An effective multi-objective adaptive iterated greedy algorithm for semi-open multi-AGV scheduling problem with charging and maintenance

Abstract

The automatic guided vehicle (AGV) scheduling problem has been a research hotspot in recent years due to its wide industrial applications. However, little attention has been paid to multi-AGV scheduling problem with charging and maintenance (MAGVSCM). This paper investigates a MAGVSCM with two objectives of energy consumption and production safety in matrix manufacturing workshop. To solve the MAGVSCM, this paper builds a mixed-integer linear programming model and proposes multi-objective adaptive iterated greedy (MAIG) algorithm. In MAIG, four problem-specific strategies are presented to find partial solutions and external archives. A population initialization strategy is proposed to improve the overall quality of the population. An adaptive strategy in destruction phase is developed to determine the degree of damage to the solution structure through the quality of the solution. An acceptance criterion of solution is designed to determine the population in the next iteration. A large number of experimental results demonstrate that the proposed algorithm is significantly beyond the algorithms in the existing literature in addressing the problem under consideration.