Energy-Saving Scheduling for Flexible Job Shop Problem with AGV Transportation Considering Emergencies

Abstract

Emergencies such as machine breakdowns and rush orders greatly affect the production activities of manufacturing enterprises. How to deal with the rescheduling problem after emergencies have high practical value. Meanwhile, under the background of intelligent manufacturing, automatic guided vehicles are gradually emerging in enterprises. To deal with the disturbances in flexible job shop scheduling problem with automatic guided vehicle transportation, a mixed-integer linear programming model is established. According to the traits of this model, an improved NSGA-II is designed, aiming at minimizing makespan, energy consumption and machine workload deviation. To improve solution qualities, the local search operator based on a critical path is designed. In addition, an improved crowding distance calculation method is used to reduce the computation complexity of the algorithm. Finally, the validity of the improvement strategies is tested, and the robustness and superiority of the proposed algorithm are verified by comparing it with NSGA, NSGA-II and SPEA2.

A Green Flexible Job-Shop Scheduling Model for Multiple AGVs Considering Carbon Footprint

“…During the early stages of the algorithm, there is a requirement to expand the search to obtain as many high-quality individuals as possible, while in the later stages, there is a requirement to ensure that the individuals are stable towards optimal, meanwhile being able to break out of the local optimum. Therefore, this paper introduces an adaptive operator to control the crossover and mutation probabilities by the number of gen [28], as shown in Equations ( 22) and (23).…”

“…Dai et al [22] constructed a multi-situational energy-aware model for FJSP with AGV transportation resource constraints and demonstrated that the transportation time was positively correlated to the overall objective. Wang et al [23] developed an improved NSGA-II for FJSP with job transportation constraints, aiming at makespan, total tardiness and energy consumption.…”

“…Li et al [24] creatively embed machine layout rearrangement, transport equipment allocation and worker assignment in the study of job shop scheduling problem (JSP). Zhang et al [25] similarly embedded limited AGV transport in the study of energy-efficient FJSP with contingencies.…”

A deep reinforcement learning method for job shop scheduling integrated with automated guided vehicles

“…Due to the intricacies of the integrated scheduling problems, current optimization methods predominantly concentrate on the latter two. Among these, metaheuristic algorithms encompass genetic algorithms [3], ant colony optimization [4], artificial bee colony [5], non dominated sorting genetic algorithm-II [6], simulated annealing [7], and others. Despite the ability of metaheuristic algorithms to approximate optimal solutions for various problems, they still encounter challenges such as susceptibility to local optima, slow convergence, and difficulties in parameter adjustment.…”

Research on dual resource flexible job-shop scheduling for textile enterprises

“…Zhou et al considered personnel and key equipment to build a flexible job shop scheduling model and used the ant colony-genetic hybrid algorithm to solve it [3]. Zhang et al proposed an improved nondominated sorting genetic algorithm to solve the dual-resource flexible job shop scheduling problem considering the delivery date [4]. Kress et al established the DRCFJSP model considering the order-dependent preparation time and proposed a solution method based on exact heuristic decomposition [5].…”