Data Driven Efficiency for E-Warehousing: Descriptive and Prescriptive Analytics
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Date
2018-05-18
Authors
Ugur, Yildiz
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
Based on data provided by a warehouse logistics management company, we analyze the warehousing operation and its major processes of order picking and order consolidation. Without access to the actual layouts and process flow diagrams, we analyze the data to describe the processes in detail, and prescribe changes to improve the operation.
We investigate the characteristics of the order preparation process and the order consolidation operation. We find that products from different orders are mixed for effective picking. Similar products from different orders are picked together in containers called totes. Full totes are stored in a buffer area, and then routed to a conveyor system where products are sorted. The contents of the totes are then consolidated into orders. This order consolidation process depends on the sequence in which totes are processed and has a huge impact on the order completion time. OCP is a new problem for both the warehouse management system and the parallel machine scheduling literature. We provide mathematical formulations for the problem and devise two solution methods. The first is a simulated annealing metaheuristic, while the second is an exact branch-and-price method.
We test the solutions on both random and industry data. Simulated Annealing is found to achieve near optimal solutions within 0.01 % of optimality. For the branch-and-price approach, we use a set partitioning formulation and a column generation method where the subproblems are single machine scheduling problems that are solved using dynamic programming. We also devise a new branching rule and new dynamic programming algorithm to solve the subproblem after branching. To assess the efficiency of the proposed branch-and-price methodology, we compare against the branch-and-price approach of Chen and Powell (1999) for the parallel machine scheduling problem. We take advantage of the fact that OCP is a generalization of the parallel machine scheduling problem. The proposed, more general, branch-and-price approach achieves the same solution quality, but takes more time.
Description
Keywords
E-warehousing, Data-driven optimization, Data analytics, Branch and price, Simulated Annealing