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Optimizing the Multi-Objective Order Batching Problem for Warehouses with Cluster Picking

Aboelfotoh, Aaya H. F.
http://orcid.org/0000-0002-4029-7756
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1564663802880513

Abstract Details

2019, Master of Science (MS), Ohio University, Industrial and Systems Engineering (Engineering and Technology).
This thesis defines different variants of the cluster-picking order batching problem (OBP) and proposes several problem-solving techniques to solve the problems. The basic OBP is commonly referred to in the literature as the grouping of customer orders into batches in a manner that minimizes the total distance such that the batch capacity is not violated. This problem is of high importance in the warehousing industry since the majority of warehouse costs arise from order picking activities. The first variant involves solving the OBP for a single work interval, where all orders must be completed by the end of the interval’s duration. The objectives addressed are minimizing the total distance traveled, aisle congestion and the total number of pickers. The second variant addresses the scenario in which customer orders are set to have distinct pick-by times, corresponding to different waves or work intervals, in addition to allowing some orders to be picked in earlier intervals to improve the efficiency of grouping. In this variant, the OBP is solved for multiple intervals in order to determine the order-to-batch and batch-to-interval assignments simultaneously such that the total distance, overall aisle congestion, maximum number of pickers needed, and the total number of batches are minimized, whilst taking into account the limit on the number of early orders. In addition, the assignment of multiple batches to pickers is also incorporated as an extension to variant 2. The s-shape routing strategy and single-block rectangular layouts with parallel aisles and are applied throughout the thesis. Moreover, the problem-solving techniques developed for the OBP variants in this thesis are greedy heuristic, differential evolution algorithm and mathematical models. The greedy heuristic primarily focuses on minimizing the total distance, whereas the differential evolution algorithm and mathematical models are able to address multiple objectives. The problem-solving techniques are experimented with using numerous generated datasets which take into account factors such as the problem size, distribution of items across the aisles and the pick-by times of orders, and varying the limit on early orders.
Gursel Suer, PhD (Advisor)
Dale Masel, PhD (Committee Member)
Tao Yuan, PhD (Committee Member)
Ana Rosado Feger, PhD (Committee Member)
214 p.
Engineering; Industrial Engineering; Operations Research
Order Batching; Order Batching Problem; Order Picking; Cluster Picking; Wave Picking; S-Shape; Total Distance; Aisle Congestion; Multiple Intervals; Optimization; Mathematical Model; Differential Evolution; Greedy Heuristic

Recommended Citations

Citations

  • Aboelfotoh, A. H. F. (2019). Optimizing the Multi-Objective Order Batching Problem for Warehouses with Cluster Picking [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1564663802880513

    APA Style (7th edition)

  • Aboelfotoh, Aaya. Optimizing the Multi-Objective Order Batching Problem for Warehouses with Cluster Picking. 2019. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1564663802880513.

    MLA Style (8th edition)

  • Aboelfotoh, Aaya. "Optimizing the Multi-Objective Order Batching Problem for Warehouses with Cluster Picking." Master's thesis, Ohio University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1564663802880513

    Chicago Manual of Style (17th edition)

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This open access ETD is published by Ohio University and OhioLINK.