Load Balancing Job Shop Scheduling with AGVs: A Multi-Objective Optimization Approach

Introducing AGVs in job shops significantly enhances the production efficiency of manufacturing enterprises. However, the majority of existing studies focus on minimizing the makespan, overlooking the load balancing issue during AGV scheduling. Although a few studies attempt to consider electricity and load constraints, yielding unsatisfactory results. To tackle these challenges, this paper proposes a multi-objective optimization approach for load-balancing job shop scheduling with AGVs. Specifically, we begin by formulating the scheduling as a multi-objective optimization problem. Subsequently, utilizing the Non-dominated Sorting Genetic Algorithm (NSGA-II), we solve the multi-objective optimization by designing encoding rules and tailoring mutation and crossover operations. The case study on a real-world scenario demonstrates the feasibility and efficiency of the proposed method in addressing such problems.