Effective NSGA-II Algorithm for a Limited AGV Scheduling Problem in Matrix Manufacturing Workshops with Undirected Material Flow

Abstract

Automatic guided vehicles (AGVs) are extensively employed in manufacturing workshops for their high degree of automation and flexibility. This paper investigates a limited AGV scheduling problem (LAGVSP) in matrix manufacturing workshops with undirected material flow, aiming to minimize both total task delay time and total task completion time. To address this LAGVSP, a mixed-integer linear programming model is built, and a nondominated sorting genetic algorithm II based on dual population co-evolution (NSGA-IIDPC) is proposed. In NSGA-IIDPC, a single population is divided into a common population and an elite population, and they adopt different evolutionary strategies during the evolution process. The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations. In addition, to enhance the quality of initial population, a minimum cost function strategy based on load balancing is adopted. Multiple local search operators based on ideal point are proposed to find a better local solution. To improve the global exploration ability of the algorithm, a dual population restart mechanism is adopted. Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.