Integrated Genetic Algorithm with Dispatching Rules to solve the Flexible Job Shop Scheduling Problem under Multi-AMR Transportation Constraints

Abstract

The Flexible Job Shop Scheduling Problem (FJSSP) is a challenging issue for industries and manufacturers. However, the transportation tasks in this problem should not be underestimated, as their impact is substantial, and they are frequently overlooked in the literature. In this paper, the FJSSP with multi-AMR (Autonomous mobile robot) transportation constraints is addressed. We developed a genetic algorithm with dispatching rules to solve the Flexible Job Shop Scheduling Problem with Multi-AMR Transportation Constraints by minimizing the total completion time. Two dispatching rules are used to assign transportation robots. The first rule is random assignment and the second rule is AMR per Job assignment. Our encoding and decoding process utilizes a three-layer sequence (machine sequence, operation sequence, and AMR sequence). Finally, we conducted comprehensive experiments on different benchmarks to assess the performance of the proposed algorithm with a variable number of AMR to transport the operations. The experimental results indicate that the proposed algorithm effectively and efficiently solve the problem.