assign more thanone unit to the forward area. They consider ware-houses that have busy and idle periods, so it is pos-sible to reduce the number of replenishments duringbusy periods by performing replenishments in thepreceding idle periods. A knapsack-based heuristicis proposed to nd the set of SKUs to put in theforward area that minimizes the expected totallabor-time related to order-picking and replenishingduring a busy period.

4.2. Assigning SKUs across zones (zoning)

The zoning problem is to specify different storagezones within a department and assign SKUs to thespecied zones. It can be both a hard and a softdecision; it is a hard decision if it leads to zone-spe-cic storage technology selection and physicalarrangement, but it is a soft decision if it is simplyan organization of similar storage locations. Thus,zoning decisions fall in between warehouse designdecisions and warehouse operation decisions.

A primary reason for dividing a storage depart-

ment into zones is to organize order picking activ-ities (i.e., zone picking). The fundamentaladvantages of zone picking are the limited spacethe picker has to traverse to pick an order, theincreased familiarity of the picker with a subsetof the SKUs, and the reduced order picking timespan for an order if zones are picked in parallel.On the other hand, additional costs may beincurred in zone picking, caused by sorting in par-allel zone picking and by the queuing in sequentialzone picking. Storage needs to be planned for zonepicking to determine the specication (the number,size, and shape) of the zones and to assign SKUs tozones in such a way that minimizes the total orderpicking cost and balance the workloads acrosszones. The literature on the storage planning forzone picking is very limited. Gray et al. (1992) pres-ent a hierarchical framework for designing ware-houses with zone picking to determine thenumber of zones and pickers, zone sizes (storagespaces per zone), storage assignment across andwithin zones, and order batch size. The effects of zone shape (i.e., the number of aisles per zoneand the length of aisles) on operational cost is



7/21

investigated by Petersen (2002) with simulation. Itis shown that zone shape has a substantial impacton the operational cost depending on factors suchas the zone size and the batch size. Algorithmsfor assigning SKUs to zones can be found in Jane

(2000) and Jewkes et al. (2004). Jane (2000) pro-poses a simple heuristic approach that assignsSKUs to zones to balance the workloads of pick-ers. Jewkes et al. (2004) consider a specic sequen-tial zone picking method where pickers work athome bases within their zones and are required toreturn to their home bases after each pick. An opti-mal approach is proposed to determine the zones,the assignment of SKUs to zones, and the baselocations in order to minimize the expected totalorder picking cost.

4.3. Storage location assignment

The storage location assignment problem (SLAP)is to assign incoming products to storage locationsin storage departments/zones in order to reducematerial handling cost and improve space utiliza-tion. Different warehouse departments might usedifferent SLAP policies depending on the depart-ment-specic SKU proles and storage technology.The storage location assignment problem is for-mally dened as follows:

Given:(1) Information on the storage area, including its

physical conguration and storage layout.(2) Information on the storage locations, includ-

ing their availability, physical dimensions,and location.

(3) Information on the set of items to be stored,including their physical dimensions, demand,quantity, arrival and departure times.

Determine:The physical location where arriving items will bestored.

Subject to performance criteria and constraints suchas:

(1) Storage capacity and efficiency.(2) Picker capacity and efficiency based on the

picker cycle time.(3) Response time.(4) Compatibility between products and storage

locations and the compatibility betweenproducts.

(5) Item retrieval policy such as FIFO (rst-in,rst-out), LIFO (last-in, rst-out), BFIFO(batch rst-in, rst-out). When using theBFIFO policy, items that arrived in the samereplenishment batch are considered to be

equivalent.In typical warehouse operations, the physical

storage infrastructure and its characteristics areknown when planning the storage location assign-ment. The availability of storage locations is alwaysknown in automated warehouses and often knownin mechanized warehouses. SLAP can be dividedinto three classes depending on the amount of infor-mation known about the arrival and departure of the products stored in the warehouse: (1) item infor-mation, (2) product information, or (3) no informa-tion. Different operational policies exist for each of these classes, and their implementation and perfor-mance have been discussed extensively in the litera-ture. Most of the research has focused on unit-loadwarehouses. Of course, these SLAP policies can beapplied to non unit-load warehouses as well, but itis usually much more difficult to provide analyticalresults because of the complexity of computing theassociated material handling times and costinvolved in a non unit-load warehouse (e.g., whenbatching and routing are used).

4.3.1. Storage location assignment problem based on item information (SLAP/II)

In the SLAP/II problem, it is assumed that com-plete information is known about the arrival anddeparture time of the individual items. The resultingproblem is a specially structured Assignment Prob-lem (AP), where items are assigned to storage loca-tions. The special structure derives from theproperty that two items can occupy the same stor-age location, provided they do not occupy it at thesame time. This problem has been called the VectorAssignment Problem (VAP), since the occupation isno longer expressed as a single binary status vari-able but as a vector over the different time periods(Goetschalckx, 1998 ). The optimal solution of thisproblem for typical warehousing operations is com-putationally impractical because of the very largeproblem instances. The problem is of interest in aca-demic research on warehouse operations because itprovides a cost lower bound or performance upperbound. An example of a heuristic SLAP/II policyis the Duration-of-Stay (DOS) policy of Goe-tschalckx and Ratliff (1990) . In DOS-based policies,



8/21

the expected DOS of the i th unit of a SKU withreplenishment lot size Q is i /k for i = 1,2, . . ., Q ,where k is the demand rate of that SKU. Then theitems of all the different products having the short-est DOS are assigned to the closest locations.

Hence, the items of a single replenishment batchof a single product may not be stored together inthe warehouse.

4.3.2. Storage location assignment problem based on product information (SLAP/PI)

Often only product information is known aboutthe items to be stored, and items are instances of products. Products may be classied into productclasses, e.g. by size or usage rate. The assignmentproblem now assigns an individual item to a prod-uct class based on its product characteristics, andassigns a product class to storage locations. Thelocation of an item in its class is most often doneusing some simple rule, such as nearest location,or randomly. If the number of classes is equal tothe number of products, then this policy is calledDedicated Storage. If the number of classes isequal to one, it is called Random Storage. Other-wise, it is called Class-Based Storage, which mayhave any number of storage classes ranging fromtwo to the number of products minus one (25storage classes are commonly used in warehouse

operations).Different criteria can be used to assign a product(class) to storage locations. The three most fre-quently used criteria (see also Frazelle, 2002) are

(1) Popularity (dened as the number of storage/retrieval operations per unit time period). Forthe popularity policy, product classes areranked by decreasing popularity and the clas-ses with the highest popularity are assignedthe most desirable locations.

(2) Maximum inventory (dened as the maximumwarehouse space allocated to a product class).For the maximum inventory policy, productclasses are ranked by increasing maximuminventory and the classes with the lowest max-imum inventory are assigned the most desir-able locations.

(3) Cube-Per-Order Index (COI, which is denedas the ratio of the maximum allocated storagespace to the number of storage/retrieval oper-ations per unit time). The COI policy takesinto consideration both a SKUs popularityand its storage space requirement. Product

classes are ranked by increasing COI valueand the classes with the lowest COI are storedin the most desirable locations.

The implementation of the above policies

depends on the types of warehouse systems andtherefore may have different variations, forexample:

(1) If storage space is measured in units (e.g.,shelves and bays), each unit can be treated asan individual product by appropriately appor-tioning demand. This is most commonly usedin unit load warehouses (e.g., Hausman et al.,1976) and sometimes in less-than-unit-loadwarehouses (e.g., Jarvis and McDowell,1991). Since each unit load occupies the sameamount of storage space, the popularity policybased on the apportioned popularity is essen-tially the same as the COI policy. However,it is different from the popularity policy with-out apportioning. For example, suppose prod-uct A has three unit loads and a popularity of three picks per day, and product B has oneunit load and a popularity of two picks aday. The popularity policy without apportion-ing will rank product A ahead of product B.On the other hand, if product A is treated as

three products (denoted as A1, A2, and A3),each of them will have an apportioned popu-larity of 1 pick per day. So the popularity pol-icy based on the apportioned popularity willnow rank product B ahead of product A1,A2, and A3, which can be easily veried tobe equivalent to the COI policy.

(2) The denition of the most desirable loca-tions depends on the system as well as thetravel pattern. For example, if traversalrouting policy is used for traveling in a con-ventional multi-parallel-aisle system, the desir-ability of locations are measured in terms of aisles where the most desirable locations arein the aisle that is closest to the I/O point. Thisleads to the so-called organ pipe storage loca-tion assignment, for example, see Jarvis andMcDowell (1991).

The above three policies are simple and exibleenough to be implemented in different warehousesystems. Among them, the COI policy has beenthe most comprehensively studied one. The COIpolicy was rst described by Heskett (1963, 1964)



9/21

without a proof of its optimality. Kallina and Lynn(1976) discussed the implementation of the COI pol-icy in practice. It has been proved that the COI pol-icy is optimal in minimizing the material handlingcost in dedicated storage when some assumptions

are satised:(1) The objective is to minimize the long-term

average order picking cost.(2) The travel cost depends only on locations.

Examples that do not satisfy this assumptioninclude the case when the travel cost is itemdependent or when there are multiple I/Opoints, and products have different probabilityof moving from/to the I/O points, i.e., it doesnot satisfy the factoring assumption as denedin Mallette and Francis (1972) .

(3) When dual or multi-command order picking isused, there is no dependence between thepicked items in the same picking tour.

(4) Certain routing policies are assumed for multi-command order picking, e.g., Jarvis andMcDowell (1991) assume the traversal routingpolicy for the conventional multi-aisle orderpicking system.

(5) There are no compatibility constraints thatlimit the storage location assignment, e.g., cer-tain items must and/or cannot be put together.

Table 2 summarizes the results on COI-based

dedicated storage and its optimality in differentorder picking systems based on the above assump-tions; Table 3 provides a group of related heuristicalgorithms for dedicated storage when theseassumptions cannot be satised, and therefore theCOI rule is not directly applicable.

Comparing dedicated storage with random stor-age, the former has the advantage of locating fast-moving and compact SKUs close to the I/O points,and therefore is benecial for efficient material han-dling. However, it also requires more storage spacesince sufficient storage locations must be reservedfor the maximum inventory of each product.Class-based storage provides an alternative that isin between and has the benets of both dedicatedand random storage. The implementation of class-based storage (i.e., the number of classes, the assign-ment of products to classes, and the storagelocations for each class) has signicant impact onthe required storage space and the material handling

Table 2

COI-based dedicated SLAP policy and its optimality in different systemsSingle-command Dual-command Multi-command Carousel

COI rules andits variants

Mallette andFrancis (1972),Harmatuck (1976)

Malmborg andKrishnakumar (1987),Malmborg andKrishnakumar (1990)

Malmborg andKrishnakumar (1989),Jarvis and McDowell (1991)

Bengu (1995),Vickson (1996),Vickson and Lu (1998)

Table 3Other dedicated SLAP policies with different complications

Citation Problem summary Algorithm

Montulet et al. (1998)Lee (1992), Rosenwein (1994),

Brynzer and Johansson (1996),van Oudheusden and Zhu (1992),Liu and Lu (1999)

The objective is to minimize the peak operations cost Branch and BoundItems are not independent such that some items are morelikely to appear on the same order

Cluster analysis; Space llingcurve based heuristics

Malmborg (1995) All items of any SKU must be located in the sameaisle in a multi-aisle AS/RS system

Random search plussimulated annealing

Lai et al. (2002), Zhang et al. (2000),Zhang et al. (2002)

Storage location assignment is constrainedby product size; all items of the same product mustbe placed at adjacent locations;and travel costs are item dependent

Simulated annealing;Genetic algorithms

Hwang et al. (2003) Product weight is considered and theobjective is to minimize the work (a function

of weight and distance) involved in order picking

A heuristic similar to COI



10/21

cost in a warehouse. Research on this problem hasbeen largely focused on AS/RS, especially single-command AS/RS. Hausman et al. (1976) show thatfor single-command AS/RS with the Chebyshevmetric, the ideal shape of storage regions is L-

shaped. For such systems, the problem reduces todetermining the number and boundaries of the clas-ses. Explicit analytical solutions for the class bound-aries can be derived for the case with 2 or 3 classes,as shown by Hausman et al. (1976), Kouvelis andPapanicolaou (1995), and Eynan and Rosenblatt(1994). For the general n-class case, Rosenblatt andEynan (1989) and Eynan and Rosenblatt (1994)suggest a one-dimensional search procedure to ndthe optimal boundaries. The implementation of class-based storage in multi-command AS/RS isdiscussed in Guenov and Raeside (1992) .

4.3.3. Storage location assignment problem based on no information (SLAP/NI)

If no information is available on the characteris-tics of the arriving items, only very simple storagepolicies can be constructed. In this case the most fre-quently used policies are (1) Closest-Open-Location(COL), (2) Farthest-Open-Location (FOL), (3)Random (RAN), and (4) Longest-Open-Location(LOL). The rst two policies pick an open locationbased on its distance to the receiving dock; the last

policy picks the location that has been vacant forthe longest time. It is not known if there is any sig-nicant performance difference between them.

4.3.4. SLAP summaryIn practice, SLAP/PI is much more common

than SLAP/II and SLAP/NI. Random, dedicated,and class-based storage are three popular used stor-age strategies, and each of them has its advantagesand disadvantages. The selection of storage strategyis a strategic decision, which affects warehousedesign and has long-term effects. For example, if random storage is used instead of dedicated storage,the warehouse might have a smaller size but requiremore effort to accurately track the inventory. Thistopic is further discussed in Section 2.5 of Guet al. (2005).

Once a storage strategy is selected, its implementa-tion is an operational problem. The implementationof random storage is relatively straightforward. Fordedicated and class-based storage, the implementa-tion involves assigning products/classes to storagelocation. The COI policy has been extensively stud-ied in the literature and is considered as more effective

than the other two policies. In class-based storage,additional decisions are to determine the number of classes and to assign products to classes. Currentresults on these decisions have been focused mainlyon AS/RS and need to be further developed for other

storage technologies.All of the above research on SLAP assumes thatreplenishment lot sizes of the SKUs are given. How-ever, Wilson (1977) demonstrates that the lot sizingproblem and the SLAP should be considered simul-taneously in order to achieve an optimal total costincluding both inventory cost and material handlingcost. Algorithms for the integrated lot sizing andSLAP problem can be found in Wilson (1977),Hodgson and Lowe (1982), Malmborg et al.(1986), Malmborg and Deutsch (1988), and Malm-borg et al. (1988).

The version of the SLAP problem studied in theliterature is most often static, i.e., it assumes that theincoming and outgoing material ow patterns arestationary over the planning horizon. In reality,the material ow changes dynamically due to fac-tors such as seasonality and the life cycles of prod-ucts. Therefore, the storage location assignmentshould be adjusted to reect changing material owrequirements. One possibility is to relocate thoseitems whose expected retrieval rate has increased(decreased) closer to (farther from) the I/O point.

Such relocations are only benecial when theexpected saving in order picking outweighs the cor-responding relocation cost. Therefore, decisionsmust be made carefully concerning which set of items to be relocated, where to relocate them, andhow to schedule the relocations. Another type of relocation might take place as a result of the uncer-tainty in incoming shipments. For example, Rolland Rosenblatt (1987) describes the situation whenthe storage area is divided into separate zones andany incoming shipment must be stored within a sin-gle zone. It might happen that none of the zones hassufficient space to accommodate an incoming ship-ment. In such cases, it is advisable to free somespace in a certain zone to accommodate the incom-ing shipment by shifting some stored products inthat zone to other zones. Table 4 gives a summaryof the literature on various dynamic storage loca-tion assignment problems.

5. Order picking

Different order picking methods can be employedin a warehouse, for example, single-order picking,



11/21

batching and sort-while-pick, batching and sort-after-pick, single-order picking with zoning, andbatching with zoning ( Yoon and Sharp, 1996 ). Eachorder picking method consists of some or all of thefollowing basic steps: batching, routing andsequencing, and sorting.

5.1. Batching

The batching problem is part of planning fororder picking. Orders are received and subsequentlyreleased for fulllment. Given a set of releasedorders, the problem is to partition the set intobatches, where each batch will be picked and accu-mulated for packing and shipping during a specictime window, or pick wave. The time requiredto pick the items in any batch should not exceedthe time window or pick wave duration. If zonepicking is employed, the batch should balance pickeffort across the zones to achieve high picker utiliza-tion, while minimizing pick time so that the numberof pickers required is minimized.

The batching problem can be stated as

Given:(1) Warehouse conguration.(2) Pick wave schedule.(3) A set of orders to pick during a shift.

Determine:A partition of orders for assignment to wavesand pickers.

Subject to performance criteria and constraintssuch as:

Picker effort, imbalance among pickers, timeslots, picker capacity, and order due dates.

In creating an abstract statement of the problem,there are potentially two levels of partitioning: (1)partitioning in time (into pick waves); and (2) parti-

tioning among pickers in a wave or zone. Con-straints include the picker capacity during the timeinterval associated with a pick wave, and perhapstime constraints on when an order should becompleted.

Partitioning into time slots is essentially a binpacking type problem, where the goal is to balancethe pick time among the time slots or pick waves.The difficulty, of course, is that the time requiredto pick a batch is not known until the batch hasbeen determined, partitioned among individualpicker, and the pickers have been routed throughthe warehouse.

Partitioning of the orders among the pickers is avariation of the classical vehicle routing problem(VRP), in which stops are assigned to routesand the objective is to minimize the total route dis-tance or time. However, in the order-batching prob-lem, assigning an order to a pickers route impliesthat all the picking locations for the SKUs in thisorder are assigned to this route. This is similar tothe pick-up and delivery vehicle routing problem,or the dial-a-ride problem, where a service requestconsists of a pick-up location and a drop-off location

Table 4Dynamic storage location assignment problem

Citation Problem statement Method

Christodes and Colloff (1972) The set of items to be relocated and their destinationsare given, and the problem is to route the relocationtour to minimize the total relocation cost

Two-stage heuristics that isoptimal in a restricted case

Muralidharan et al. (1995) The set of high-demand items to be relocated and theirdestinations are given, and the problem is to route therelocation tour to minimize the total relocation cost

A nearest-neighbor heuristicand an insertion heuristic

Jaikumar and Solomon (1990) Determine the items to be relocated and their destinationswith the objective to nd the minimum number of relocationsthat results in a throughput satisfying the throughputrequirement in the following busy periods

Optimal ranking algorithm

Sadiq et al. (1996) Determine the relocation schedule in face of the dynamicallychanging order structure, i.e., relocate items that are more likelyto appear in the same order in clusters

Rule of thumb procedurebased on cluster techniques

Roll and Rosenblatt (1987) Using zone storage without splitting, it might happen that noneof the zones has sufficient space to accommodate an incomingshipment. The problem is how to shift some stored products in

a certain zone to other zones in order to freespace for the incoming shipment

Rule of thumb procedure



12/21

with time precedence. In the order partitioningproblem, there may be many stops (SKUs) associ-ated with a single service request (order) but thereare no precedence constraints.

The published research has focused primarily on

the problem of partitioning among pickers. Thereare two major types of batching heuristics thatattempt to minimize total picking effort and arebased on VRP heuristics. A seed algorithm selectsinitially a single seed order in the batch. Moreorders are then added according to a route closenesscriterion until no more orders can be added due to acapacity constraint. The capacity constraint can bebased on total pick time, number of orders in thebatch, or weight. A savings heuristic starts byassigning each order to a separate batch. The algo-rithm then iteratively selects a pair of batches to becombined based on the savings of combining themuntil no more batches can be combined due to thecapacity constraint.

Central to both types of algorithms is an order-to-route closeness metric, which denes the orderaddition rule in the seed algorithms and the combi-nation rule in the saving algorithms. Table 5summarizes closeness metrics proposed in the litera-ture. The seed and savings algorithms proposed inthe literature are similar in terms of their generalprocedure, but differ in the closeness metric used.

Table 6 shows the different algorithms and the close-

ness metrics they used as shown by the bold numberafter each citation.

Many of the papers listed in Table 6 also provideperformance evaluation of the different batchingalgorithms using simulation. It is however difficultto draw general conclusions since the performancedepends heavily on factors such as storage locationassignment policies, routing policies, the structureof orders, storage systems, and the maximum batchsize. A comprehensive study that considers all the

above factors and the various batch constructionheuristics has not been published at this time. Afew results have been published where two policyclasses are studied jointly, for example, de Kosteret al. (1999) evaluate batching and routing algo-rithms together, and Ruben and Jacobs (1999) evalu-ate batching algorithms with different SLAP policies.

The majority of literature has been focused onthe objective of minimizing the total order pickingtime. In practice, there are might be other importantcriteria, for example, lead time and tardiness.Elsayed et al. (1993) present a heuristic for batchingorders that have due dates with the objective to min-imize earliness and tardiness penalties. Elsayed andLee (1996) consider batching and sequencing of both storage and retrieval orders such that the totaltardiness of the retrieval orders is minimized. Cor-mier (1987) propose a heuristic for batching andsequencing orders to minimize the weighted sumof order picking time and tardiness in an AS/RS.Won and Olafsson (2005) develop mathematicalmodels and heuristics that solve the joint problemof order batching and picking considering bothpicking efficiency and order lead time.

Table 5Order closeness metrics for batching

Index Closeness metric Example

1 Number of commonlocations between two orders

Elsayed (1981)

2 Combined number of locations of two orders

Elsayed andStern (1983)

3 Sum of the distance between eachlocation of one order and the closestlocation on the other order

Elsayed andStern (1983)

4 Difference of the order-theta valuesof two orders dened based onspace-lling curves

Gibson andSharp (1992)

5 The number of additionalaisles to travel when two ordersare combined

Rosenwein(1996)

6 Savings in travel when twoorders are combined

Elsayed andUnal (1989)

7 Center of gravity metric Rosenwein(1996)

8 Economic convex hull based metric Hwang andLee (1988)

9 Common covered regions or areas Hwang et al.

(1988)

Table 6Order batching heuristics by type

Seed algorithm Saving algorithm

Elsayed (1981) (1) Rosenwein (1996) (5, 7)Elsayed and

Stern (1983)(1, 2, 3) Hwang and

Lee (1988)(8)

Elsayed andUnal (1989)

(6) Elsayed andUnal (1989)

(6)

Gibson andSharp (1992)

(3, 4) de Kosteret al. (1999)

(6)

Hwang andLee (1988)

(8)

Hwang et al.(1988)

(9)

Pan andLiu (1995)

(1, 3, 4, 6, 8)

de Kosteret al. (1999)

(3, 5, 6, 7)



13/21

Very few papers have developed optimal orderbatching algorithms. Armstrong et al. (1979) presenta mixed-integer formulation for batching in a semi-automated order-picking system with the objectiveto minimize the total picking time. The model was

solved using Benders decomposition. Gademannet al. (2001) consider the order batching problemwith the objective of minimizing the maximum leadtime of the batches and solve the formulation opti-mally using a branch-and-bound algorithm. Gade-mann and van de Velde (2005) solve the batchingproblem to minimize the total order picking timeby formulating it as a set partitioning problem,and then solving it with a branch-and-price algo-rithm and an approximation algorithm. Chen andWu (2005) use a clustering approach to batch ordersthat are highly associated, i.e., orders sharing a largenumber of common items. They propose a methodto calculate the association between orders, whichis used in an integer-programming model to maxi-mize the total association measure.

5.2. Sequencing and routing

The sequencing and routing decision in orderpicking operations determines the best sequenceand route of locations for picking and/or storing agiven set of items. The objective is typically to min-

imize the total material handling cost. This problem

is a warehouse-specic Traveling Salesman Problem(TSP), where the picking/storing location of an itemis given. The problem where there are several candi-date locations for the retrieval or storage of an itemis more complex and few research results are avail-

able, although it is often found in practice. TheTSP in the warehouse is special because of the aislestructure of the possible travel paths. The publishedresearch focuses on four classes of warehouse sys-tems, i.e., conventional multi-parallel-aisle systems,man-on-board AS/RS systems, unit-load AS/RSsystems, and carousel systems.

5.2.1. Sequencing and routing for conventional multi-parallel-aisle systems

In a conventional multi-parallel-aisle system, theaisle structure limits the TSP state space, whichgreatly simplies its solution. Ratliff and Rosenthal(1983) propose a polynomial-time dynamic pro-gramming algorithm to optimally solve this prob-lem. The algorithm depends on the followingassumptions: parallel, narrow and equal aisles, asingle I/O point for the picker in the warehouse,the aisles connected by a cross aisle at each end,and the SKU locations given. Other authors haverelaxed some of these assumptions and proposeddifferent algorithms to deal with these complica-tions. These related results are summarized in Table

7, where Ratliff and Rosenthal (1983) is listed rst

Table 7Algorithmic routing approaches for conventional multi-parallel-aisle warehouses

Citation Problem setting Algorithm Optimal or not

Ratliff and Rosenthal (1983) Narrow aisles;A tour starts and endsat the central depot;Only two cross aisleslocated at the ends of picking aisles;Picking locations are given

A dynamic programmingbased algorithm

Optimal with computational timelinear in the number of aisles

Goetschalckx andRatliff (1988a,b)

Routing in wide aisles A shortest path algorithmand a set-covering basedalgorithm with the consecutiveones property

Optimal for the routingwithin a single aisle

de Koster and vander Poort (1998)

A tour can start and end at thehead of any picking aisle

An extension of Ratliff andRosenthal (1983)

Optimal

Roodbergen andde Koster (2001b)

There are three cross aisles An extension of Ratliff andRosenthal (1983)

Optimal

Vaughan and Petersen(1999), Roodbergen andde Koster (2001a)

There are arbitrary numberof cross aisles

Dynamic programmingbased heuristics

Heuristics

Daniels et al. (1998) Picking locations need to be

selected before routing

TSP based heuristics with

local search methods

Heuristics



14/21

with the assumptions they made and followed by theother results that relax some of the restrictiveassumptions (see the problem setting column inTable 7 ).

Although it is possible to construct optimal rout-

ing algorithms efficiently, simple heuristics such asthe traversal and return policies are widely used inpractice because they are easy to understand andthe resulting routes are more consistent. For the tra-versal policy, the picker will cross through the wholeaisle that contains at least one pick, and thereforealways enters at one end of the aisle and exits atthe other end. For the return policy, the pickeralways enters and exits at the same end of the aisle,and the same aisle may be entered twice from its twoends. Detailed description of these routing policiesand their variations can be found in Hall (1993)and Caron et al. (1998) . Performance evaluationof these different routing algorithms can be foundin Hall (1993) and Caron et al. (1998, 2000) basedon analytical models, and Petersen (1997, 1999),Petersen and Schmenner (1999), and Petersen andAase (2004) based on simulations.

5.2.2. Sequencing and routing for man-on-board AS/RS

The routing problem for man-on-board AS/RS isa TSP with a Chebyshev distance metric. The litera-

ture on this problem has been focused primarily onefficient heuristics. Gudehus (1973) describes theband heuristic, which divides the rack into twoequal height horizontal bands; the points in thelower band are visited in the increasing x-coordinatedirection, while the points in the upper band are vis-ited in the opposite direction. If the tour must visitmany points, the rack may be divided into severalpairs of horizontal bands. Goetschalckx and Ratliff (1988c) propose a convex hull algorithm based onthe property of Chebyshev metric that some pointsnot on the convex hull can be inserted into it with-out incurring additional travel distance. The algo-rithm constructs the convex hull of all the pickinglocations, then those free insertion locations foreach segment of the convex hull are identied andinserted into the convex hull, and then the remain-ing points are sequentially inserted into the tour ina way that minimizes the increase in tour lengthfor each insertion. The band algorithm is easy toimplement and computationally efficient, but mightgive inferior solutions in some cases. On the otherhand, the convex hull algorithm is effective in nd-ing short tours, but is difficult to implement (to nd

the convex hull and free insertion points) and lesscomputationally efficient.

Bozer et al. (1990) propose the 1/2 band insertionheuristic, which is a combination of the band andconvex hull heuristics. The heuristic rst divides

the rack into three equal width horizontal bands,all the points in the rst and third band are routedin the same way as in the band heuristic to obtaina partial tour, and the points in the middle bandare then inserted as in the nal stage of the convexhull algorithm. Other heuristics in the literatureinclude the center sweep heuristic ( Bozer et al.,1990), the space-lling curve based heuristic(Bartholdi and Platzman, 1988 ), and the combinedconvex hull heuristic for a variation of the man-on-board systems ( Hwang and Song, 1993 ). Localimprovement procedures ( Bozer et al., 1990; Makrisand Giakoumakis, 2003 ) can be used together withall the above heuristics to further reduce the tourlength.

Bozer et al. (1990) give a comprehensive compar-ison of these heuristics, and conclude that the con-vex hull and 1/2 band insertion heuristicsconsistently outperform the others, and suggestthe use of the 1/2 band heuristic because it achievesperformance close to that of the convex hull algo-rithm, but is very simple to implement and runs veryefficiently. Bachers et al. (1988) provide a compari-

son of several traditional TSP heuristics, such asthe nearest-neighbor method, the successive inser-tion method, and the local search method, throughsimulation.

Kim et al. (2005) study a special AS/RS systemthat is similar to the man-on-board system in thesense that it picks multiple items from the rack ineach cycle. The difference is that after each pick,the picked item must be put into a drop buffer thatis vertically below the picking location. The routingproblem for this system can also be formulated as aspecial TSP, and an x-coordinate based heuristicand a clustering based heuristic are proposed tosolve it.

5.2.3. Sequencing and routing for unit-load AS/RS The routing problem for unit-load AS/RS (also

called the interleaving problem) pairs a storageoperation with a retrieval operation for a dual com-mand cycle. Graves et al. (1977) demonstrate thatcareful interleaving can effectively reduce the totaltravel distance by reducing the unproductive travelbetween storage and retrieval locations. The algo-rithms reported in the literature are either static or



15/21

dynamic. Static algorithms x a block of storageand retrieval requests, sequence the requests in theblock, and execute the resulting schedule ignoringnew storage and retrieval requests. Dynamic algo-rithms re-sequence the storages and retrievals when-ever new requests arrive. The static sequencingproblem for randomized and class-based storage isbelieved to be NP-hard, and most algorithms for thisproblem use a nearest-neighbor heuristic or one of its variations. Han et al. (1987) proposed a matchof a storage location with a retrieval location thathas the minimum travel distance between them.Lee and Schaefer (1996) developed an assignment

formulation and can nd an optimum or near-optimum solution for problems of moderate size.The static case for dedicated storage policies canbe solved in polynomial time by formulating it as atransportation or assignment problem ( van den Bergand Gademann, 1999; Lee and Schaefer, 1997 ).Table 8 summarizes the static algorithms for differ-ent systems and storage policies. Dynamic algo-rithms in the literature are mainly direct extensionsof the static algorithms that re-sequence the requestswhenever a new request arrives in the system asreported by Lee and Schaefer (1997), Eben-Chaime(1992), and Ascheuer et al. (1999) . Seidmann(1988) proposes a different dynamic controlapproach based on articial intelligence techniques.

In some cases, Just-In-Time performance of theAS/RS is more important than minimizing the totaloperational cost. For example, if the AS/RS is usedto feed a production line, it is important that therequested materials are retrieved at the time deter-mined by the production schedule. Lee and Kim(1995) and Linn and Xie (1993) develop heuristicsto sequence the storage and retrieval requests inorder to improve the due date related performance.

Several authors have studied the dwell pointselection problem in a unit-load AS/RS. The dwellpoint is the position where the S/R shuttle stopswhen the system is idle. The dwell point can beselected to minimize the expected travel time tothe position of the rst transaction after an idle per-iod, and thus improve system response. Researchresults on this topic can be found in Bozer andWhite (1984), Egbelu (1991), Egbelu and Wu(1993), Chang and Egbelu (1997), Hwang and Lim(1993), Peters et al. (1996), and van den Berg (2002) .

Simulation studies of the operational policies foran unit-load AS/RS can be found in Linn and Wysk

(1987) and van den Berg and Gademann (2000) ,which compare different sequencing rules, dwellpoint selection rules, and storage location assign-ment rules under various conditions of the productmix and the traffic intensity.

5.2.4. Sequencing and routing for carousel systemsThe sequencing problem in carousel systems was

rst considered by Bartholdi and Platzman (1986) .They assume that the orders are picked one at atime, which leads to two sequencing problems, i.e.,the pick sequencing within an order and thesequencing of orders. The effect of the latter is notsignicant when the order arrival rate is small com-pared with the order retrieval rate, so the problemsimplies to the pick sequencing within the orders.They present a polynomial algorithm to optimallysolve this problem, as well as some simple heuristicsthat are easier to compute and perform well whenthe number of picks is large relative to the total stor-age space. When the order arrival rate is large, thesequencing of orders must be considered in minimiz-ing the unproductive time of traveling from the endposition of one order to the start position of the

Table 8Static sequencing algorithms for dual-command AS/RS

Citation Problem setting Algorithm Optimal or not

Randomized storage Han et al. (1987) Unit-load AS/RS Nearest-neighbor heuristic HeuristicLee and Schaefer (1996) Unit-load AS/RS Assignment-based algorithm e-optimalMahajan et al. (1998) Miniload end-of-aisle AS/RS Nearest-neighbor heuristic HeuristicKeserla and Peters (1994) Unit-load dual shuttle AS/RS Minimum-perimeter heuristic HeuristicSarker et al. (1991) Unit-load dual shuttle AS/RS Nearest-neighbor heuristic Heuristic

Dedicated storage van den Berg andGademann (1999)

Unit-load AS/RS Transportation problem Optimal

Lee and Schaefer (1997) Unit-load AS/RS Assignment problem Optimal

Class-based storage Eynan and Rosenblatt (1993) Unit-load AS/RS Nearest-neighbor heuristic HeuristicSarker et al. (1994) Unit-load dual shuttle AS/RS Nearest-neighbor heuristic Heuristic



16/21

next. In this case, an efficient heuristic is proposedbased on the additional assumption that each orderis picked along its shortest spanning interval, whichis the shortest interval that covers all the pickinglocations of the order. It is shown that the proposed

heuristic will produce a solution that is never morethan 1 revolution longer than the optimal, i.e., themore orders to be picked, the better the solution.

Ghosh and Wells (1992) and van den Berg (1996)consider the problem when the sequence of orders isxed (but the pick sequence within the orders is tobe determined), and propose efficient dynamic pro-gramming approaches to optimally solve it. vanden Berg (1996) also considers the case when bothin-order and between-order picking sequences areto be determined by assuming that each order ispicked along its shortest spanning interval. Theyformulate this problem as a Rural Postman Prob-lem on a circle and solve it to optimality. Further-more, they show that the solution obtained withthe extra constraint is at most 1.5 revolutions morethan the optimal without the extra constraint. Theabove research treats the carousel as a one-dimen-sional system, i.e., the travel perpendicular to therotation of the carousel was not considered. Wenand Chang (1988) consider a two-dimensional car-ousel system and propose three heuristics that areextensions of Bartholdi and Platzmans optimal

algorithm. Han and McGinnis (1986), and Hanet al. (1988) extend the nearest-neighbor heuristicsdiscussed earlier for the dual-command AS/RS tocarousels and rotary racks. (A rotary rack is similarto a carousel except that it has several layers, andeach layer can be operated independently.)

5.2.5. Sequencing and routing summaryIn summary, the sequencing and routing problem

is the most studied problem in warehouse operation.Most of the research assumes that the locations tobe visited are given. The problem when multiplecandidate locations are available for the retrievalor storage of an SKU remains an interesting andchallenging research problem (for example, seeDaniels et al., 1998). Also, in a warehouse setting,batching is closely related to sequencing, and there-fore those problems require a joint solution method.Furthermore, because of the conned and narrowtravel paths in a warehouse, another relevant vari-ant of the sequencing and routing problem wouldconsider congestion when there are multiple orderpicking tours executed at the same time period inthe same area.

5.3. Sorting

Sorting is required when multiple orders arepicked together. It can be performed either duringthe picking process (sort-while-pick) or after the

picking process (sort-after-pick). Sort-while-pick isquite straightforward and is typically modeled byinating the item extraction time. For sort-after-pick, a separate downstream sorting system is usedto perform the sorting function. A number of ques-tions are related to the operation of the sortingsystem.

Sorting systems used in warehouses usuallyinclude an accumulation conveyor, a recirculationconveyor, and exit lanes, and they operate simulta-neously on all the orders in a single pick-wave.Items for a pick wave arrive at the accumulationconveyor where they wait to be released into thesorting process. They are put onto the recirculationconveyor through an induction point after the itemsin the previous pick-wave nish their sorting process(in some cases, the items are allowed to enter therecirculation conveyor before the previous wavehas totally nished its sorting). The orders areassigned to sorting lanes according to order-to-laneassignment rules. Items circulate in the recirculationconveyor and enter the assigned sorting lane if allitems of the preceding order assigned to that lane

have been sorted. If not, the items bypass the sort-ing lane and re-circulate. Eventually, sorted ordersare removed from sorting lanes, checked, packed,and shipped. Therefore, the operation problem forsorting involves decisions such as wave-releasingand order-to-lane assignment so that the orderscan be efficiently sorted in a given wave.

There are relatively few research results in thisarea. Bozer and Sharp (1985) consider a system thatprocesses a relatively small number of large orders.In this case, each sorting lane is typically dedicatedto one order. The authors use simulation to analyzethe dependence of the system throughput on factorssuch as the induction capacity, the number of lanes,and the length of lanes. Bozer et al. (1988) considera similar problem but with a large number of smallorders. In this case, each lane is assigned severalorders and an order-to-lane assignment policydetermines how and when the orders enter the sort-ing lanes. Orders that are not yet assigned a lane areforced to recirculate. Using simulation, they com-pare different order-to-lane assignment rules, whichinclude the simplest FCFS rule and priority rulesbased on the sizes of orders or the time that an order



17/21

has been in the system. They nd that the FCFS ruleconsistently outperforms more elaborated rules.Johnson (1998) veries this result with analyticalmodels for the sorting system operated under differ-ent order-to-lane assignment rules. Meller (1997)

propose an optimal order-to-lane assignmentmethod to minimize the sorting time for a pick-wavebased on a set-partitioning model.

In practice, the sorting time in an automatic sort-ing system might not be a critical factor as long asall orders can be sorted within a given wave. There-fore, simple heuristics would suffice in most practi-cal cases if orders were partitioned into pick wavesin a balanced way.

6. Conclusions and discussions

The distribution of the research results amongthe various warehouse operational problems isshown in Fig. 1, where the numbers in parenthesesrepresent the number of papers addressing the cor-responding problem. It is clear that the pastresearch has focused strongly on storage and orderpicking. This is not surprising since these are thetwo warehouse functions that have the largestimpact on the overall warehouse operational perfor-mance including storage capacity, space utilization,and order picking efficiency.

On the other hand, the development of researchis not well balanced. Some problems received farmore attention from the research community thanothers. For example, the SLAP and routing prob-lems account, respectively, for 32% and 38% of the total surveyed literature, while zoning accountsfor less than 6%. Furthermore, there is little directevidence of collaboration of the academic researchcommunity with industry. Many of the researchresults are not sufficiently communicated to indus-try to make a signicant impact on the practice of warehouse operations. More communication fromboth sides might help to better identify the real chal-lenges faced in warehouse operations, to appreciatethe opportunities for better operation, and to realizethese opportunities by close cooperation betweenresearchers and practitioners.

The problems discussed in this paper are at theoperational level, which means that decisions needto be made quite frequently and the inuence of these decisions is typically of a short duration andlocalized. Such decisions typically need to be madequickly without extensive computational resources.This tends to encourage the use of heuristic proce-

dures that can nd a good solution reliably in a rea-sonable amount of time. In addition, from themanagement point of view, an ideal solutionmethod should be simple, intuitive, and reliable inorder to minimize the training costs in the

warehouse.Another consequence of the operational natureof the problems discussed in this paper is that theproblems should be considered dynamically by con-stantly incorporating new information about theoperating environments. Some research on thedynamic planning of warehouse operations exists,but the dynamic problems are much less studiedthan the equivalent static problems. Furthermore,research in the literature usually concentrates oncertain standard performance measures, such asthe total order picking cost. In many practical situ-ations, different objectives such as the tardiness, orthe order cycle time, are as important as the tradi-tional aggregate performance measure.

In summary, there continues to be a need forresearch focusing on the operational managementof warehousing systems, where the different pro-cesses in the warehouse are considered jointly, theproblems are placed in their dynamic nature, andmultiple objectives are considered simultaneously.Clearly, the research domain of warehouse opera-tions is very rich and challenging. Given the preva-

lence of warehouses in the supply chains, suchresearch results can have a signicant economicimpact.

References

Armstrong, R.D., Cook, W.D., Saipe, A.L., 1979. Optimalbatching in a semi-automated order picking system. Journalof the Operational Research Society 30 (8), 711720.

Ascheuer, N., Grotschel, M., Abdel-Hamid, A.A.-A., 1999.Order picking in an automatic warehouse: Solving onlineasymmetric TSPs. Mathematical Methods of Operations

Research 49 (3), 501515.Bachers, R., Dangelmaier, W., Warnecke, H.J., 1988. Selectionand use of order-picking strategies in a high-bay warehouse.Material Flow 5, 233245.

Bartholdi, J.J., Gue, K.R., 2000. Reducing labor costs in an LTLcrossdocking terminal. Operations Research 48 (6), 823832.

Bartholdi, J.J., Platzman, L.K., 1986. Retrieval strategies for acarousel conveyor. IIE Transactions 18 (2), 166173.

Bartholdi, J.J., Platzman, L.K., 1988. Design of efficient bin-numbering schemes for warehouses. Material Flow 4, 247 254.

Bengu, G., 1995. An optimal storage assignment for automatedrotating carousels. IIE Transactions 27, 105107.

Bozer, Y.A., 1985. Optimizing throughput performance indesigning order picking systems. PhD thesis, Department of



18/21


19/21

Gu, J.X., Goetschalckx, M., McGinnis, L.F., 2005. Warehousedesign and performance evaluation: A comprehensive review.Working Paper, Virtual Factory Laboratory, Georgia Insti-tute of Technology.

Gudehus, T., 1973. Principles of order picking: Operations indistribution and warehousing systems. W. Essen, Germany (inGerman).

Gue, K.R., 1999. The effects of trailer scheduling on thelayout of freight terminals. Transportation Science 33 (4),419428.

Guenov, M., Raeside, R., 1992. Zone shapes in class basedstorage and multicommand order picking when storage/retrieval machines are used. European Journal of OperationalResearch 58, 3747.

Hackman, S.T., Rosenblatt, M.J., 1990. Allocating items to anautomated storage and retrieval system. IIE Transactions 22(1), 714.

Hall, R.W., 1993. Distance approximation for routing manualpickers in a warehouse. IIE Transactions 25 (4), 7687.

Han, M.H., McGinnis, L.F., 1986. Carousel application for

work-in-process: Modelling and analysis. Technical Report,Material Handling Research Center, Georgia Institute of Technology, Atlanta, Georgia.

Han, M.H., McGinnis, L.F., Shieh, J.S., White, J.A., 1987. Onsequencing retrievals in an automated storage/retrieval sys-tem. IIE Transactions 19 (1), 5666.

Han, M.H., McGinnis, L.F., White, J.A., 1988. Analysis of rotary rack operation. Material Flow 4, 283293.

Hariga, M.A., Jackson, P.L., 1996. The warehouse schedulingproblem: Formulation and algorithms. IIE Transactions 28,115127.

Harmatuck, D.J., 1976. A comparison of two approaches tostock location. The Logistics and Transportation Review 12(4), 282284.

Hausman, W.H., Schwarz, L.B., Graves, S.C., 1976. Optimalstorage assignment in automatic warehousing systems. Man-agement Science 22 (6), 629638.

Heskett, J.L., 1963. Cube-per-order index a key to warehousestock location. Transportation and Distribution Management3, 2731.

Heskett, J.L., 1964. Putting the Cube-Per-Order index to work inwarehouse layout. Transportation and Distribution Manage-ment 4, 2330.

Hodgson, T.J., Lowe, T.J., 1982. Production lot sizing withmaterial-handling cost considerations. IIE Transactions 14(1), 4451.

Hwang, H., Lee, M.-K., 1988. Order batching algorithmsfor a man-on-board automated storage and retrievalsystem. Engineering Costs and Production Economics 13,285294.

Hwang, H., Lim, J.M., 1993. Deriving an optimal dwell point of the storage/retrieval machine in an automated storage/retrieval system. International Journal of ProductionResearch 31 (11), 25912602.

Hwang, H., Song, J.Y., 1993. Sequencing picking operations andtravel time models for man-on-board storage and retrievalwarehousing system. International Journal of ProductionEconomics 29, 7588.

Hwang, H., Baek, W., Lee, M.-K., 1988. Clustering algorithmsfor order picking in an automated storage and retrievalsystem. International Journal of Production Research 26 (2),

189201.

Hwang, H., Yong, H.O., Cha, C.N., 2003. A stock location rulefor a low level picker-to-part system. Engineering Optimiza-tion 35 (3), 285295.

Jaikumar, R., Solomon, M.M., 1990. Dynamic operationalpolicies in an automated warehouse. IIE Transactions 22(4), 370376.

Jane, C.C., 2000. Storage location assignment in a distributioncenter. International Journal of Physical Distribution andLogistics Management 30 (1), 5571.

Jarvis, J.M., McDowell, E.D., 1991. Optimal product layout inan order picking warehouse. IIE Transactions 23 (1), 93 102.

Jewkes, E., Lee, C., Vickson, R., 2004. Production location,allocation and server home base location for an order pickingline with multiple servers. Computers and OperationsResearch 31, 623636.

Johnson, M.E., 1998. The impact of sorting strategies onautomated sortation system performance. IIE Transactions30, 6777.

Kallina, C., Lynn, J., 1976. Application of the cube-per-order

index rule for stock location in a distribution warehouse.Interfaces 7 (1), 3746.Keserla, A., Peters, B.A., 1994. Analysis of dual-shuttle auto-

mated storage/retrieval systems. Journal of ManufacturingSystems 13 (6), 424434.

Kim, B.-I., Heragu, S.S., Graves, R.J., Onge, A.S., 2005.Clustering-based order-picking sequence algorithm for anautomated warehouse. International Journal of ProductionResearch 41 (15), 34453460.

Kouvelis, P., Papanicolaou, V., 1995. Expected travel time andoptimal boundary formulas for a two-class-based automatedstorage/retrieval system. International Journal of ProductionResearch 33 (10), 28892905.

Lai, K.K., Xue, J., Zhang, G., 2002. Layout design for a paperreel warehouse: A two-stage heuristic approach. InternationalJournal of Production Economics 75 (3), 231243.

Lee, M.-K., 1992. A storage assignment policy in a man-on-board automated storage/retrieval system. InternationalJournal of Production Research 30 (10), 22812292.

Lee, M.-K., Kim, S.-Y., 1995. Scheduling of storage/retrievalorders under a just-in-time environment. International Jour-nal of Production Research 33 (12), 33313348.

Lee, H.S., Schaefer, S.K., 1996. Retrieval sequencing for unit-load automated storage and retrieval systems with multipleopenings. International Journal of Production Research 34(10), 29432962.

Lee, H.S., Schaefer, S.K., 1997. Sequencing methods for auto-mated storage and retrieval systems with dedicated storage.Computers and Industrial Engineering 32 (2), 351362.

Linn, R.J., Wysk, R.A., 1987. An analysis of control strategiesfor an automated storage/retrieval system. INFOR 25 (1), 66 83.

Linn, R.J., Xie, X.D., 1993. A simulation analysis of sequencingrules for ASRS in a pull-based assembly facility. InternationalJournal of Production Research 31 (10), 23552367.

Liu, C.H., Lu, L.Y., 1999. The procedure of determining theorder picking strategies in distribution center. InternationalJournal of Production Economics 6061, 301307.

Mahajan, S., Rao, B.V., Peters, B.A., 1998. A retrieval sequenc-ing heuristic for miniload end-of-aisle automated storage/retrieval systems. International Journal of Production

Research 36 (6), 17151731.



20/21

Makris, P.A., Giakoumakis, I.G., 2003. k -Interchange heuris-tic as an optimization procedure for material handlingapplications. Applied Mathematical Modelling 27 (5), 345 358.

Mallette, A.J., Francis, R.L., 1972. A generalized assignmentapproach to optimal facility layout. AIIE Transactions 4 (2),144147.

Malmborg, C.J., 1995. Optimization of cube-per-order indexwarehouse layouts with zoning constraints. InternationalJournal of Production Research 33 (2), 465482.

Malmborg, C.J., Deutsch, S.J., 1988. A stock location model fordual address order picking systems. IIE Transactions 20 (1),4452.

Malmborg, C.J., Krishnakumar, B., 1987. On the optimality of the cube per order index for conventional warehouses withdual command cycles. Material Flow 4, 169175.

Malmborg, C.J., Krishnakumar, B., 1989. Optimal storageassignment policies for multiaddress warehousing systems.IEEE Transactions on Systems, Man, and Cybernetics 19 (1),197204.

Malmborg, C.J., Krishnakumar, B., 1990. A revised proof of optimality for the cube-per-order index rule for stored itemlocation. Applied Mathematical Modelling 14 (2), 8795.

Malmborg, C.J., Balachandran, S., Kyle, D.M., 1986. A modelbased evaluation of a commonly used rule of thumb forwarehouse layout. Applied Mathematical Modelling 10 (2),133138.

Malmborg, C.J., Krishnakumar, B., Simons, G.R., 1988. Amathematical overview of warehousing systems with single/dual order-picking cycles. Applied Mathematical Modelling12 (1), 28.

Meller, R.D., 1997. Optimal order-to-lane assignments in anorder accumulation/sortation system. IIE Transactions 29,293301.

Montulet, P., Langevin, A., Riopel, D., 1998. Minimizing thepeak load: An alternate objective for dedicated storagepolicies. International Journal of Production Research 36(5), 13691385.

Muralidharan, B., Linn, R.J., Pandit, R., 1995. Shuffling heuris-tics for the storage location assignment in an AS/RS.International Journal of Production Research 33 (6), 1661 1672.

Pan, C.-H., Liu, S.-Y., 1995. A comparative study of orderbatching algorithms. Omega International Journal of Man-agement Science 23 (6), 691700.

Peters, B.A., Smith, J.S., Hale, T.S., 1996. Closed form modelsfor determining the optimal dwell point location in automatedstorage and retrieval systems. International Journal of Pro-duction Research 34 (6), 17571771.

Petersen, C.G., 1997. An evaluation of order picking routingpolicies. International Journal of Operations and Manage-ment Science 17 (11), 10981111.

Petersen, C.G., 1999. The impact of routing and storage policieson warehouse efficiency. International Journal of Operationsand Production Management 19 (10), 10531064.

Petersen, C.G., 2002. Considerations in order picking zoneconguration. International Journal of Operations and Pro-duction Management 22 (7), 793805.

Petersen, C.G., Aase, G., 2004. A comparison of picking, storage,and routing policies in manual order picking. InternationalJournal of Production Economics 92 (1), 1119.

Petersen, C.G., Schmenner, R.W., 1999. An evaluation of routingand volme-based storage policies in an order picking opera-tion. Decision Science 30 (2), 481501.

Ratliff, H.D., Rosenthal, A.S., 1983. Order-picking in a rectan-gular warehouse: A solvable case of the traveling salesmanproblem. Operations Research 31 (3), 507521.

Roll, Y., Rosenblatt, M.J., 1987. Shifting in warehouses. Mate-rial Flow 4, 147157.

Roodbergen, K.J., de Koster, R., 2001a. Routing methods forwarehouses with multiple cross aisles. International Journalof Production Research 39 (9), 18651883.

Roodbergen, K.J., de Koster, R., 2001b. Routing order pickers ina warehouse with a middle aisle. European Journal of Operational Research 133, 3243.

Rosenblatt, M.J., Eynan, A., 1989. Deriving the optimal bound-aries for class-based automatic storage/retrieval systems.Management Science 35 (12), 15191524.

Rosenwein, M.B., 1994. An application of cluster analysis to theproblem of locating items within a warehouse. IIE Transac-tions 26 (1), 101103.

Rosenwein, M.B., 1996. A Comparison of Heuristics for theProblem of Batching Orders for Warehouse Selection. Inter-national Journal of Production Research 34 (3), 657664.

Rowenhorst, B., Reuter, B., Stockrahm, V., van Houtum, G.J.,Mantel, R.J., Zijm, W.H.M., 2000. Warehouse design andcontrol: Framework and literature review. European Journalof Operational Research 122, 515533.

Ruben, R.A., Jacobs, F.R., 1999. Batch Construction Heuristicsand Storage Assignment Strategies for Walk/Ride and PickSystems. Management Science 45 (4), 575596.

Sadiq, M., Landers, T.L., Taylor, G.D., 1996. An assignmentalgorithm for dynamic picking systems. IIE Transactions 28,607616.

Sarker, B.R., Sabapathy, A., Lal, A.M., Han, M., 1991. Theperformance evaluation of a double shuttle automatedstorage retrieval system. Production Planning & Control 2(3), 207213.

Sarker, B.R., Mann, L., Santos, J.D., 1994. Evaluation of a class-based storage scheduling technique applied to dual-shuttleautomated storage and retrieval systems. Production Planning& Control 5 (5), 442449.

Seidmann, A., 1988. Intelligent control schemes for automatedstorage and retrieval systems. International Journal of Pro-duction Research 26 (5), 931952.

Tsui, L.Y., Chang, C.H., 1990. A microcomputer baseddecision support tool for assigning dock doors in freightyards. Computers and Industrial Engineering 19 (14), 309 312.

Tsui, L.Y., Chang, C.H., 1992. An optimal solution to a dockdoor assignment problem. Computers and Industrial Engi-neering 23 (14), 283286.

van den Berg, J.P., 1996. Multiple order pick sequencing in acarousel system: A solvable case of the rural postmanproblem. Journal of Operational Research Society 47, 1504 1515.

van den Berg, J.P., 2002. Analytic expressions for the optimaldwell point in an automated storage/retrieval system. Inter-national Journal of Production Economics 76 (1), 1325.

van den Berg, J.P., Gademann, A.J.R.M.N., 1999. Optimalrouting in an automated storage/retrieval system with dedi-cated storage. IIE Transactions 31, 407415.



21/21

van den Berg, J.P., Gademann, A.J.R.M.N., 2000. Simulationstudy of an automated storage/retrieval system. InternationalJournal of Production Research 38 (6), 13391356.

van den Berg, J.P., Sharp, G.P., Gademann, A.J.R.M.N., Pochet,Y., 1998. Forward-reserve allocation in a warehouse withunit-load replenishments. European Journal of OperationalResearch 111, 98113.

van Oudheusden, D.L., Zhu, W., 1992. Storage layout of AS/RSracks based on recurrent orders. European Journal of Operational Research 58, 4856.

Vaughan, T.S., Petersen, C.G., 1999. The effect of warehousecross aisle on order picking efficiency. International Journalof Production Research 37 (4), 881897.

Vickson, R.G., 1996. Optimal storage locations in a carouselstorage and retrieval system. Location Science 4 (4), 237245.

Vickson, R.G., Lu, X., 1998. Optimal product and serverlocations in one dimensional storage racks. European Journalof Operational Research 105 (1), 1828.

Wen, U.-P., Chang, D.-T., 1988. Picking rules for a carouselconveyor in an automated warehouse. Omega InternationalJournal of Management Science 16 (2), 145151.

Wilson, H.G., 1977. Order quantity, product popularity, and thelocation of stock in a warehouse. AIIE Transactions 9 (3),230237.

Won, J., Olafsson, S., 2005. Joint order batching and orderpicking in warehouse operations. International Journal of Production Research 43 (7), 14271442.

Yoon, C.S., Sharp, G.P., 1996. A structured procedure foranalysis and design of order pick systems. IIE Transactions28, 379389.

Zhang, G., Xue, J., Lai, K.K., 2000. A genetic algorithm basedheuristic for adjacent paper-reel layout problem. Interna-tional Journal of Production Research 38 (14), 33433356.

Zhang, G., Xue, J., Lai, K.K., 2002. A class of genetic algorithmsfor multiple-level warehouse layout problems. InternationalJournal of Production Research 40 (3), 731744.


Documents

Warehouses Components