Abstract
An efficient storage strategy for retail e-commerce warehousing is important for minimizing the order retrieval time to improve the warehouse-output efficiency. In this paper, we consider a model and algorithm to solve the cargo location problem in a retail e-commerce warehouse. The problem is abstracted into storing cargo on three-dimensional shelves, and the mathematical model is built considering three objectives: efficiency, stability, and classification. An artificial swarm algorithm is designed to solve the proposed models. Computational experiments performed on a warehouse show that the proposed approach is effective at solving the cargo location assignment problem and is significant for the operation and organization of a retail e-commerce warehouse.
Highlights
Under the new retail model, customers have higher and higher requirements on the timeliness of online shopping distribution with the rapid popularization of online shopping
(3) Figure 4 illustrates that the optimized result of artificial fish swarm algorithm (AFSA) is superior to that of the particle swarm optimization (PSO) and genetic algorithm (GA) in terms of the forklift operating time. e functional values using the PSO and GA are reduced from the initial value 983.2857 to 787.3571 and 672.5714, which reduce 19.9259% and 31.5996%, respectively
After the three algorithms are each iterated 200 times, the optimized result diagrams that are obtained are shown in Figures 8 and 9. e optimized results obtained based on the three algorithms reveal the following: (1) e results of the PSO, GA, and AFSA show that the program can converge after a limited number of iterations and obtain optimized results
Summary
Under the new retail model, customers have higher and higher requirements on the timeliness of online shopping distribution with the rapid popularization of online shopping. Many scholars studies on cargo location assignment primarily from the viewpoints of cargo turnover efficiency, shelf stability, and warehouse storage strategy to minimize the total order picking distance. Many authors have studied some optimization problems, including picking routes, location assignment, and picking order distance to minimize operational cost. Tian et al studied the optimal location of a transportation facility and automotive service enterprise issue and presented a novel stochastic multiobjective optimization to address it [21] Some of these studies considered the inbound and outbound warehouse times, the stability of shelves, and the classification of cargo, as we do in this paper. According to the actual layout of warehouse shelves, the influence of X parity on the in-out storage efficiency in x-axis direction is considered on the basis of the existing optimization research model of cargo location.
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