A hybrid of adaptive large neighborhood search and tabu search for the order-batching problem

被引:95
|
作者
Zulj, Ivan [1 ]
Kramer, Sergej [2 ]
Schneider, Michael [3 ]
机构
[1] Univ Hohenheim, Dept Procurement & Prod, Schwerzstr 40, D-70599 Stuttgart, Germany
[2] Deutsch Bahn AG, DB Management Consulting, Gallusanlage 8, D-60329 Frankfurt, Germany
[3] Rhein Westfal TH Aachen, Deutsch Post Chair Optimizat Distribut Networks, Kackertstr 7 B, D-52072 Aachen, Germany
关键词
Logistics; Order batching; Adaptive large neighborhood search; Tabu search; Hybrid metaheuristics; PICKING SYSTEMS; ROUTING-PROBLEMS; PICKER BLOCKING; TRAVEL DISTANCE; WAREHOUSE; AISLE; ALGORITHMS; TIME; SOLVE;
D O I
10.1016/j.ejor.2017.06.056
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Given a set of customer orders and a routing policy, the goal of the order-batching problem (OBP) is to group customer orders to picking orders (batches) such that the total length of all tours through a rectangular warehouse is minimized. Because order picking is considered the most labor-intensive process in warehousing, effectively batching customer orders can result in considerable savings. The OBP is NP-hard if the number of orders per batch is greater than two, and the exact solution methods proposed in the literature are not able to consistently solve larger instances. To address larger instances, we develop a metaheuristic hybrid based on adaptive large neighborhood search and tabu search, called ALNS/TS. In numerical studies, we conduct an extensive comparison of ALNS/TS to all previously published OBP methods that have used standard benchmark sets to investigate their performance. ALNS/TS outperforms all comparison methods with respect to both average solution quality and run-time. Compared to the state-of-the-art, ALNS/TS shows the clearest advantages on the larger instances of the existing benchmark sets, which assume a higher number of customer orders and higher capacities of the picking device. Finally, ALNS/TS is able to solve newly generated large-scale instances with up to 600 customer orders and six articles per customer order with reasonable run-times and convincing scaling behavior and robustness. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:653 / 664
页数:12
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