Robust possibilistic programming for joint order batching and picker routing problem in warehouse management

Decisions made for designing and operating a warehouse system are of great significance. These operational decisions are strongly affected by total logistics costs, including investment and direct operating costs. The number of orders made by customers in the logistics section of warehouse management is very high because the number, type of products and items ordered by different customers vary broadly. However, machines layout for picking up products at logistics centres is minimal, inflexible, and, in some cases, inconclusive. In this study, we address joint order batching procedures of orders considering picker routing problem as a mixed-integer programming model. Extensive numerical experiments were generated in small, medium, and large sizes. In order to consider the uncertainty of parameters, we applied robust possibilistic programming for this problem. Three different meta-heuristic algorithms; genetic algorithm, particle swarm optimisation algorithm, and honey artificial bee colony algorithms are used as solution approaches to solve the formulated model. The performance of solution approaches over the problem was analysed using several test indexes. In all three group examples, there was no significant difference among mean values of the objective function, while there was a remarkable difference among computing times.

Automated summary

Decisions made for designing and operating a warehouse system are crucial, with total logistics costs and customer order quantities playing key roles. This study focused on joint order batching procedures and picker routing problems, using robust possibilistic programming and meta-heuristic algorithms to address the issue. The performance of the solution approaches was influenced by computing times, with no significant difference observed in the mean values of the objective function across different test examples.