An evolutionary algorithm-based decision support system for managing flexible manufacturing

Muzaffer Kapanoğlu

doi:10.1016/j.rcim.2004.07.008

2004-12-01

PRE

Muzaffer Kapanoğlu

William A. Miller

DOI：10.1016/j.rcim.2004.07.008

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摘要

En 中文

The high investment cost of flexible manufacturing systems (FMS) requires their management to be effective and efficient. The effectiveness in managing FMSs includes addressing machine loading, scheduling parts and dispatching vehicles and the quality of the solution. Therefore the problem is inevitably multi-criteria, and decision maker's judgement may contribute to the quality of the solution and the systems's performance. On the other hand, each of these problems of FMS is hard to optimize due to the large and discrete solution spaces (NP-hard). The FMS manager must address each of these problems hierarchically (separately) or simultaneously (aggregately) in a limited time. The efficiency of the management is related to the response time. Here we propose a decision support system that utilizes an evolutionary algorithm (EA) with a memory of good past experiments as the solution engine. Therefore, even in the absence of an expert decision maker the performance of the solution engine and/or the quality of the solutions are maintained. The experiences of the decision maker(s) are collected in a database (i.e., memory-base) that contains problem characteristics, the modeling parameters of the evolutionary program, and the quality of the solution. The solution engine in the decision support system utilizes the information contained in the memory-base in solving the current problem. The initial population is created based on a memory-based seeding algorithm that incorporates information extracted from the quality solutions available in the database. Therefore, the performance of the engine is designed to improve following each use gradually. The comparisons obtained over a set of randomly generated test problems indicate that EAs with the proposed memory-based seeding perform well. Consequently, the proposed DSS improves not only the effectiveness (better solution) but also the efficiency (shorter response time) of the decision maker(s). (C) 2004 Elsevier Ltd. All rights reserved.

Keyword:

evolutionary algorithms

genetic algorithms

memory-based reasoning

flexible manufacturing

decision support systems

operations scheduling

parts assignment