Discrete Differential Evolution Algorithm with the Fuzzy Machine Selection for Solving the Flexible Job Shop Scheduling Problem

Abstract

The objective of the research is to solve the flexible job shop scheduling problem (FJSP). In this paper, the new algorithm is proposed mainly based on discrete concepts of the differential evolution (DE) algorithm with the new idea called the fuzzy machine selection approach. In the first step, the initial population is created by using a set of the population generation rules. The second step, the arithmetic swapping operation is applied to search for the new operation sequence. In addition, the fuzzy machine selection is utilized to select the proper machine according to the number of operation load and the machine processing time load. Next, the precedence preserving order-based crossover (POX) and the uniform crossover operation are used to enhance the exploitation capability in the third step. The fuzzy machine selection approach embedded the new criteria is used in the local search process to explore the neighbor solution in the surrounding areas of the best 10% of all solutions. The comparative result shows the best performance of the proposed algorithm when compared with the other comparison algorithms.