Buffer allocation in unreliable production lines using a knowledge based system

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Abstract

One of the most important but difficult optimization problems concerns the optimal allocation of buffers in a production system (line) with stochastic inputs and outputs. The buffer allocation problem is a non-linear problem with integer variables and there exists no closed-form solution for the objective function.

The optimization of production lines performance is a problem of great complexity and, therefore, of significant research interest. The problem may involve the optimization of many conflicting objectives, such as increasing throughput and reducing work-in-process time. The majority of existing studies have used various heuristics and search methods based on operations research. These methods have been proved to be computationally inefficient, especially for large production lines. This paper presents ASBA2, a knowledge based system that determines near optimal buffer allocation plans, with the objective of maximising production lines throughput. The allocation plan is calculated subject to a given amount of total buffer slots, in a computationally efficient way. ASBA2 operates in close cooperation with a simulation method, which provides ASBA2 with performance measures concerning production line behaviour. Moreover, to evaluate results provided by ASBA2, we have utilized an exact numerical algorithm for calculating the throughput of unreliable production lines.

Section snippets

Introduction and literature review

Over the years a large amount of research has been devoted to the analysis and modelling of production lines. For a systematic classification of the relevant works the interested reader is addressed to a review paper by Papadopoulos and Heavey[2].

The allocation of buffer units in production lines is a major optimization problem faced by manufacturing systems designers as well as by researchers. It has to do with devising an allocation plan for distributing a certain amount of buffer space among

The model and the buffer allocation problem

In asynchronous production lines, when a workstation has completed its processing and the next buffer has available space, then the processed part is passed on. Then, the workstation starts processing a new part that is taken from its buffer. In case the buffer has no parts, the workstation remains idle until a new part is placed in the buffer. This type of line is subject to manufacturing blocking (or blocking after service, as it is known in the literature) and starving.

The knowledge based system

Complexity, criticality and the experience intensity of allocating buffers in a production line justify the need for an advisory system. In Ref.[1] we have investigated the use of a specific framework for representation and reasoning, according to which we have structured and encoded knowledge for solving the following problem: given a total buffer size and a minimum required throughput, find a buffer allocation configuration that minimizes the average work-in-process subject to a minimum

Numerical results

In this section, we present numerical results that have been obtained by running ASBA2. Results are split into two classes: (a) for exponential lines and (b) for Erlangian lines.

Specifically, Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, provide numerical results concerning the buffer allocation in production lines with exponentially distributed service and repairing times, whereas, Table 8, Table 9, Table 10, Table 11, Table 12, Table 13, Table 14, provide analogous numerical

Concluding remarks

The paper describes ASBA2, a knowledge based system that solves the well-known problem for buffer allocation in production lines. The system is an extension of ASBA, which authors have described in an earlier paper. ASBA allocates buffer space in reliable production lines, aiming at reducing WIP, subject to a given total buffer space and a required throughput. ASBA computes near optimal buffer allocations for reliable, balanced and unbalanced, production lines, whereas ASBA2 aims to extend the

Acknowledgements

This work has been supported by the Research Committee of the University of the Aegean, the members of which we sincerely thank. Without their support this research work would not have been completed. We highly appreciate the invaluable help of Mr M. Vidalis (Ph.D. student in the Department of Mathematics, University of the Aegean) for the development and running of the exact methods.

George A. Vouros is a Lecturer in the area of Artificial Intelligence at the Department of Mathematics, University of the Aegean, Greece. He received his Ph.D. from the University of Athens in 1992. He has participated in many E.U. funded projects and his current research interests include expert systems and intelligent multimedia systems. He is member of AAAI, IEEE and member of the board of directors of the Hellenic Society for Artificial Intelligence.

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George A. Vouros is a Lecturer in the area of Artificial Intelligence at the Department of Mathematics, University of the Aegean, Greece. He received his Ph.D. from the University of Athens in 1992. He has participated in many E.U. funded projects and his current research interests include expert systems and intelligent multimedia systems. He is member of AAAI, IEEE and member of the board of directors of the Hellenic Society for Artificial Intelligence.

H. T. Papadopoulos (Chrissoleon) is an Associate Professor in the area of Production and Operations Management at the Department of Business Administration of the University of the Aegean, Chios island. He received his B.Sc. (in Mathematics) from the Aristotle University of Thessaloniki, Greece, his M.Sc. (in Operations Research and Informatics) from the National Kapodistrian University of Athens and his Ph.D. in Operations Research and Industrial Engineering from the Department of Industrial Engineering of the University College Galway, Ireland in 1989. Before joining the University of the Aegean, he was the Logistics and Administration Manager and Customer Services Sales Manager at Digital Equipment Corporation (DEC) Hellas. His research interests include stochastic modelling, design and analysis of manufacturing systems, production and operations management, optimization of queueing systems, Logistics and Purchasing management and development of Decision Support Systems. He is co-author of a book on the analysis and design of manufacturing systems, published by Chapman and Hall. He is a member of INFORMS (former ORSA/TIMS).

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