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.
Recommended Citation
S. Sittisathanchai et al., "Genetic Scheduler for Job-Shops," Artificial Neural Networks in Engineering - Proceedings (ANNIE'94), vol. 4, pp. 351 - 356, Dec 1994.
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