A product-driven system approach for multilevel decisions in manufacturing planning and control

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A Product-Driven System Approach for Multi-Level Decisions

in Manufacturing Planning and Control

Journal: Production & Manufacturing Research

Manuscript ID: Draft

Manuscript Type: Original Paper

Keywords: Intelligent manufacturing systems, Manufacturing systems, Production planning and control

URL: http://mc.manuscriptcentral.com/tmpr

Production & Manufacturing Research

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A Product-Driven System Approach for Multi-Level Decisions in

Manufacturing Planning and Control

Manufacturing planning and control systems (MPCS) incorporate processes that

consider several levels of product aggregation and different time horizons for

decision making. The decisions rendered on each level do not always have

similar objectives. In the context of intelligent manufacturing systems (IMS), the

coordination of decisions on different levels is a fundamental problem. Extensive

research on IMS, specifically regarding coordination among decision levels in

product-driven control systems (PDCS), is non-existent. Therefore, simulations

of the planning and control processes are proposed to analyze the coordination of

multi-level objectives. The proposed implementation simulates the coordination

between tactical and operational levels. At the tactical level, production plans are

obtained through a system based on advanced planning and scheduling (APS). At

the operational level, a decentralized system, which is based on distributed

decision rules, is implemented. The simulation considers decentralized decisions

that are managed by production lots, which are modeled as holons, and based on

an industrial study case. The results indicate that coordination is feasible and

highlight the importance of the reactivity caused by the distributed decisions

made by the active lots. The proposed simulation schema can also be used to

compare conventional and holonic collaborative approaches

Keywords: intelligent manufacturing systems; manufacturing systems;

production planning and control.

Subject classification codes: include these here if the journal requires them

1. Introduction

Currently, holonic manufacturing systems (HMS) are a feasible alternative for

improving the flexibility and adaptability of manufacturing. These characteristics are

fundamental because of the complexity and dynamism of the current systems

(Valckenaers et al., 2007). In a holonic system, entities (machines, robots, AGVs or

workers) are modeled as holons, which consist of a physical component and an

information processing component.

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Holons can be individual entities or can be composed of other holons. A set of

organized holons is named a holarchy. The objective is to achieve collaborative

behavior among these holons, in real or simulated systems, which enables them to make

decisions that reflect their environment.

A considerable amount of research has been devoted to the application of this concept

for scheduling decisions and manufacturing execution systems (MES). However, fewer

studies have focused on the relationship between the operation and execution levels and

the upper levels of decision making or enterprise resources planning (ERP) systems.

For example, InteRRaP (Fischer, 1999) defines an agent as a set of functional layers

linked by a control structure, which is based on communication. This architecture is

based on agents using the social model BDI (Belief, Desires and Intentions)

(Wooldridge, 2000). InteRRaP was proposed for flexible manufacturing systems

(FMS). Functional layers correspond to the three basic activities that agents must

perform in a FMS. Activities include coordination, problem solving and implementation

of local plans. This architecture considers planning as the highest level in

the decision tree. Decisions at this level are implemented offline without the potential

for subsequent change.

The architecture PROSA (Product-Resource-Order-Staff Architecture) (Van Brussel et

al., 1998) is an architecture used for modeling and implementing a holonic

manufacturing system. PROSA defines three basic types of holons: orders, products and

resources. In addition, a holon staff can be defined as decision procedures or

knowledge. All holons in this holarchy are organized to conduct manufacturing

activities.

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An interesting aspect of PROSA is its interpretation of a product as an active entity in

the production process, which is a widespread practice in studies that address smart

products (Pannequin et al., 2007). Nevertheless, the majority of the applications using

PROSA as modeling framework for holonic systems concerns operational and control

levels and, more specifically, in the context of modeling and implementation of MES

systems.

ExPlanTech is an agent-based technology for planning and production control

(Pˇechouˇcek et al., 2007). This technology is based on Proplant (Marik et al., 2000),

which is an architecture that was developed as a multi-agent system for project-based

production systems. ExplanTech represents a generalization of Proplant for mass-

production companies. The system functions by using a community of autonomous

agents that represent entities or production information. A central feature of this

technology is based on the premise that no centralized decision mechanism is

utilized.

PABADIS Promise is an architecture for production control based on a pyramid with

three levels of automation (Wunsch and Bratukhin, 2007). One of its main objectives is

to avoid the centralization of decisions by positioning the decision levels closer to the

work-flow levels. Decision levels correspond to the ERP level (tactical level), MES

level (operational level) and control level. Communication between ERP and MES is

based on “web services” using ACL (agent communication language). The architecture

is based on a manufacturing-order operation decomposition that is obtained from ERP.

Although these approaches consider planning and control levels, they do not consider

the inclusion of products as central entities in the decision-making process (except

PROSA). On the contrary, PDCS (McFarlane et al., 2002;Morel et al., 2003, 2007)

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convert the role of products to active agents in the decision-making process, in which

products can be also modeled as holons.

On the other hand, decisions made at the planning level require the inclusion of

medium-term horizons to prevent “myopia”. For that, the resources required for

production (personnel, labor, raw materials, and machinery maintenance) should be

necessarily planned in advance.

Conversely, operational-level decisions are concerned with short-term horizons, which

are inherently “myopic”. Finally, operational-level decisions must respond quickly and

efficiently to disturbances (production blocking, machine breakdowns, and demand

changes). Thus, planning and control systems should be robust, flexible and reactive

with respect to short, medium, and long term decisions.

In this context, in Herrera (2011) and Herrera et al. (2012) we proposed an architecture

that models a holarchy from products and sets of products at each level. This approach

allows coordination among decision levels and their associated decision horizons, while

focusing on the main objective of a production system (its products). Similar to other

approaches in manufacturing (Tang et al., 2011), this architecture is based on the Viable

System Model. The main advantage of this modeling framework is recursion, that is,

each composition level of the holarchy organization exhibits the same structure and

organization on each of its levels. Recursion enables the replication of the same

functions at each level, with only modifications to the objectives and decision methods.

Therefore, the aim of this work is to analyze the coordination between centralized

planning and decentralized control decisions in PDCS. Decentralized decisions are

assumed to be performed by numerous holons. These holons detect disturbances in

planning and trigger local changes that affect central planning. To accomplish this task,

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we developed an agent-based simulation of decisions that is based on an industrial case

study. At the planning level, the goal is to preserve the stability of the plans. At the

operational level, the goal is to minimize makespan degradation by satisfying buffer-

stock constraints.

From the viewpoint of PDCS, the objective of this approach is to demonstrate the

advantages of a distributed decision and analyze its relationship with the objectives of

other decision levels (tactical). The results are obtained through simulation. This

approach aims to obtain quantitative comparisons and validation measures for suitable

benchmarking regarding traditional techniques for production planning and control.

This last point has been defined as a main objective of newer challenges proposed by

the HMS community (Valckenaers et al., 2006).

This paper is organized as follows: Section 2 is dedicated to all elements considered in

the simulation experiment, and Section 3 presents the simulation results. Section 4

presents an analysis of the main results, and Section 5 states the conclusion and

provides some research perspectives.

2. Materials and Methods

2.1 Centralized and distributed decisions

At different levels of MPCS, decisions are obtained by considering a rolling horizon.

Levels are associated with different levels of an aggregation of products, such as

product families, production orders, lots, and finished products or components.

A major challenge is to preserve the coherence of decisions among the levels. But, in

practice, when disturbances occur, the objectives for each level are not easily achieved,

and disturbances may cause major planning changes. Note that frequent changes can be

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the source of considerable instability. In addition, these effects often cause reduced

efficiency and poor productivity. Short-term changes are more frequent and can

significantly reduce system performance. In this context, MPCS should provide

sufficient flexibility at the operational levels and ensure consistency with the defined

objectives at the upper levels.

This approach considers two decision levels: tactical and operational levels. At the

tactical level, the decision concerns the production quantities for every item within a

product family and for each period on a planning time horizon. This problem is

generally associated with the master production schedule (MPS) and is usually modeled

using a lot-sizing model (Pochet and Wolsey, 2006). The objective is to minimize

production costs by establishing a set of parameters as marginal costs and considering

the system capacity. During the first period of this planning horizon and once each

quantity (by item) has been obtained, these quantities must be divided and sequenced to

be incorporated in the manufacturing system. This lot-streaming problem, (Sarin and

Jaiprakash, 2007) whose objectives are to reduce the total production time (Cmax), is

usually applied to manufacturing systems that contain parallel manufacturing processes.

The decision at this level is comprised of a sequence of sub-lots that correspond to the

weekly planning, which considers the production start time and the quantity of each

product to be manufactured.

One of the primary aspects of the lot-streaming problem is that it assumes constant

production rates. These rates may be affected by various disturbances, such as machine

blocking, machine breakdowns or accidents. Due to these occurrences, changes in the

parameters of the model may affect planning efficiency. These changes reduce

production capacity and increase the gap between planning and the

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launched production. This gap is named “system nervousness”.

Our aim is to study the relationships among decision levels in the context of PDS. Then,

products or sub-lots are modeled as holons with the capacity to modify their

environment. Holons are assumed capable of making a single distributed decision. More

specifically, a holon can decide to stop its production at a certain stage and heuristically

reassign the remaining quantity. Re-assignment consists of assigning the quantity that

has not been manufactured to another lot(s) (one or many), which modifies the

planning. Until the new assignment is completed, a part of the sub-lot remains in an

intermediate stock (buffer). The feasibility of this “decision of splitting” is dependent on

the remaining production and stock capacities, and is also dependent on the existence of

similar types of sub-lots (same reference) that were previously planned.

Once the feasibility of divisions has been determined, the sub-lot holon evaluates the

variation in planning through a re-planning linear programming model. The model

seeks to replace the subdivided lots and evaluate different alternatives that will

minimize the increase of Cmax. These alternatives, which correspond to different sub-

sets of the same reference that will increase their size, are placed in the queue sub-

module.

Subsequently, we describe the industrial study case and how the sub-lot holon renders

the previously described decision.

2.2 Study Case

The company selected for the case study is a subcontractor that manufactures

turbochargers for the automotive industry. The facility produces a maximum of ten

thousand products per day with hundreds of references. The plant is divided into

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production cells, which encompass all stages of production that are required to produce

a finished product. Some production cells are dedicated to a specific customer. In this

study, we consider one of these production cells.

In a cell, the production process is divided into two stages. An initial set of operations

are performed in the first line (module A), generating semi-finished products. These

products are assembled into three independent sub-assembly modules (module B). The

production cell includes storage of raw materials, semi-finished (buffer), and finished

products.

Figure 1 About Here

2.3 Distributed decision model

Indexes

l � 1,2, … , L : lots,

i ∈ Ω� : sub-lots in lot l,

j � 1,2, … , J : sequence positions,

k � 1,2, … , K : cells at B.

VariablesVariablesVariablesVariables

C#$% : makespan,

xb()* : re-planned sub-lot quantity of item I in sequence position j assigned to

module k of stage B,

STA) : start time at stage A of sub-lot in position j,

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STB)* : start time at module k of sub-lot in position j

ParametersParametersParametersParameters

xcut : quantity to be re-planned,

q( : minimum sub-lot size of item i,

TPA( : marginal production time at A of item i,

TPB( : marginal production time at B of item i,

SA( : setup time at A of item i,

SB( : setup time at B of item i,

I � ∑ |Ω�|7(89 : number of sub-lots,

L : number of items,

K : number of sub-modules,

n� � :Q�/q�= : maximum number of sub-lots

in lot l,

Ω� � >1,2, … , n�? : set of sub-lots in lot l

xf()* : fixed sub-lot quantities.

yf()* : fixed sequence.

t@AB : new start time at A for the first sub-lot in the planning after disturbance

detection.

EPFG min C#$% E1G

∑ ∑ ∑ xb()*H*89 � xcutI

)89(JKL E2G

xb()* � 0, iϵΩ�, ∀l ∶ l Q w, ∀j, ∀k E3G

STA9 � t@AB E4G

STA) � STA)T9 U ∑ ∑ ∑ TPA� ∙ xf(E)T9G* U SA� ∙ yf(E)T9G*, ∀j: j W 1H*89

I)89(JKL EG

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STB)* X STA) U ∑ ∑ TPA� ∙ yf()*, ∀j, ∀k(JKY7�89 E5G

STB)* X STBE)T9G* U ∑ ∑ TPB� ∙ [xf(E)T9G* U xb(E)T9G*\ U SB� ∙ yf(E)T9G*, ∀j: j W(JKY7�89

1, ∀k (6)

C#$% X STBI* U ∑ ∑ TPB� ∙ [xf(I* U xb(I*\ U SB� ∙ yf(I*, ∀k(J@Y7�89 (7)

xb()* ∈ ]^, STA, STB, C#$% X 0 (8)

Objective function (1) minimizes the Cmax represented by the end date of the

last piece in the sequence. Constraint (2) ensures that the sum of the re-assignments

(xbijk) will be equal to the remaining quantity in the intermediate stock (xcut).

Constraint (3) establishes that the re-assignment can only be performed for the planned

sub-lots that belong to the same lot that was previously divided. The start time of the

sub-lot in position j is set to tnew by constraint (4). Constant tnew represents the new

start time of the first sub-lot after disturbance detection. This sub-lot corresponds to the

first sub-lot in the planned sequence (not yet in production). The recursive relationship

in constraint (5) expresses that the start time of module A for the sub-lot in the j-th

position must be equal to the start time of the previous sub-lot (sub-lot in position j − 1)

plus its setup and production time, which is determined by considering a fixed sequence

(xfijk and yfijk). Constraint (6) ensures that the start time of module B will always be

greater than the start time of module A plus the production time for module A

corresponding product). Constraint (7) considers that the production time for module B

must be increased proportionally by the re-assigned quantities. The makespan is defined

by constraint (8).

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2.4 Simulation and Parameters

Simulation considers two decision levels. The first level (tactical) is

implemented using an integer programming model that defines quantities as produced

by item and period in a rolling horizon. The details of this model are provided by

Herrera and Thomas (2009). In the first level, the quantities are divided into sub-lots

during the first period, and the sequence to be used in the manufacturing process must

be defined using an integer programming model that solves the lot-streaming problem.

The model is similar to P0 but the quantities and sequences are variable, which

increases the execution time but are performed only at the beginning of the operation

period.

Once the system has been initialized with these results, the simulation begins. A

platform is used at this step (Pannequin et al., 2009) to facilitate the discrete events

simulation and the evaluation of different criteria. During the production period (week),

variations in the production times are simulated for the different modules; disturbances

are simulated as blocking and breakdowns, for example.

To react to perturbations, P0 is solved to determine if a certain quantity of items

is placed into stock and to determine whether this decision improves the planning with

respect to the initial situation. The decision to use this distributed decision process is

dependent on the variation between the planned waiting time and the real time of a

product in the queue of module B.

The simulation is performed considering a horizon of one year, obtaining

weekly operational results, of which some results consider the distributed decision and

some results disregard the distributed decision. Stability is achieved at the tactical level

using a nervousness measure proposed in (Herrera and Thomas, 2009). This measures,

quantifies the variation in the planned quantities on a weekly basis. At the operational

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level, the obtained Cmax and work-in-process (WIP) are compared. Some of the

parameters that were utilized are listed in Table 1.

Table 1 about here

3. Results

3.1 Nervousness

Figure 2 about here

Figure 2 shows the results, considering nervousness, of both cases (centralized

and hybrid) for a comparison. The cases represent situations in which the product is

active (hybrid) and situations in which the product is inactive (centralized). The

complete experiment is discussed in Herrera (2011). The centralized case considers a

model that reduces the nervousness of the plan, thus, its shape in Figure 2 represents a

“stable plan”. These results represent the difference between the launched production

and the weekly planned production for a one-year operational horizon. The figures

cover 60 periods according to a transient period of 8 weeks.

Figure 3 shows the same results after a filter is applied (Savitzky-Golay). The

reason for applying a filter is to distinctly capture the differences between the two

decision systems. This particular filter was chosen because it preserves the haracteristics

of the initial distribution and the relative minimum and maximum, as well as the width

of the peaks. Table 2 displays the results of a statistical hypothesis test that was

employed to verify if differences exist between the series. The H0 hypothesis was

described as “significant differences exist between both cases with respect to the

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nervousness results”, and the H1 hypothesis was described as “significant differences

do not exist between both cases respect to the nervousness results”. The results reveal

no changes in stability for the plans in which the products are active.

3.2 Cmax

Figure 3 about here

Table 2 about here

Figure 4 about here

Figure 4 about here

Table 3 about here

Figure 4 shows the results, considering Cmax, of both cases (centralized and hybrid) for

a comparison. Figure 5 shows products can affect the planning process and considerably

improve the production completion time.

3.1 WIP

This indicator corresponds to the average stock of all references. Its average is

calculated at the end of the week.

Figure 6 about here

Figure 7 about here

Table 4 about here

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Figure 6 displays the results considering the work-in-process, of both cases (centralized

and hybrid) for a comparison. Figure 7 shows the after filtering. Table 4 demonstrates

that the intermediate stock level is utilized more frequently when the products are

active.

4. Discussion

The results of Cmax are particularly interesting because they reveal a net gain

without any deterioration in stability (see Figure 5). This finding indicates that it is

possible to achieve “robustness” in the final results.

Figure 7 demonstrates that we “must pay” in stock (which is intuitively expected) the

gains in nervousness and Cmax. The Cmax efficiency is directly related to the increase

and even saturation of the intermediate stock. Thus, it is possible to conclude that the

proposed approach enables a better utilization of this resource.

5. Conclusion and future work

A simulation of different decision levels for MPCS has been presented. The objective

was to analyze the coordination of decisions at different levels using centralized and

distributed methods. Local decisions represent decisions made by a “holon lot” in the

context of PDCS.

The results demonstrate the feasibility and efficiency of coordination between central

and local decisions with different objectives using a PDS approach. The feasibility of

obtaining a stable planning in the middle-term (tactical level) is assessed and a

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significant performance in reactivity in the short term (operational level) is achieved.

The use of models and methods that are based on mathematical programming is

justified because these models enable acceptable approximations to the problem and

provide a comparison with other approaches as, for example, collaborative strategies.

This stage of our work represents a starting point for further research developments.

The usefulness of the proposed system should be validated in large-production

environments or for situations in which the consideration of centralized decisions is not

feasible at all.

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102x65mm (96 x 96 DPI)

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For Peer Review O

nly

134x29mm (96 x 96 DPI)

Page 26 of 28



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For Peer Review O

nly

146x39mm (96 x 96 DPI)

Page 27 of 28



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For Peer Review O

nly

139x43mm (96 x 96 DPI)

Page 28 of 28



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Documents

A product-driven system approach for multilevel decisions in manufacturing planning and control