A matheuristic method for the automated guided vehicle scheduling problem with flexible charging and job release

Abstract

Automated guided vehicles (AGVs) are vital in modern manufacturing for efficient material transport, requiring optimized scheduling to enhance system performance. This study introduces a new AGV scheduling problem that integrates task assignments, processing sequences, and flexible charging operations while accounting for job release times. The problem is NP-hard since it combines parallel machine scheduling and bin packing. We first formulate it as a mixed-integer linear program (MILP), which is strengthened by a set of valid inequalities. To address practical-sized instances, a matheuristic approach combining MILPs and an adaptive large neighborhood search is proposed. Key innovations include a three-step initialization algorithm for high-quality solutions, tailored destroy-and-repair operators, and a specialized evaluation function for efficient makespan approximation. Extensive experiments on 360 instances show that the matheuristic outperforms the commercial solver CPLEX in both solution quality and efficiency. Sensitivity analyses offer managerial insights into factors like charging strategies and energy management, supporting decision-making in AGV scheduling. A performance profit plot and a time-to-target plot are drawn to further validate the performance of the proposed matheuristic. The method also achieves 230 new best solutions for benchmark problems with slight modifications, demonstrating its versatility and effectiveness.