A Multi-Objective Flexible Job-Shop Scheduling Model Based on Fuzzy Theory and Immune Genetic Algorithm
Dailun Shi, Baitao Zhang, Yue-Wu Li
Abstract
This paper explores flexible job-shop scheduling problem (FJSP), using the rolling window rescheduling strategy. The fuzzy delivery time was considered, which satisfies the trapezoidal delivery window of the fuzzy membership function and directly bears on consumer satisfaction. Taking machine failure as the cause of dynamic interferences, the author established a dynamic scheduling model for the FJSP with fuzzy delivery time, according to the fuzzy mathematics theory. The model has multiple objectives: minimizing energy consumption, maximum makespan and consumer dissatisfaction. Next, the immune genetic algorithm (IGA) was improved to solve the model. The established model and the improved IGA were verified through simulations, in comparison with the genetic algorithm (GA). The research results shed new light on the FJSPs in real-world scenarios.