A Three-Stage model for Clustering, Storage, and joint online order batching and picker routing Problems: Heuristic algorithms. (May 2023)

• Record Type: Journal Article
• Authors: Mojaver Tabrizi, A.; Vahdani, Behnam; Etebari, F.; Amiri, M.
• Is Part Of: Computers & industrial engineering. Volume 179(2023)
• Publication Date: 2023-05
• Subjects: Clustering -- Class-based storage -- Online environment -- Joint order batching and picker routing -- Learning effects -- Heuristics; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2023.109180
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Isntroducing a hierarchical framework for planning an online shopping center. Presenting a mathematical model for clustering products. Presenting a mathematical model for the storage assignment problem. Presenting a mathematical model for a joint online order batching and picker routing problem. Offering two operational heuristic and an effective online heuristic algorithms. Abstract: The online shopping center is an electronic commerce where customer orders arrive over time. The joint online order batching and picker routing is essential for managing online shopping, as it allows for the retrieval of products from storage locations as order arrival timings become known. Meanwhile, the picker routing performance is substantially affected by the product allocation to storage locations, whereas the success of the storage assignment depends on the product grouping. In order to handle such a system, this study offers a novel three-stage model, the first stage of which aims to classify products according to various available capacity levels to maximize similarity measures among products. The second stage determines the assignments of the obtained classes to carriers and storage locations in a multi-block 3D warehouse to minimize the total transportation costs and the energy costs of carriers. The third stage provides a joint online order batching and picker routing model in a picker-to-parts system to minimize completion time in the 3D warehouse. Since learning effects affect the search and pick times, and pickers' horizontal and vertical velocities are dissimilar, these crucial factors are considered for calculating travel time of pickers. In addition, two efficient heuristic algorithms are proposed to solve the developed models in the first two stages, and an effective online 2-competitive heuristic algorithm is developed to solve the proposed model in the third stage. Moreover, a broad range of numerical experiments illustrates the validity and applicability of the proposed models and solution approaches. What is more, a competitive analysis evaluates the effectiveness of the proposed online heuristic algorithm. The results reveal that the developed hierarchical framework can provide high-quality solutions in a reasonable time and reduce the total cost and makespan by 35.65% and 12.48% on average, respectively.