With the advent of multi-purpose machining centers in dynamic job shops, scheduling problems are not complete without considering multiple job-routes. Existing scheduling approaches scarcely address the flexibility in job routes. Significant improvements to the scheduiing performance of dispatching rules have been reported using simple machine selection rules. However, these machine selection rules are incapable of assessing the prevailing conditions at the job shop, which precludes the possibility of improving the scheduling performance further. Hence, the authors propose a new machine selection methodology based on fuzzy inference that uses the prevailing conditions at the job shop to dynamically select the most appropriate machine. In this paper three machine selection rules are compared to the new fuzzy machine selection approach and its effectiveness is evaluated through an extensive simulation study of a dynamic job shop under various Job shop conditions. The results indicate that the fuzzy machine selection approach has better performance compared to the machine selection rules.
Fuzzy Logic, Job Shop Scheduling and Machine Selection
All articles published by Studies in Informatics and Control (SIC) are published in accordance with the terms and conditions of the Creative Commons Attribution License (CC BY-NC-SA 4.0).