A hybrid of adaptive large neighborhood search and tabu search for the order-batching problem |
| |
Authors: | Ivan ?ulj Sergej Kramer Michael Schneider |
| |
Institution: | 1. Department of Procurement and Production, University of Hohenheim, Schwerzstr.?40, Stuttgart 70599, Germany;2. Deutsche Bahn AG, DB Management Consulting, Gallusanlage.?8, Frankfurt am Main 60329, Germany;3. Deutsche Post Chair – Optimization of Distribution Networks, RWTH Aachen University, Kackertstr.?7 B, Aachen 52072, Germany |
| |
Abstract: | 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. |
| |
Keywords: | Logistics Order batching Adaptive large neighborhood search Tabu search Hybrid metaheuristics |
本文献已被 ScienceDirect 等数据库收录! |
|