A New Method for Solving Dynamic Flexible Job Shop Scheduling Problems Integrating Genetic Algorithm and Priority Rules

Abstract

Dynamic flexible job shop scheduling problems has been one of the important and strongly NP-hard problem of manufacturing systems for many years. Most of the proposed algorithms are based on priority rules; By using these rules, the arrived jobs go to a long queue of waited jobs and sometimes it takes a long time for a job to be processed. In this paper a new approach, integrating of priority rules and genetic algorithm is presented, by decomposition of a dynamic problem to smaller dynamic and static problems. A module converts the queue of dynamic jobs to static, and then a genetic algorithm has been used to improve some objective functions.