An effective self-adaptive iterated greedy algorithm for a multi-AGVs scheduling problem with charging and maintenance

The automatic guided vehicle (AGV) scheduling problem in matrix manufacturing workshop has been a research hotspot in recent years because of its wide industrial applications. However, the research on multi-AGVs scheduling problem with charging and maintenance (MAGVSCM) is rarely reported. In the paper, the MAGVSCM is investigated to reduce the total cost composed of travel cost, penalty cost and vehicle cost. To this end, a mixed-integer linear programming model and a self-adaptive iterated greedy (SAIG) algorithm are pro-posed. Based on the problem characteristics, a new solution presentation and the solution accelerated evaluation method are presented and applied in the SAIG. In the SAIG, several nearest-neighbor-based improved heuristics are proposed and used to generate an initial solution. A destruction procedure with self-adaptive strategy is developed to enhance the exploration capability of the algorithm. A merging local search method is used to reduce the number of AGVs as much as possible without lowering the total cost. An acceptance criterion with restart strategy is designed to determine the current solution in the next iteration. A large number of experi-mental results show that the proposed algorithm significantly outperforms the existing algorithms in solving the problem under consideration.

Automated summary

This paper investigates the scheduling problem of multi-AGVs with charging and maintenance (MAGVSCM) in a matrix manufacturing workshop. It proposes a mixed-integer linear programming model and a self-adaptive iterated greedy (SAIG) algorithm to reduce the total cost. The experimental results show that the proposed algorithm significantly outperforms existing algorithms in solving the problem.