Genetic Scheduler for Job-Shops

Abstract

In this paper, a genetic algorithm approach is described for generating Job Shop Schedules (JSS) in a discrete manufacturing environment. Genetic algorithm (GA) is used as an effective search technique for finding an optimal schedule via a population of gene strings which represent alternative feasible schedules. Each chromosome is referred to a sequence of operation that represent as a priority queue. Specifically, a gene string should have a structure that imposes the most common restrictive constraint; a precedence constraint. GA propagates new population of genes through number of cycles called generations by implementing natural genetic mechanism. Two genetic operators, namely order-based crossover and order-based mutation, are included to overcome the difficulties of the precedence constraint. The proposed approach is prototyped and tested on four different JSS problems based on the problem size namely; small, medium, large, and a sample problem provided by a company. The comparative results indicate that the proposed approach are consistently better than those of heuristic algorithms used extensively in industry.

Department(s)

Engineering Management and Systems Engineering

Second Department

Nuclear Engineering and Radiation Science

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 The Authors, All rights reserved.

Publication Date

01 Dec 1994

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