Particle Swarm Optimization Using Neighborhood‐Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

Abstract

Outbound container storage location assignment problem (OCSLAP) could be defined as how a series of outbound containers should be stacked in the yard according to certain assignment rules so that the outbound process could be facilitated. Considering the NP-hard nature of OCSLAP, a novel particle swarm optimization (PSO) method is proposed. The contributions of this paper could be outlined as follows: First, a neighborhood-based mutation operator is introduced to enrich the diversity of the population to strengthen the exploitation ability of the proposed algorithm. Second, a mechanism to transform the infeasible solutions into feasible ones through the lowest stack principle is proposed. Then, in the case of trapping into the local solution in the search process, an intermediate disturbance strategy is implemented to quickly jump out of the local solution, thereby enhancing the global search capability. Finally, numerical experiments have been done and the results indicate that the proposed algorithm achieves a better performance in solving OCSLAP.

Storage assignment optimization for fishbone robotic mobile fulfillment systems

“…The intelligent algorithms commonly used to solve the optimization problem of storage assignment include particle swarm optimization [15][16][17], genetic algorithm [17,18], ant colony optimization [19] and other neighborhood search heuristic algorithms [20][21][22]. There have been many studies on the improvement of these algorithms.…”

“…Lee et al [13] proposed a dual-objective storage allocation optimization model that takes picking efficiency and traffic balance into account and uses a multi-objective evolutionary algorithm to solve it. Different improved particle swarm optimization algorithms and improved genetic algorithms are used to solve the problem of outbound storage assignment and job scheduling [14][15][16][17][18][19]. Ning et al [20] put forward a method for optimizing the rack location based on item correlation and rack relevance in the fishbone layout.…”

Structural Damage Identification Based on l1Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy

“…Hu [23] designed a mutation operator similar to PSO iteration to provide individuals a direction in the evolutionary process, which can produce potential good individuals. He et al [24] proposed a neighbourhoodbased mutation operator strategy which is introduced into PSO to achieve the purpose of enhancing population diversity.…”