Multi-Time Constrained Dual-Resource Flexible Job Shop Scheduling Based on Multi-Objective Evolutionary Algorithm

This paper presents a new generalization of the uniform dual-resource constrained flexible job shop scheduling problem (DRCFJSP) for automated flexible manufacturing system, considering two multi-time constraints of setup time and transportation time. Design a mixed integer nonlinear programming model with multiple optimization objectives of minimizing maximum completion time, minimizing transportation time and minimizing setup time. And a hybrid multi-objective evolutionary algorithm (HMOEA) is proposed to solve the problem. Through simulation experiments on different data sets and algorithm comparison, the effectiveness and feasibility of the proposed algorithm are verified. Finally, we designed thirteen processing cases under different conditions and compared them with four different comparison algorithms, and concluded that the HMOEA algorithm is superior in solving the DRCFJSP problem, which is of great significance to the use of algorithms for optimal design in actual production.