Green flexible job shop integrated scheduling optimization for machines and AGVs based on the INSGA-II algorithm

Abstract

Reducing carbon emissions is becoming increasingly important in manufacturing, highlighting the need for green scheduling in flexible systems. In real-world manufacturing, jobs often require transportation between machines, making transportation resources essential for effective scheduling and system optimization. This paper addresses the green flexible job shop scheduling problem for Automated Guided Vehicles (AGV) (EVFJSP-AGV), improving coordination between machine operations and AGV allocation to minimize the makespan, energy consumption, and AGV transportation distance. Detailed energy consumption models for both machines and AGVs are developed to promote energy savings. An AGV speed selection mechanism enhances production and transportation coordination while charging constraints ensure practical scheduling. To solve this multi-objective optimization problem, an improved NSGA-II algorithm (INSGA-II) is proposed, incorporating a four-segment coding method, a reverse population strategy, and a multi-neighborhood structure search strategy. Extensive numerical experiments evaluate the performance of the INSGA-II algorithm. Sensitivity analysis across various scales demonstrates the model and algorithm effectively address the green flexible job shop AGV scheduling problem.