Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 257, Issue 1, Pages 185-196Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2016.07.017
Keywords
Facility planning; Order picking methods; Route-selecting order batching model; Lower bound algorithm; S-shape routing method
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2014R1A1A2053550]
- R&D Program of the Korea Railroad Research Institute, Republic of Korea
- National Research Foundation of Korea [2014R1A1A2053550] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Ask authors/readers for more resources
This paper introduces a route-selecting order batching model with the S-shape routing method in parallel-aisle order picking (SRSB). Order pickers in a wide-aisle system prefer the S-shape route taking a u-turn at the last aisle to shorten the travel distance. Although u-turns improve operations, they actually increase computational complexity in order batching. Our study defines a route-set for the S-shape routes and composites a best fit route for batches from the predefined S-shape routes while partitioning orders into batches. The large-scale extension of the SRSB obtains near-optimal solutions by the tight lower bound of the by identifying the route-selection based relaxed batching model. A comparison of the heuristics solution and its pairing lower bound shows 3.5-6.8 percent optimal gaps on average in a six-aisle parallel-aisle system over a 200-500 orders time-window, which outperforms an available best large-scale algorithm with a 9.9 percent shorter travel distance on average. Using the identified lower bound, we evaluate other large-scale batching algorithms in the published literature. We find that a popular savings algorithm shows 8.3-19.8 percent optimal gaps over a variety of large-scale simulation cases. (C) 2016 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available