Collaborative Optimization of Storage Location Assignment and Path Planning in Robotic Mobile Fulfillment Systems

The robotic mobile fulfillment system (RMFS) is a new automatic warehousing system, a type of green technology, and an emerging trend in the logistics industry. In this study, we take an RMFS as the research object and combine the connected issues of storage location assignment and path planning into one optimization problem from the perspective of collaborative optimization. A sustainable mathematical model for the collaborative optimization of storage location assignment and path planning (COSLAPP) is established, which considers the relationship between the location assignment of goods and rack storage and path planning in an RMFS. On this basis, we propose a location assignment strategy for goods clustering and rack turnover, which utilizes reservation tables, sets AGV operation rules to resolve AGV running conflicts, and improves the A-star(A*) algorithm based on the node load to find the shortest path by which the AGV handling the racks can complete the order picking. Ultimately, simulation studies were performed to ascertain the effectiveness of COSLAPP in the RMFS; the results show that the new approach can significantly improve order picking efficiency, reduce energy consumption, and lessen the operating costs of the warehouse of a distribution center.

Improving Order-picking Operations with Precedence Constraints through Efficient Storage Location Assignment

This paper is inspired by a manual picking retail company where shape and weight constraints affect the order-picking process. We proposed an alternative clustering similarity index that considers the similarity, the weight and the shape of products. This similarity index was further incorporated in a storage allocation heuristic procedure to set the location of the products. We test the procedure in a retail company that supplies over 191 stores, in Northern Portugal. When comparing the strategy currently used in the company with this procedure, we found out that our approach enabled a reduction of up to 40% on the picking distance; a percentage of improvement that is 32% higher than the one achieved by applying the Jaccard index, a similarity index commonly used in the literature. This allows warehouses to save time and work faster.

A bi-level optimization model for grouping constrained storage location assignment problems

In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.