An estimation distribution algorithm for wave-picking warehouse management
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An estimation distribution algorithm for wave-picking warehouse management Jingran Liang1 · Zhengning Wu2 · Chenye Zhu2 · Zhi-Hai Zhang1 Received: 27 May 2019 / Accepted: 4 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Recently, market has witnessed a tremendous growth in E-commerce sales, which bring tons of opportunities as well as challenges. Warehouses have to handle unique characteristics of customer orders in the era of E-commerce which consists of small order scales, large items count, unexpected irregular order arrival patterns, seasonality demand peeks, and high service level expectations. Warehouses are adopting wave-picking as an effective policy composed of item-batching, loadassignment and picker-routing problems. In this research, principle combination of load-assignment and picker-routing problems is studied. A mixed integer mathematical model is established based on features of a wave-picking warehouse. In order to conquer the complexity caused by routing decision of the proposed problem, a set of effective modified Estimation Distribution Algorithms is developed. The set of proposed algorithms is proved to have stable gaps (1% on average and maximum less than 2%) compared with Cplex 12.8, while can be solved in much larger scale within quite short time (100 pickers and 350 items in each wave within less than 2 min). Keywords Wave picking · Warehouse management · Load assignment problem · Picker routing problem · Estimation distribution algorithm
Introduction In the past few years, there is a tremendous growth in Ecommerce sales. In US, the compound annual growth rate of e-commerce is estimated to be 9.5% since 2013. (Acimovic and Graves 2015). In face of significant growth, the corresponding growth sale market draw lots of attention. Traditionally, sale orders are relatively big and preciously punctual arrivals due to mechanism of contracts. In the field of E-commerce, typical characteristics can be summarized as small order scales, large items count, unexpected irregular order arrival patterns, seasonality demand peeks, and high service level expectations. (Ardjmand et al. 2018) Therefore, traditional warehouse management system (WMS) cannot properly and efficiently handle managerial operations in current circumstance which needs additional modification
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Zhi-Hai Zhang [email protected]
1
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
2
Faculty of Engineering and Information Technologies, University of Sydney, Sydney, NSW 2008, Australia
and enhancement to keep running effectively. Knowledge and application of operations research and effective analytics methods are of vital significance to upgrade WMS capabilities to adapt and work in the trend of E-commerce. This research is to develop suitable and valid models and algorithms to optimize managerial operations include item selections, wave separations and picker routings. To deal with a diverse set of operations in warehouse, most WMS applied a wave-based
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