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A Genetic Algorithm Approach to Dynamic Job Shop Scheduling (1997)
| Content Provider | CiteSeerX |
|---|---|
| Author | Lin, Shyh-Chang Goodman, Erik D. Punch, William F. |
| Description | This paper describes a genetic algorithm approach to the dynamic job shop scheduling problem with jobs arriving continually. Both deterministic and stochastic models of the dynamic problem were investigated. The objective functions examined were weighted flow time, maximum tardiness, weighted tardiness, weighted lateness, weighted number of tardy jobs, and weighted earliness plus weighted tardiness. In the stochastic model, we further tested the approach under various manufacturing environments with respect to the machine workload, imbalance of machine workload, and due date tightness. The results indicate that the approach performs well and is robust with regard to the objective function and the manufacturing environment in comparison with priority rule approaches. 1 |
| File Format | |
| Language | English |
| Publisher Date | 1997-01-01 |
| Publisher Institution | Problems”, Proceedings of the Seventh International Conference on Genetic Algorithms |
| Access Restriction | Open |
| Subject Keyword | Objective Function Dynamic Job Shop Stochastic Model Maximum Tardiness Dynamic Problem Genetic Algorithm Approach Weighted Tardiness Priority Rule Approach Machine Workload Manufacturing Environment Flow Time Tardy Job Due Date Tightness |
| Content Type | Text |
| Resource Type | Article |
