A concise review of flexible manufacturing systems and FMS literature

155

Tutorial

A Concise Review of Flexible Manufacturing Systems and FMS Literature P.J. O 'Grady Department of Industrial Engineering, North Carolina State University, Raleigh, North Carolina, U.S.A.

and

U. Menon Department of Industrial Engineering California Polytechnic State University, San Luis Obispo, California 93407, U.S.A.

The widespread popularity of Flexible Manufacturing Sys- tems (FMS) has resulted in a prolific bibliography of literature describing several frameworks for analysis, planning and control of this automated mode of production. In this paper we review literature and discuss both the major characteristics of FMS and the contemporary decision support systems that have emerged to address the planning and control problems inherent in FMS.

[The bibliography provides 150 references.]

Keywords: Flexible Manufacturing Systems, Manufacturing systems, Production planning, Production control, Decision support systems, Hierarchical control, Simulation, Queuing theory, Integer programming, Heuristics, Mathematical programming, Multiple criteria decision methods, Goal programming, Au- tomation, FMS, Bibliography, CAD/CAM.

Peter O'Grady is a faculty member in the Department of Industrial En- gineering at North Carolina State University. He has also held academic appointments at University of Not- tingham and The University of Florida, Gainesville. His industrial experience includes work for Rolls- Royce Ltd. and International Com- puters Ltd. He has directed major research projects in Computer Aided Manufacturing. His qualifications include a Bachelors degree with honours

from Cambridge University and a Ph.D. from the University of Nottingham. He is a Chartered Professional Engineer and is affiliated with liE, SME, I. Prod. E., BPICS, CAM-I, SERC/ACME, ESPRIT and BIM.

North-Holland Computers in Industry 7 (1986) 155-167

1. The Characteristics of FMS

The sustained interest in the concept and application of Flexible Manufacturing Systems (FMS) has generated an abundance of contemporary literature, usually with a descriptive emphasis from industrial authors and an analytical treatment from academic writers.

The ASP (Automated Small-Batch Production) study published by the National Engineering Laboratory [98] presents the early technological initiative in Britain, promoting the case for the form of automated manufacture that is now termed FMS. This initiative was followed by significant governmental funding to encourage large scale investment in FMS by British companies, notable examples of which are the current generation of FMS facilities at Anderson Strathclyde plc., Gardner & Sons, Normalair-Garett Ltd. and S C A M P / 6 0 0 G r o u p plc.

I n t r o d u c t o r y de sc r i p t i ons o f F M S are p r o v i d e d

by K u e m m e l [83], Ingerso l l Eng inee r s [64], D r a p e r

L a b o r a t o r i e s [24], R a n k y [111] a n d H a r t l e y [51].

T h e desc r ip t i ons i nc lude c o v e r a g e o f phys i ca l c o n -

f igura t ion , a u t o m a t e d ma te r i a l hand l ing , p r o d u c t

r a n g e capab i l i t y a n d the con t ro l sys tems d e p l o y e d

Unny Menon is a Professor of In- dustrial Engineering at California Polytechnic State University, San Luis Obispo. He has also held academic appointments at Sheffield Polytechnic, England, RIT at Rochester N.Y. and The University of Nottingham, En- gland. His industrial experience has included work for British Steel Corp. at Sheffield, Bechtel Power Inc. at San Francisco and IBM at San Jose. His undergraduate studies in Production Engineering were completed at Shef-

field Polytechnic. His advanced degrees include an M. Phil. from Sheffield Polytechnic and a Ph.D. from The University of Nottingham. He is a Chartered Professional Engineer and is affiliated with liE, I. Mech. E., IITRI and CAM-I.

0166-3615/86/$3.50 © 1986 Elsevier Science Publishers B.V. (North-Holland)

156 Tutorial ('omput¢'rs in hnlu,gtr~

in a variety of FMS installations currently operational or being commissioned.

The Ingersoll Engineers report suggests that a precise definition of FMS has not yet been formulated and they offer the following interim definitions:

"A process under control to produce varieties of components or products within its stated capability and to a predetermined schedule".

"A technology which will help achieve leaner factories with better response times, lower unit costs and higher quality under an improved level of management" .

Both of their interim definitions perhaps fail to capture the essential system elements comprising FMS and the distinguishing operational characteristic that is normally associated with this mode of manufacture.

The definition stated in the Draper Labs FMS handbook concurs with the contemporary perception of FMS, i.e.

"'a computer-controlled configuration of semi-indepen- dent work stations and a material handling system designed to efficiently manufacture more than one part number at low to medium volumes".

The FMS handbook was compiled by the Charles Stark Draper Laboratories as part of a major research contract for the United States Army/Tank Automotive Command (TACOM). This comprehensive treatise on FMS consists of five volumes; only Vols. I-IV are universally available, Vol. V documents computer software for FMS and is on restricted distribution. The pyramidal presentation structure in the handbook commences with an overview of FMS in Vol. I, followed by detailed descriptions of system technology in Vol. II, which includes detailed case studies of the following FMS installations at American companies: • Kearney & Trecker FMS at AVCO-Lycoming,

Stratford, Connecticut, installed in 1979 for machining ten types of stainless steel turbine- engine components for the M-1 tank.

• White Sundstrand FMS at Detroit Diesel Ali- son, Indianapolis, Indiana, installed in 1983 for machining a product range of 40 large transmis- sion covers and housings.

• Giddings & Lewis FMS at Caterpillar Tractor, Aurora, Illinois, installed in 1980 for machining a range of 8 large steel weldments (4 × 4 × 12 feet part envelope). Volume III of the manual provides explicit

guidelines on writing engineering specifications

for FMS and the implementation logistics required for this sophisticated technology. Volume IV is a large appendix with examples of RFP (Request for Proposals from vendors), a sample FMS proposal from a vendor, a glossary of FMS terms and various technical details pertinent to FMS.

The comprehensive nature of the Draper Labs handbook makes it a valuable source of reference for everyone associated with the analysis or implementation of Flexible Manufacturing Systems.

Whereas, the Draper Labs presentation con- centrates on American applications of FMS, an international review is provided by Dupont- Gatelmand [25] and also by Bilalis [10]; Chapter IV with details of FMS hardware and configuration, covering installations in Europe, Japan and North America. International comparisons of FMS installations and classifications with respect to product attributes can be found in editorial surveys published by American Machinist [4] and Tech- nocrat [133].

Warnecke & Scharf [136] in this early paper, discuss the significant criteria that should be considered in the development of Integrated Manu- facturing Systems. They have emphasized the need for the following: • a hierarchic framework • product range flexibility with adaptive mach-

ines • system integration using automated workpiece

handling and tool changing • enlargability of the system • compatibility with other systems.

Their conceptual framework incorporates weighted multiple objectives to compute the system performance index of alternative machine configurations. Industrial studies emphasising the implementational aspects of Integrated Manufac- turing Systems can be found in the doctoral theses of Kruse [82] and Farkas [33].

Warnecke & Vettin [137] have formulated a classification scheme to categorise FMS celt configuration with respect to the modes of material handling deployed and the optional inclusion of ASRS (Automated Storage and Retrieval System) as the flexible buffer to cope with variations of work-in-process. Stute et al. [128] have described a modelling framework for the determination of the optimal physical layout for FMS. Nys et al. [102] present a mathematical framework for the selection of the optimum equipment configuration for

Computers in Industry P.J. O'Grady, U. Menon / Concise Review of FMS 157

FMS. This Russian paper includes an example of its application to a system for the machining of housings. Tipnis [134] has formulated a systematic methodology for evaluating system viability with respect to the parameters and process variables for FMS based on micro-economic modelling concepts.

The principal characteristics and limiting constraints of FMS which may have to be taken into account in the planning and control process for such systems are as follows: • The span of product range flexibility that is

obtainable from the current generation of FMS is generally quite narrow. The Ingersoll En- gineers survey [64] found that

"In most cases the systems were relatively inflex- ible ....

Compatible part numbers were restricted to an aver- age of eight .....

The proportion of all components in the plant that pass through the system was approximately 4% ....

Inadequate provision of tooling at spindle further restricted flexibility".

• The use of automated tool magazines is a typi- cal feature of currently installed FMS. The technical viability of a "virtual tool provision- ing architecture" can be sustained; such features being anticipated in the next generation of FMS, subject to economic viability of the sophisticated material handling infrastructure that will be necessary.

• The number of machines per FMS configuration in the installations reviewed by Ingersoll and by Technocrat, were within the range 2-13 work stations per FMS, with a significant proportion in the lower half of that band. Flexibility is emphasized as an important oper-

ational characteristic in this particular form of automated manufacture. A comprehensive discussion of flexibility as a fundamental attribute in a variety of contexts can be found in the doctoral dissertation by Mandelbaum [87]. Slack [117] has examined the significant dimensions of flexibility from an operations management viewpoint with reference to FMS parameters.

Browne et al, [13] have proposed some criteria which provide a structured basis for considering the nature of flexibility that is attainable in the context of automated manufacturing systems. They have also provided a framework for categorising the modes of flexibility that are currently operational. Zelenovic [148] considers the productivity

factors which reinforce the pursuit of flexibility, as an essential condition for effective productive systems. His discourse includes a quantitative definition of flexibility and a structured approach for the design of flexible production systems, based on Yugoslav experience. Gustavsson [45] discusses methods for calculating flexibility levels at a macro level applied to Swedish Industry.

Although flexibility is a generally preferred attribute the extent of "attainable flexibility" is restrained by economic considerations, resulting in limited flexibility as the "viable norm" in practice. The term "bounded product range versatility" could be used to describe the mode of flexibility that is normally available in contemporary FMS systems.

The determination of the viable product range for a given FMS configuration has been considered as a problem for analysis by some researchers. Vaithianathan [135] and Kusiak [84] describe numerical clustering techniques which en- able the identification of " the plausible product range versatility" for a given system. Certain aspects of this form of analysis are similar to methods devised for family formation in Group Technology. Askew et al. [6] describe a computer algorithmic approach for identifying the set of key components eligible for an automated manufacturing system, which they have applied in industrial studies at Simon Containers and at West- land Helicopters.

2. Planning and Control Systems

The literature review of planning and control systems for automated manufacture indicates much diversity in the conceptual representation and modes of analysis deployed by contemporary researchers. Although diversity of research methodology is evident, it is also found that there is universal acceptance of the observation that "this is an immensely complex planning problem". Ex- plicit declaration of this complexity can be found in Barash et al. [7], Buzacott & Shanthikumar [15], Gershwin et al. [42] and Stecke [126]. Some specific statements from these well known researchers are repeated verbatim here, to convey the intensity of concern that has been expressed, arising from the combinatorial characteristic of the solution space that describes the planning and control problem for FMS.

158 Tutorial ('omputers in Industry

"I t is our opinion based on fours years of research that these problems are extraordinarily complicated and require major advances in systems engineering, operations research, decision analysis and control sciences . . . . (Gershwin et al. [42], at the Massachusetts Institute of Technology).

"The scheduling problem appears to be unsolvable in anything like a rigorous manner . . . . this is a provably difficult combinatorial problem .. . . The consensus of expert opinion is that it will eventually be shown that no practical algorithm can ever be developed for these problems" (Barash et al. [7], at Purdue University).

The nature of this difficulty in its global context can be found in King [78], where a scheme is presented to classify the computational complexity of scheduling problems. This scheme is derived from the mathematical theory that has emerged to categorise the solvability or otherwise of combina- torially complex problems (e.g. the term "NP- complete"; indicating a class of notoriously difficult scheduling problems).

In the ensuing sub-sections the noteworthy literature on planning and control is reviewed within the following categories: • the hierarchic framework • simulation models • queueing theory models • integer programming models • heuristic algorithms • other (unclassified) algorithms and models.

2.1. The Hierarchic Framework

The operations planning and control aspects of FMS have been considered by a number of authors. Warnecke & Scharf [136] and Eversheim & Westkamper [30] were among the earliest to recognise the need for a hierarchical structure for managing automated manufacturing systems, based on their research in West Germany. Subse- quently, Buzacott & Shanthikumar [15] in their paper (cited by many others) on the analysis of FMS, indicate that the complexity of the overall planning and control problem necessitates the need for systems analysis at the following three levels:

i. Pre-release planning concerned with the selection of suitable orders for production over a medium term time interval compatible with overall system resoures.

ii. Order release or input control to determine the sequence and timing of the release of jobs to the system.

iii. Operational control of the movement of parts

between machines, route management and dealing with disruptions.

The level (i) pre-release planning problem is addressed in O'Grady & Menon [107].

In addition to their proposal for a hierarchic planning framework Buzacott & Shanthikumar ac- knowledge some major difficulties involved, expressed as follows:

"Because of the inherent complexity of operational control it is often considered advantageous to transfer certain decisions from the operational control level to the pre-release level. For example if the operational sequence constraints permit alternative routings, some specific routing may be chosen at the pre-release level in order to remove this decision from operational control.

Ashby's principle of requisite variety states that the greater the control options available, i.e., the more the variety available to the controller, the less will be the effect of disturbances on system performance. Thus, insofar as it is technically and economically possible, advantage should be taken of the flexibility inherent in the FMS. Unfor- tunately, the more complex the information structure the more complex is the model required to describe the system and the less likely that simple solutions can be found." . . . . (Buzacott & Shanthikumar [15]).

The hierarchic approach to the overall planning and control problem is also supported by other researchers, notably Akella et al. [3], Bell & Bilalis [9], Canuto et al. [17], Chen-Chuan et al. [22], Eversheim & Fromm [31], Furlani et al. [39], Fox [34], Harrison & O'Grady [50], Kimenia [75], Mohanty & Krishnawswamy [94], Murotsu et al. [96], Stecke [126,127] and Suri & Whitney [131]. Nilsson [99] describes a hierarchical framework which includes CAD/CAM.

It is noted that the fundamental basis of this hierarchical approach to production planning can be traced back to the widely recognised contribu- tion by Hax & Meal [52], although they were not considering FMS per se and their span of coverage included multi-plant corporate planning decisions. In the FMS context, whilst there has been a recognition of the need for a hierarchic approach, much of the published analysis has been focussed on the problems at the short-term operational control level.

2.2. Simulation Models

Simulation based approaches to examine system behaviour have been popular in contemporary FMS research. Among the earliest to use simulation models in the analysis of FMS configurations were Weck & Schuring [139], Mayer & Talavage


[88], Chan & Rathmill [19], Hutchinson [56] and Stecke & Solberg [125]; based on earlier thesis completed in 1977.

Simulation approaches continue to be popular and many suppliers of FMS hardware now offer such feasibility analysis as an element of the system design and implementation process for their clients, as described by Fox [34] representing Cincinnati-Milacron (a U.S. vendor of turnkey FMS) and the use of the 'See Why' simulation system by KTM plc (a U.K. supplier of turnkey FMS) as reported by Kochan [80].

In addition to the simulation based approaches cited already a representative cross section of other FMS simulation models largely at the operational planning and control level can be found in Bell & Bilalis [9], Bilalis [10], Bevans [11], Browne & Rathmill [12], Carrie et al. [18], Elmaraghy [29], Iwata et al. [68], Mills [93], Musselman [97], Rathmill &Chan [112], Stanek & Rudolph [123], Spur et al. [121] and Hutchinson [57].

In general the purpose of the type of simulation modelling described by all the authors cited in this survey has been one of the following: • to establish the viability of a given FMS config-

uration of machines and transport devices • to assist the system design process with respect

to hardware choices • test operational planning and control strategies. The factors generally considered in such studies are: • the effects of machine and system reliability

with respect to breakdown/repair parameters (MTBF/MTTR) and throughput performance

• the comparison of alternative loading strategies based on priority sequencing rules

• the problems arising from different aspects of system flexibility

• comparison of alternative configurations • potential congestion of the transport system.

2.3. Queueing Theory Models

The modelling of FMS as a closed network of queues has been a popular framework for analysis adopted by a number of authors. Solberg [118] was amongst the first to formulate a queueing network model of FMS type facilities, drawing on concepts provided by Gordon & NeweU [44] and computational algorithms proposed by Buzen [16]. Much of the initial research conducted at Purdue

University by Solberg on automated manufacturing systems, was completed as part of the substan- tial research grant from NSF (National Science Foundation). The normal practice of disseminat- ing the results (or the CAN-Q software in this case) of NSF sponsored non-confidential research to benefit fellow researchers in the field appears to have been granted generously by Solberg. This has facilitated the widespread usage of this queueing theory formulation based on the CAN-Q computer program by several researchers world-wide. Buzacott and Shanthikumar [15] use similar concepts in their queueing models of simple FMS configurations, to compare system performance using alternative in-process storage policies. In addition to the general conclusions on production capacity, they suggest that diversity of job routes and flexibility of job sequence are desired attributes of products being processed by FMS. Secco-Suardo [114] has also deployed a "closed queueing network model" of FMS. In addition, he used non-linear programming to determine the optimal workload distribution such that production rate is maximised. Other users of queueing theory models include Actis-Dato et al. [1], Hildebrandt [53], Kimenia & Gershwin [73], Kay & Walmsley [70], Kay & Rathmill et al. [71], Suri [129] and Yao [146].

The attractiveness of the queueing theory approach supported by CAN-Q computations is that approximate indications of the adequacy of particular systems can be obtained readily and such approximations may be sufficient as a preliminary solution. The weakness of the approach is acknowledged by most of its proponents; viz. the necessary assumptions made in the queueing theory derivations are unrealistic for most FMS installations (see critiques by Wilhelm & Sarin [140] and Suri & Cao [1301).

In particular, consider the fundamental as- sumption that the processing time at each machine is stochastic (Exponential distribution) and that jobs arrive according to a Poisson distribution; it is known however, that the process times on the numerically controlled machines typically found in FMS are deterministic. This constant machine cycle time per process step is generally estimated by the NC-postprocessor and confirmed during cutting trials. Transport time between work stations could be stochastic, but it is typically of a much shorter duration than machine cycle time

160 Tutorial Uornputer,s m Industry

and hence likely to be insignificant in the context of system performance.

The review of the cited work deploying a closed network queueing theory approach indicates that while the approach is impressive in its mathematical treatment and is useful in providing approximate guidelines at the preliminary system design stage, it is generally of limited value in the operational planning and control function.

2.4. Integer Programming Models

Stecke [126 and 127] constructed a non-linear integer programming model to solve certain production planning problems for the FMS at Cater- pillar Tractor, Peoria, Illinois. She identified five planning problems and applied her model to obtain solutions for problems (ii) and (o) only from the following list:

i. Part type selection problem: From a set of part types that have production requirements, determine a subset for immediate and simultaneous processing.

ii. Machine grouping problem: Partition the machines into machine groups in such a way that each machine in a particular group is able to perform the same set of operations.

iii. Production ratio problem: Determine the relative ratios at which the part types selected in problem (i) will be produced.

iv. Resource allocation problem: Allocate the limited number of pallets and fixtures of each fixture type among the selected part types.

v. Loading problem: Allocate the operation and required tools of the selected part types among the machine groups subject to technological and capacity constraints of the FMS.

Stecke was able to obtain solutions (for problems (ii) and (v)) with respect to the requirements for the FMS at Caterpillar Tractor, using a standard mixed integer programming system. She points out in the conclusions to her doctoral work that "(such models) although solvable, they are in general, too large to be a computationally feasible procedure. A good heuristic should be used instead".

One aspect of the Stecke model which is of interest is that she has considered certain limitations which arise in relation to the allocation of tools to the slots at each tool magazine. She formulated a tooling constraint to account for the

number of slots occupied by tools of different size/orientation and the static weight balancing of the tools distributed within different sectors of the circular tool magazine.

It is observed that problem (i) in her list is in fact the pre-release planning problem considered earlier in the discussion of the universally popular hierarchic approach. A structured approach for the solution of this problem is presented in O'Grady & Menon [107].

Similarly, a modelling framework which could provide solutions for problem (iii) (left unsolved by Stecke) can be found in O'Grady & Menon [106]. This has been accomplished by adapting the Modern Control Theory based optimisation model for production planning devised by O'Grady [103]. This adaptation, enables the analysis of work flow in FMS with due regard to multiple system attributes in a discrete time representation of the state space.

Wilson [144] has also used an integer programming formulation of the tool magazine assignment problem identified by Bell & De'Souza at Lough- borough University of Technology. In this problem the objective is to place tools in pockets, so that the total time taken for a complete sequence of the manufacturing process is minimised, given uni-directional rotation of the tool carousel and a robot arm in use for tool transfer.

Canuto, Menga & Bruno [17] reporting on FMS research at Politechnico di Torino Italy, have described an integer constraint formulation for tool assignment within a hierarchic structure which also includes operation allocation, part allocation, routing and scheduling. However, they do not indicate any practical application of their model and the computational difficulties (if any) associated with its implementation.

2.5. Heuristic Algorithms

The complexity of the planning problem (Barash et al. [7], Gershwin et al. [42], Stecke [126]) for FMS encourages the consideration of heuristic methodologies which facilitate the determination of solutions which are feasible and acceptable vis-a-vis the pursuit for the optimum solution. The Oxford English Dictionary explana- tion of the term heuristic is: "serving to discover; (of computer problem solving) by trial and error; derived from the Greek word heurisko (find)."


The appropriateness of the heuristic approach for production planning is aptly expressed by King and Spachis [79] as follows:

"Industrial scheduling problems are very complex and, as has been demonstrated, even very simplified models can be solved optimally only with enumerative methods which are prohibitively expensive in computer time. In the con- struction of models of actual scheduling situations some inaccuracy is inevitable because of the assumptions made and errors in data collected. If this is the case then it seems fatuous to worry about extreme precision in the solution. In reality it is a feasible and not necessarily optimal solution that is required. The idea of using sub-optimal methods is not new although it has gained academic respectability only recently. The term "heuristic" is commonly used for approximate methods, meaning a fast way of getting good feasible solutions." Wild [142] offers the following description of

heuristics in his discourse on production management techniques:

"A heuristic procedure is an intuitively designed procedure capable of providing a good, but not necessarily optimum solution to a problem. Because of the complexity of many situations facing the production manager, rigorous methods providing optimum solutions are rarely available, hence heuristic procedures are of considerable importance."

The perception of heuristics declared by Taha [132] in the popular reference text on Operations Research techniques is noteworthy because it appears in the context of presentations dominated by the emphasis on optimization. This perception is stated as follows:

"Although mathematical models seek the determination of the best (optimum) solution, sometimes the mathematical formulation may be too complex to allow an exact solution. Even if the optimum solution can be attained eventually, the required computation may be impractically long. In this case, heuristics can be used to develop good (approximate) solutions."

2.5.1. The Scope for Heuristics Given this background of heuristic approaches

in general and the acknowledged complexity of the planning problem for FMS, it would appear that there is scope for the development of heuristic algorithms to provide adequate and acceptable solutions. As stated earlier, the conclusions arrived at by Stecke [126] in her doctoral work on FMS production planning based on optimization models is that "(such models) although solvable, they are in general, too large to be a computationally feasible procedure. A good heuristic should be used instead". The computational difficulty cited by Stecke [126] is well known; arising as it does in the context of an optimization approach based on Integer Programming. It is interesting that Hillier

and Lieberman [54] have cited a different and hybrid role for heuristics, to enhance the solvability of such Integer Programming problems, which they describe as follows:

"Considerable progress is now being made in developing efficient heuristic procedures for finding feasible integer solutions for these problems (Integer Programming) that are not necessarily optimal but usually will be better than can be found by simple rounding." Although there does not appear to be any pub-

lished work to date on the application of heuristics for FMS planning and control per se, there is no shortage of literature expounding its productive application in analogous areas of operations planning and control.

2.5.2. Heuristics Applied to Manufacturing Systems A concise review of the application of heuristic

algorithms for resource allocation with respect to project management and for assembly line balancing can be found in McMillan [91]. Surveys on the productive use of heuristics to solve the complex problem of job shop scheduling can be found in Gere [41] and in Panwalker & Iskander [109]. Similar reviews of heuristic algorithms devised by several contributors and applied to a broad range of production planning and control problems can be found in research readings edited by Buffa [14], Montgomery & Berry [95] and King and Spachis [79].

The use of heuristics continues to be popular as an acceptable part of contemporary research methodology as evident in recent contributions to production planning literature from Elmaleh & Eilon [28], Falster & Rolstadas [32], Iwata et al. [67], O 'Grady & Byrne [104] and O 'Grady and Harrison [105].

2.6. Other Algorithms and Models The review in this section includes a variety of

other approaches which have been applied with respect to FMS. They are not strictly amenable to categorisation and will therefore be considered individually in the ensuing discussion of the literature describing such approaches.

McCartney & Hinds [89] and Halevi & Weill [47] have formulated a planning algorithm for FMS using a control strategy which alters the parameters at the level of the machining process. This approach is based on a control philosophy which uses real-time monitoring and feedback, which the objective of minimising overall produc-

162 Tutorial ("omputer.~ m Industr~

tion costs in an interactive decision making environment. A similar philosophy of system control is proposed by Seidman & Schweitzer [115] who use the optimal steady state matrix from a Markovian analysis of the options available for machine loading, adaptive route selection, sequencing parts and altering machining parameters. They avoid the computational difficulties related to the manipulation of "large state transi- tion matrices", by deploying off-line analysis to determine the optimal state matrix, which is then used in table look-up mode at the on-line control level.

Kimenia & Gershwin [73] have devised a multi-level control algorithm with stochastic optimal control at the first level, for FMS with machines prone to failure. They use an estimate based control scheme with the objective of meeting production throughput targets whilst coping with machine failures and avoiding excessive in-process inventory. The application of the concept is il- lustrated using an example system with two process steps and four machines, but there is no evidence of its implementation in industry.

Spur & Seliger [122] have proposed a simultaneous processing scheduling strategy for FMS. They seek to improve system performance by con- trolling the product mix in the system, so that product types being processed concurrently have attributes compatible with the availability profile of system resources. The application of this strategy to the FMS at DIAG in Germany is described and they claim that high machine utili- sation, faster throughput and lower work progress can be obtained.

Shanthikumar & Sargent [116] have developed a hybrid model which combines the analytical aspects of queueing theory with the experimental mode of a simulation approach to determine the mean inventory level and production capacity of FMS, when subject to variations in job arrival rate and buffer capacity. Whilst the approach would appear to overcome some limitations of a conven- tional queueing theory approach the nature of solutions obtained is of similar form in terms of mean values and would therefore appear to be of limited usefulness.

Iwata et al. [66] have described a scheduling approach for FMS using a network graph representation which is used in conjunction with a branch-and-bound optimization procedure to de-

termine minimum make-span schedules. This approach is used to determine the ideal combination of: parts entered, selection of machine tools at each process step and the loading sequence of parts to each machine. An example problem with five machines in a four stage process is used to illustrate the algorithm, but they do not indicate if there are any limitations when applied to larger industrial systems.

Whilst this literature survey has not located any published research deploying "knowledge struc- tures and computational reasoning" ~as in Artifi- cial Intelligence and Expert Systems), it can prob- ably be assumed that contemporary research is in progress using such frameworks to facilitate planning and control decisions for FMS. Fox et al. [36] present a knowledge/reasoning based approach in the context of a constraint directed search process for job shop scheduling. This approach could find application in an FMS environment; although Fox et al. do not specify that possibility. In addition current doctoral research pursuits by Morse and Lim at the University of Nottingham and Rayson at Trent Polytechnic are likely to provide research publications based on such Artificial Intelligence frameworks for automated manufacturing systems in the near future.

3. Multiple Criteria Decision Methods

Production planning decisions in FMS, as in many similar systems, require the fulfilment of several objectives. Approaches to such problems which provide a unified framework for considering several criteria concurrently have been re- ferred to in management science literature as Multi-Objective Decision Analysis or Multiple Criteria Decision Methods (MCDM being a popular abbreviation). A comprehensive review of MCDM methods is provided by Zeleny [150]. Descriptions of such decision methods are also presented by Goicoechea et al. [43], Hwang & Masud [58], and Ignizio [61].

Eilon [26] provides a lucid analysis of the intri- cate role of goals and constraints within the global context of managerial decision systems and clari- fies the use of terminology such as optimizing, satisficing and adaptivizing, which appear in the semantics of contemporary research literature.

Decision making in a multi-objective environ-

Computers in Industry

ment is frequently-hindered by conflicting objectives (see readings edited by Bell et al. [8]), which in extreme cases leads to the conclusion that no solution exists to satisfy all the requirements, especially if the representation of available resources takes the form of "hard" inviolable constraints. However it is possible to circumvent such a stalemate if the search for answers is based on finding compromise solutions with some objectives not entirely fulfilled, facilitated by "soft constraints" which permit deviations from the no- tional limit. An approach with this desired feature is Goal Programming; the original version uses a weighted attainment function as described in the scholarly treatise by Charnes and Cooper [20]. A subsequent variant of this modelling framework using pre-emptive priorities for rank ordered consideration of goal preferences has gained considerable popularity among contemporary researchers for Goal Programming models with comparatively few variables.

The pre-emptive form of Goal Programming originates from the doctoral research of Ijiri [62,63] which was conducted under the supervision of Cooper. ~Subsequent applications and extensions of the pre-emptive version have been undertaken by Jaaskelainen [69], Lee [85] and Ignizio [59]. The main advantage in adopting the pre-emptive ranking formulation is that the problem of having to deal with non-commensurable units for the multiple goals in the attainment function is avoided; but it suffers from the disadvantage that standard mathematical programming software cannot be used, restricting the scope for application to small problems. In contrast, the original version with the weighted attainment function can be solved using standard mathematical programming software and could therefore be preferred for large problems.

The relative merits of the weighted attainment function and the pre-emptive ranking forms of Goal Programming have been debated by a number of writers, notably Charnes & Cooper [21], Goicoechea et al. [43], Ignizio [60,61], Lee [85], Kornbluth [81] and Zeleny [150]. The application of Goal Programming in a number of areas has been the subject of surveys by Kornbluth [81], Ignizio [60] and Lin [86]. The prolific extent of contemporary literature on Multiple Criteria Deci- sion Methods is evident in the bibliographies included in Cochrane & Zeleny [23] and Zeleny [150]. Although Goal Programming remains the

P.J. O'Grady, U. Menon / Concise Review of FMS 163

most popular MCDM approach, a number of alternatives have emerged extending the scope for further applications. Notable examples among these are the developments by Zeleny [150] of Compromise Programming and DeNovo Program- ming.

The belief that user interaction in the algorithmic process could enhance the applicability of solutions obtained has encouraged the development of a number of interactive approaches to MCDM; a contemporary system is described by French [38] outlining Interactive Multi-Objective Programming; its aims, applications and demands with respect to human behaviour. An interactive system for supporting multi-objective decision making and the experimental studies using a computer algorithm are described by Wuwongse et al. [1451.

Other techniques that have become part of the growing literature on MCDM can be found in Goicoechea et al. [43], Zeleny [150], Adulbhan & Tabucanon [2] and in the readings edited by Bell et al. [8].

The review of MCDM literature indicates that several approaches are available for the formulation and analysis of problems which require the consideration of multiple attributes in the choices that have to be made. However the practical application of several of these approaches would seem to be inhibited by the lack of satisfactory computational algorithms which can address the magni- tude and complexity of problems arising in the logistics of automated manufacturing. The development and industrial application of a multiple criteria planning framework for FMS can be found in O'Grady & Menon [107] and in Menon [92].

4. Conclusions

A representative cross-section of the contemporary frameworks for the analysis of planning and control problems associated with Flexible Manufacturing Systems have been reviewed in this paper. The nature of the problems arising in this form of automated manufacture have been recog- nized as being complex. The emergent approaches discussed in this paper offer solutions to some of these problems. The application of these approaches is dependent on sustaining the simplify- ing assumptions which are invoked either to re-

164 Tutorial ('omputer~ ~ lndustrt

d u c e p r o b l e m c o m p l e x i t y to m a n a g e a b l e p r o p o r -

t ions or to r e s t ruc tu re the p r o b l e m so as to m a k e

it c o m p a t i b l e wi th the f o r m a t o f a genera l ap-

p r o a c h , e.g. q u e u e i n g theory , s i m u l a t i o n o r

m a t h e m a t i c a l p r o g r a m m i n g . Th i s rev iew ind ica tes

tha t whi ls t a n u m b e r o f genera l p u r p o s e m e t h o d -

o logies have been a d a p t e d to address F M S p r o b -

l ems there w o u l d a p p e a r to be c o n s i d e r a b l e scope

for fu r the r research .

R e f e r e n c e s

[1] Actis-Dato, M., Bruno, G., Canuto, E. & Menga, G. (1984) "Modelling Flexible Manufacturing Systems by' Closed Queuing Network Analysis'. COMAU spa. Dip di Automatica e Informatica, Politechnico di Torino (Turin).

[2] Adulbhan, P. & Tabucanon, M.T. (Eds.) (1981) Multi- criterion Optimization in Industrial Systems'. Asian Inst. Tech., Bangkok, (Pergamon Press).

[3] Akella, R,, Choong, Y. & Gershwin, S.B. (1984)/'Perfor- mance of Hierarchic Production Scheduling Policy', IEEE Transactions on Components, Hybrids and Manufactur- ing Technology, Vol CHMT-7, 3, 225.

[4] American Machinist (1981) 'CAM: An International Comparison'. Special report 740, American Machinist, November, 207.

[5] ASP Committee/N.E.L. (1978) 'Automated Small Batch Production - The ASP Report'. (National Engineering Laboratory: E. Kilbride).

[6] Askew, M., Edkins, M., Leonard, R. & Kilmartiin, B,R. (1983) "Modelling Multi-Product Manufacturing Systems to Identify Key Components for Selecting CNC Machining Centres and to Aid in the Design of Flexible Manufactur- ing Systems '. Proceedings 24th Intl. Machine Tool De- sign & Research Conference, Manchester, (Macmillan Press), 449.

[71 Barash, M.M., Leimkuhler, F.F., Solberg, J.J. & Tala- vage, J.J. (1981) "Optimal Planning of Computerised Manufacturing Systems'. 8th NSF Grantees Conf., Jan 27-29, (Stanford), J-l, (Washington D.C.: NTIS).

[8] Bell, D.E., Keeney, R.L. & Raiffa, H. (Eds.), (1977) "Conflicting Objectives in Decisions'. (John Wiley).

[9] Bell, R. & Bilalis, N. (1982) "Loading and Control Strate- gies for an FMS for Rotational Parts'. 1st Intl. Conf. on Flexible Manufacturing Systems, Brighton (Bedford: IFS/Elsevier).

[10] Bilalis, N. (1983) "The Design and Control of Flexible Manufacturing Systems for Rotational Parts '. Ph.D. The- sis, Loughborough University of Technology.

[111 Bevans. J.P. (1982) 'First Choose an FMS Simulator'. American Machinist, May.

[12] Browne, J. & Rathmill, K. (1983)"The Use of Simulation Modelling as a Design Tool for FMS'. Proceedings of Second International Conference on FMS, London (Bed- ford: IFS/Elsevier) 197.

[13] Browne, J., Dubois, D., Rathmill, K., Sethi, S.P. & Stecke, K.E. (1984), "Classification of Flexible Manufac- turing Systems'. FMS Magazine, 114.

[14] Buffa, E.S. (Ed.) (1966) 'Readings in Prc~duction and Operations Management ". (John Wiley).

[15] Buzacott, J.A. & Shanthikumar, J.G. (1980) "Models for Understanding Flexible Manufacturing S)'stem.~'. Ameri- can Institute Industrial Engineers Transactions, 12,4.

[16] Buzen, J.P. (1973) "Computational Algorithms for Closed Queueing Networks with Exponential Servers'. Communi- cations of Association of Computing Machinery, 16. 5. 527.

[17] Canuto, E., Menga, G. & Bruno, G. (1983) "Analysis of Flexible Manufacturing Systems'. In: 'Efficiency of manufacturing systems', B. Wilson, C.C. Berg & D. French (Eds.) (Plenum Press) 189.

[18] Carrie, A.S., Adhami, E., Stephens, A. & Murdoch, I.C. (1984) 'Introducing a Flexible Manufacturing System'. In- ternational Journal of Production Research, 22, 6, 907.

[19] Chan, W.W. & Rathmill, K. (1978) 'Digital Simulation of a Proposed Manufacturing System ". Proceedings 19th Intl. Machine Tool Design & Research Conference, Manches- ter (Macmillan Press).

[20] Charnes, A. & W.W. Cooper (1961) 'Management Modeh" and Industrial Applications of Linear Programming ', Vol I, Appendix B, p 210-223. Wiley.

[21] Charnes, A. & Cooper, W.W. (1975) "Goal Programming and Constrained Regression A Comment '. Omega In- ternational Journal of Management Science. 3, 4.

[22] Chen-Chuan, J., Lasserre, J.B. & Roubellat, F. (1981) 'Hierarchical Planning." A Case Study'. Proceedings of IFORS, J.P. Brans (Ed) (N. Holland).

[23] Cochrane, J.L. & Zeleny, M. (1973) 'Multiple Criteria Decision Making'. Seminar papers (University of S. Carolina Press).

[24] Draper Laboratories, Charles Stark (1983) 'Flexible Manufacturing System Handbook', Vols I - IV. (U.S. Dept. Commerce NTIS Publ. No. AD/A127 927-930).

[25] Dupont-Gatelmand, C. (1982) 'A Survey of Flexible Manufacturing Systems '. Journal of Manufacturing Sys- tems, 1, 1.

[26] Eilon, S. (1972) 'Goals and Constraints in Decision Mak- ing'. Operational Research Quarterly, 23, 1, 3.

[27] Eilon, S. (1975) 'Five Approaches to Aggregate Production Planning'. AIIE Trans., Vol 7, No 2, p 16-29.

[28] Elmaleh, J. & Eilon, E. (1974) 'A New Approach to Production Smoothing'. International Journal of Produc- tion Research, 12, 6, 673.

[29] Elmaraghy, H.A. (1982) 'Simulation and Graphical Animation of Advanced Manufacturing Systems '. Journal of Manufacturing Systems, 1, 1.

[30] Eversheim, W. & Westkamper, E. (1975) Computer-Con- trolled Manufac tur ing Sys tems-Plann ing and Organization'. Proc. NAMRAC III, N. American Metal- working Res. Conf. (Dearborn, MI: SME), 728.

[31] Eversheim, W. & Fromm, W. (1983) 'Production Control in Highly Automated Manufacturing Systems'. Proc. AU- TOFACT-Europe, Geneva (Dearborn: SME), p3-1 : 3-13.

[32] Falster, P. & Rolstadas, A. (1982) 'The Integration of Production Management and Flexible Manufacturing" 5th Intl. Conference I F I P / I F A C PROLAMAT 82, Leningrad (N. Holland).

[33] Farkas, J, (1979) "Some Aspects of an Integrated Manufac- turing System'. Ph.D. Thesis, University of Aston.


[34] Fox, K. (1982) 'Simulation for Destgn and Scheduling of Flexible Manufacturing Systems'. Proc. AUTOFACT 4 (Dearborn, MI: CASA/SME) p6-27 : 6-36.

[35] Fox, M.S. (1981) 'Reasoning with Incomplete Knowledge in a Resource-Limited Environment: Integrating Rea- soning and Knowledge Acquisition'. Report No. CMU-RI- TR-81-3 (Robotics Institute, Carnegie-Mellon Univer- sity).

[36] Fox, M.S., Smith, S.F., Allen, B.P., Strohm, G.A. & Wimberly, F.C. (1983) "ISIS: A Constraint Directed Rea- soning Approach to Job Shop Scheduling'. Trends & Ap- plications in Artificial Intelligence (IEEE/Computer Society Press) ~/6.

[37] Fox, M.S. (1983) 'Job Shop Scheduling: An Investigation in Constraint Directed Reasoning'. Ph.D. Dissertation, Carnegie-Mellon University.

[38] French, S. (1984) 'Interactive Multi-Objective Program- ming: Its Aims, Applications and Demands'. Journal Op- erational Research Society, 35, 9, 827.

[39] Furlani, C.M., Kent, E.W., Bloom, H. & Mclean, C.R. (1983) "The Automated Manufacturing Research Facility of the National Bureau of Standards'. Proc. Computer Simu- lation Conference (Vancouver B.C.).

[40] Furth, B., Roubos, P. & Remmerwaal, J.L. (1983) "Mis- chedule - a GT Approach to Sequencing of Jobs'. Annals of CIRP, 32/1/1983.

[41] Gere, W.S. Jr. (1966) 'Heuristics in Job Shop Scheduling'. Management Science, Series A, 13, 3, 167.

[42] Gershwin, S.B., Athans, S.M. & Ward, J.E. (1981) 'Pro- gress in Mathematical Methods for Manufacturing Sys- tems'. 8th NSF Grantees Conference (Stanford) (Washington D.C.: NTIS), G-1.

[43] Goicoechea, A., Hansen, D.R. & Duckstein, L. (1982) 'Multiobjective Decision Analysis with Engineering and Business Applications '. (John Wiley).

[44] Gordon, W.J. & Newell, G.F. (1967) "Closed Queueing Systems with Exponential Servers'. Operations Research, 15, 2, 254.

[45] Gustavsson, S.O. (1984) 'Flexibility and Productivity in Complex Production Processes'. International Journal of Production Research, 22, 5, 801.

[46] Hahn, R.W. (1984) "On Reconciling Conflicting Goals: Applications of Multi-Objective Programming'. Operations Research, 32, 1.

[47] Halevi, G. & Weill, R. (1984) "On-line Scheduling for Flexible Manufacturing Systems'. Annals of CIRP, 33/1/1984.

[48] Hansen, P. (1983) 'Essays and Surveys on Multiple Criteria Decision Making'. Proc. 5th International Conference on Multiple Criteria Decision Making, Mons (Springer- Verlag).

[49] Harrison, C. (1985) 'A Search Sequencing Rule for Manufacturing Systems'. Ph .D. Thesis, University of Nottingham.

[50] Harrison, C. & O'Grady, P.J. (1984) 'Hierarchical Pro- duction Planning and Control'. Proc 1st European Workshop Real-time Control of Large Scale Systems, G. Schmidt (Ed) (Patras, Greece).

[51] Hartley, J. (1984) 'FMS at work', (Bedford: IFS/Else- vier).

[52] Hax, A.C. & Meal, H.C. (1975) 'Hierarchical Integration

of Production Planning and Scheduling'. In: LOGISTICS Vol 1, TIMS studies in Management Science, Geisler M.A. (Ed.) (N. Holland).

[53] Hildebrandt, R.L. (1980) 'Scheduling Flexible Manufac- turing Systems when Machines are Prone to Failure'. Ph. D. Dissertation, M.I.T.

[54] Hillier, F,S. & Lieberman, G.J. (1980) 'Introduction to Operations Research'. (Holden-Day Inc).

[55] Hutchinson, G.K. (1979a) 'Publications on Flexible Manufacturing Systems'. School of Business Paper, Uni- versity of Wisconsin.

[56] Hutchinson, G.K. (1979b) 'Activity Cycles: A Basis for Manufacturing Systems Modefing and Control'. (American Society Mechanical Engineers paper no. 79 WA/DSC- 21).

[57] Hutchinson, G.K. (1983) 'The Design of an Automated Material Handling System for a Job Shop', Computers in Industry, Vol. 4.

[58] Hwang, C.L. & Masud, A.S.M. (1979) 'Multiple Objective Decision Making, Methods and Applications: A State of the Art Survey'. (Springer-Verlag).

[59] Ignizio, J.P. (1976) 'Goal Programming and Extensions', (Lexington Books- D.C. Heath & Co.).

[60] Ignizio, J.P. (1978) 'A Review of Goal Programming: A Tool for Multi-Objective Analysis'. Operational Research Quarterly, 29, 11.

[61] Ignizio, J.P. (1982) 'Linear Programming in Single and Multi-Objective Systems'. (Prentice-Hall).

[62] Ijiri, Y. (1963) 'Goal Oriented Models for Accounting and Control '. Ph.D. Thesis, Carnegie Institute of Technology.

[63] ljiri, Y. (1965) 'Management Goals and Accounting for Control'. (N. Holland).

[64] Ingersoll Engineers (1982) 'The FMS Report'. (Bedford: IFS Publications).

[65] Institution of Mechanical Engineers (1983) "An Anno- tated Bibliography of Flexible Manufacturing Systems'. (London: I. Mech. E.).

[66] lwata, K., Murotsu, A., Oba, F. & Yasuda, K. (1981) "Optimum Production Scheduling in Flexible Manufactur- ing Systems'. Proc. VI Intl. Conference on Production Research (Novi-Sad, Yugoslavia).

[67] lwata, K., Murotsu, A., Oba, F. & Yasuda, K. (1982) 'Production Scheduling of Flexible Manufacturing Sys- tems'. Annals of CIRP, 31/1/1982.

[68] Iwata, K., Oba, F. & Yasuda, K. (1984) "Simulation for Design and Operation of Manufacturing Systems'. Annals of CIRP, 33/1/1984.

[69] Jaaskelainen, V. (1969) 'A Goal Programming Model of Aggregate Production Planning'. Swedish Journal of Eco- nomics, LXXI, 1, 14.

[70] Kay, J.M. & Walmsley, A.J. (1982) 'Computer Aids for the Optimal Design of Operationally Effective FMS'. First Intl. Conference on FMS, Brighton (IFS/Elsevier).

[71] Kay, J.M. & RathmiU, K. (1983) 'When Modelling Can Help Your FMS Plans'. FMS Magazine, 239.

[72] Keen, P.G.W. (1977) "The Evolving Concept of Optimal- ity'. In: Multiple Criteria Decision Making, M.K. Starr & M. Zeleny (Eds.) (N. Holland) 31.

[73] Kimenia, J. & Gershwin, S.B. (1979) 'Network Flow Optimization in Flexible Manufacturing Systems'. 17th IEEE Conference on Decision & Control Including Adaptive Processes (San Diego) 633.

166 Tutorial C~m~puter.s In Industry'

[74] Kimenia, J.G. & Gershwin, S.B. (1980) 'Multicommodity Network Flow Optimization in Flexible Manufacturing Systems'. Report No ESL-FR-834-2, Electronic Systems Laboratory (Boston: M.I.T.).

[75] Kimenia, J,G. (1982) "Hierarchical Control of Production in Flexible Manufacturing Systems', Ph. D. Dissertation, M.I.T.

[76] Kimenia, J.G. & Gershwin, S.B. (1983) 'An Algorithm for the Computer Control of a Flexible Manufacturing System'. Institute of Industrial Engineers Transactions, 15, 4.

[771 Kimenia, J. & Gershwin, S.B. (1985) 'Flow Optimization in Flexible Manufacturing Systems'. International Journal of Production Research, 23, 1, 81.

[78] King, J.R. (1979) 'Scheduling and the Problem of Compu- tational Complexity'. Omega International Journal of Management Science, 7, 3, 233.

[79] King, J.R. & Spachis, A.S. (1980) "Scheduling: Bibliogra- phy and Review'. International Journal of Physical Distri- bution and Materials Management, 10, 3.

[80] Kochan, A. (1983)'See. Wt(v at KTM" FMS Magazine, 305.

[81] Kornbluth, J. (1973) 'A Survey of Goal Programming'. Omega International Journal of Management Science. 1, 2.

[82] Kruse, G. (1977) "Integrated Manufacturing System De- sign in a Multi-Product Company" Ph.D. Thesis, Univer- sity of Aston.

[83] Kuemmel, T.J. (1981) "System Considerations in Flexible Manufacturing Systems'. HOUSTEX Conference Mono- graph MF81-101 (Dearborn MI,: S.M.E.).

[84] Kusiak, A. (1983) "Part Families Selection Model for Flexible Manufacturing Systems'. Proceedings Institute of Industrial Engineers Conference (Louisville).

[85] Lee, S.M. (1972) 'Goal Programming for Decision Analy- sis'. (Auerbach).

[86] Lin, W.T. (1980) 'A Survey of Goal Programming Appli- cations" Omega International Journal of Management Science, 8, 1, 115.

[87] Mandelbaum, M. (1978) 'Flexibility in Decision Making: Exploration and Unification'. Ph.D. Thesis, University of Toronto.

[88] Mayer, R.J. & Talavage, J.J. (1976) "Simulation of a Computerized Manufacturing System'. Report 4, NSF Grant APR 74-15256 (Purdue University).

[89] McCartney, J.'& Hinds, B.K. (1981) "Interactive Schedul- ing Procedures for Flexible Manufacturing Systems" 22nd International Machine Tool Design & Research Con- ference (Macmillan Press).

[90] McCartney, J. & Hinds, B.K. (1982) 'Tooling Economics in Integrated Manufacturing Systems'. International Jour- nal of Production Research, 20, 4, 493.

[91] McMillan, Jr. C. (1970) 'Mathematical Programming: an Introduction to the Destgn and Application of Optimal Decision Machines' (John Wiley).

[92] Menon, U. (1985) 'A Multi-Objective Production Planning Framework for Automated Manufacturing Systems" Ph. D. Thesis, University of Nottingham.

[93] Mills, R.1. (1983) "Computer Simulation - A Feasibility and Planning Tool for FMS', Proceedings Second Intl. Conference on FMS, Brighton (IFS/Elsevier), 185.

[94] Mohanty, R.P. & Krishnawswamy, M.V.R. (1984) 'An

Assessment of Some Approaches to Hierarchical Produc'- tion Planning" International Journal of Production Re- search, 4, 1.

[95] Montgomery, D.C. & Berry, W.L. (Eds.)~1974)'Produc- tion Planning, Scheduling, and Inventor?/' Control: Con- cepts, Techniques, and Systems, (American Institute of Industrial Engineers) Monograph No. 1.

[96] Murotsu, Y., Oba, F., lwata, K. & Yasuda, K. (1983) "A Production Scheduling System for Flexible Manufacturing Systems'. Proceedings of Computer Applications in En- gineering, E.A. Warman (ed.) (IFIP/N. Holland).

[97] Musselman, J. (1984) "'Computer Simulation." A Design Tool for FMS' Manufacturing Engineering, 117.

[98] National Engineering Laboratory (1978) 'Automated Small Batch Production - The ASP Report" (E. Kilbride: National Engineering Laboratory).

[99] Nilsson, N. (1983) 'FIGARMA - Fully Integrated Gar- ment Manufacture'. Computers in Industry Vol. 4.

[100] Nof, S.Y., Barash, M.M. & Solberg, J.J. (1979) 'Oper- ational Control of 1tern Flow in Versatile Manufacturing Systems" International Journal of Production Research, 17, 5, 479.

[101] Nor, S.Y., Whinston, A.B. & Bullers, W.I. (1980) 'Control and Decision Support in Automatic Manufacturing Sys- tems'. American Institute of Industrial Engineers Trans- actions, 12, 2.

[102] Nys, D.A, et al. (co-authors not named) (1983) "Selection of the Optimum Equipment Configuration for Flexible Manufacturing Systems for Machining Housing Type Parts" Stanki i Instrument, Vol 54, Issue 6 1983 (English translation in Soviet Engineering Research, 3, 6).

[103] O'Grady, P. (1981) 'The Application of Discrete Modern Control Theory to the Problem of Production Planning and Control'. Ph.D. Thesis, University of Nottingham.

[104] O'Grady, P.J. & Byrne, M.D. (1985) 'A Combined Switching Algorithm and Linear Decision Rule Approach to Production Planning'. International Journal of Produc- tion Research, 23 (pending publication)

[105] O'Grady, P.J. & Harrison, C. (1985) "A General Search Sequencing Rule for Job Shop Sequencing" International Journal of Production Research (pending publication).

[106] O'Grady, P.J. & Menon, U. (1983) "Work Flow in Flexi- ble Manufacturing Systems'. Proc. 24th International Machine Tool Design & Research Conference, Manches- ter (Macmillan Press) 437.

[107] O'Grady, P.J. & Menon, U. (1985), 'A Multiple Criteria Approach for Production Planning of Automated Manufac- turing'. Engineering Optimization, 8, 3, 161.

[108] Olson, D.L. (1984) "Comparison of Four Goal Pro- gramming Algorithms. Journal of Operational Research Society, 35, 4, 347.

[109] Panwalker, S.S. & Iskander, W. (1977) 'A Survey of Scheduling Rules" Operations Research, 25, 1, 45.

[110] Philips, D.T., Ravindran, A. & Solberg, J.J. (1976) 'Oper- ations Research: Principles and Practice'. (John Wiley).

[111] Ranky, P. (1983) "The Design and Operation of FMS'. (Amsterdam: IFS/N. Holland).

[112] Rathrnill, K. &Chan, W.W. (1984) 'What Simulation Can Do for FMS Design and Planning'. FMS Magazine, April.

[113] Redmond, G.R. (1983) 'The Evaluation of an FMS - a

Computers in lndust O' P.J. O'Grady, U. Menon / Concise Review of FMS 167

Case Study" Proc. Institute of Industrial Engineers Con- ference, (Louisville).

[114] Secco-Suardo, G.M. (1979) 'Workload Optimization in a FMS Modelled as Closed Network of Queues" Annals of CIRP, 28/1/1979.

[115] Seidman, A. & Schweitzer, P.J. (1983) 'Real.Time On-Line Control of a FMS cell" Proc. Institute of Industrial Engineers Conference (Louisville) 562.

[116] Shanthikumar, J.G. & Sargent, R.G. (1981) 'A Hybrid Simulation Model of a Computerized Manufacturing Sys- tem'. Proc. International Federation Operational Re- search Societies, J.P. Brans (Ed.) (N. Holland) 901.

[ 117] Slack, N. ( 1983)' Flexibility as a Manufacturing Objective" International Journal of Operations Management, 3, 3, 4.

[118] Solberg, J.J. (1976) 'Optimal Design and Control of Com- puterized Manufacturing Systems" American Institute of Industrial Engineers Conference (Boston) 138.

[119] Solberg, J.J. (1977) 'A Mathematical Model of Com- puterized Manufacturing Systems'. Proc. 4th International Conference Production Research (Tokyo).

[120] Speed, D.J. (1983) 'Steps to FMS: The Different Ap- proaches" Numerical Engineering, 4, 6, 7.

[1211 Spur, G., Hirn, W. & Seliger, G. (1982) 'The Role of Simulation in Design and Manufacturing Systems'. IFIP/IFAC Conf. PROLAMAT 82 (Leningrad) N. Hol- land.

[122] Spur, G. & Seliger, G. (1983) 'Simultaneous Processing. Scheduling Concept for Flexible Manufacturing Systems" Annals of CIRP, 32/1/1983.

[123] Stanek, W. & Rudolph, K. (1982) "MAO-SIM: An Oper- ation Research Model System for Automatized Manufac- turing Simulation'. IFIP/IFAC Conference PRO- LAMAT 82, Leningrad (N. Holland).

[124] Starr, M.K. & Zeleny, M. (Eds.) (1977) "Multiple Criteria Decision Making'. TIMS Studies in Management Science (N. Holland).

[125] Stecke, K.E. & Solberg, J.J. (1981) "Loading and Control Policies for a Flexible Manufacturing System" Interna- tional Journal of Production Research, 19, 5, 481.

[126] Stecke, K.E. (1981) 'Production Planning Problems for Flexible Manufacturing Systems'. Ph .D. Dissertation, Purdue University.

[127] Stecke, K.E. (1983) "Formulation and Solution of Nonlin- ear Integer Production Planning Problems for Flexible Manufacturing Systems" Management Science, 29, 3, 273.

[128] Stute, G., Bauer, E. & Wilhelm, R. (1976) "How to Find the Optimal Configuration for a Flexible Manufacturing System" Proc. NAMRC IV N. American Metalworking Conf., G. Stule (Ed.) (Dearborn MI: SME) 408.

[129] Suri, R. (1981) 'New Techniques for Modelling and Con- trol of FMS" Proceedings IFAC: International Federa- tion Automatic Control (Tokyo).

[130] Suri, R. & Cao, X. (1983) 'Optimization of Flexible Manufacturing Systems Using New Techniques in Discrete Event Systems '. TIMS/ORSA Conference (Chicago).

[131] Suri, R. & Whitney, C.K. (1983) "Designing a Decision Support System for Flexible Manufacturing" FMS Handbook, Vol 1II, Section 6,0, U.S. Dept of Commerce NTIS Publ. No. AD/A127929.

[132] Taha, H.A. (1982)'Operations Research: an Introduction: 3rd Edition (Macmillan Inc).

[133] Technocrat: Staff Writer (1983) "Features of Japanese Flexible Manufacturing Systems'. Technocrat, 16, 4, 14.

[134] Tipnis, V.A. (1982) "Economic Feasibility of Computer Integrated FMS'. Proc. PROLAMAT 82, Leningrad, IFIP/IFAC (N. Holland).

[135] Vaithianathan, R. (1982) 'Scheduling in Flexible Manu- facturing Systems: Proc. Institute of Industrial Engineers Conference (Cincinnati).

[136] Warnecke, H.J. & Scharf, P. (1973) "Some Criteria for the Development of Integrated Manufacturing Systems" 2nd International Conference on Developments in Produc- tion Systems (Denmark).

[137] Warnecke, H.J. & Vettin, G. (1977) 'Strategies for the Organisation and Control of Discontinuous Conveyors in Flexible Manufacturing Systems: 9th CIRP Seminar on Manufacturing Systems (Cranfield).

[138] Warnecke, H.J. & Steinhilper, R. (1983) 'New Concept of a Manufacturing Cell for Unmanned Production'. Manu- facturing Systems, 12, 1, 26.

[139] Weck, M. & Schuring, A., (1975) 'What Aids are Availa- ble for the Design and Development of Computer Con- trolled Manufacturing Systems'. Proc. NAMRC III, N. American Metalworking Conf. (Dearborn MI.: S.M.E.) 751.

[140] Wilhelm, W.E. & Sarin, S.C. (1983) 'Models for the Design of Flexible Manufacturing Systems" Proc. Institute of Industrial Engineers Conference (Louisville).

[141] Wilhelm, W.E. & Shin, H.-M. (1985) 'Effectiveness of Alternative Operations in Flexible Manufacturing Systems" International Journal of Production Research, 23, 1, 65.

[142] Wild, R. (1971) "The Techniques of Production Manage- ment: Holt, Rinehart and Winston.

[143] Wilson, B., Berg, C.C. & French, D. (Eds.) (1983) "Ef- ficiency of Manufacturing Systems" NATO Conference series, 14 (Plenum Press).

[I44] Wilson, J.M. (1984) 'Formulation and Solution of a Set of Sequencing Problems for Flexible Manufacturing Systems: Working Paper, Dept. Management Studies (Loughbor- ough University of Technology).

[145] Wuwongse, V., Kobayashi, S. & Ichikawa, A. (1983) "An Interactive System for Supporting Multiobjective Decision Making'. Automatica, 19, 6, 697.

[146] Yao, D.D.W. (1983) 'Queueing Models of Flexible Manu- facturing Systems" Ph.D. Thesis, University of Toronto.

[147] Yao, D.D.W. & Buzacott, J.A. (1983) 'Modelling FMS as a Closed Non-Markovian Network of Queues: TIMS/ORSA Conference (Chicago).

[148] Zelenovic, D.M. (1982) 'Flexibility - a Condition for Effective Productive Systems" International Journal of Production Research, 20, 3, 319.

[149] Zeleny, M. (1974) 'Linear Multiobjective Programming" Lecture notes in Econ. & Math. Syst., No 95 (Springer- Verlag).

[150] Zeleny, M. (1982) "Multiple Criteria Decision Making" (McGraw Hill).

Documents

A concise review of flexible manufacturing systems and FMS literature