Version 1
: Received: 30 January 2024 / Approved: 31 January 2024 / Online: 1 February 2024 (04:24:06 CET)
Version 2
: Received: 3 February 2024 / Approved: 5 February 2024 / Online: 5 February 2024 (07:29:22 CET)
Version 3
: Received: 5 February 2024 / Approved: 6 February 2024 / Online: 6 February 2024 (04:15:07 CET)
Version 4
: Received: 1 September 2024 / Approved: 2 September 2024 / Online: 2 September 2024 (17:02:17 CEST)
How to cite:
Alnashmy, M. Optimizing Travel Distance in a Synchronized-Zone Order Picking System: A Mathematical Programming Approach. Preprints2024, 2024012218. https://doi.org/10.20944/preprints202401.2218.v3
Alnashmy, M. Optimizing Travel Distance in a Synchronized-Zone Order Picking System: A Mathematical Programming Approach. Preprints 2024, 2024012218. https://doi.org/10.20944/preprints202401.2218.v3
Alnashmy, M. Optimizing Travel Distance in a Synchronized-Zone Order Picking System: A Mathematical Programming Approach. Preprints2024, 2024012218. https://doi.org/10.20944/preprints202401.2218.v3
APA Style
Alnashmy, M. (2024). Optimizing Travel Distance in a Synchronized-Zone Order Picking System: A Mathematical Programming Approach. Preprints. https://doi.org/10.20944/preprints202401.2218.v3
Chicago/Turabian Style
Alnashmy, M. 2024 "Optimizing Travel Distance in a Synchronized-Zone Order Picking System: A Mathematical Programming Approach" Preprints. https://doi.org/10.20944/preprints202401.2218.v3
Abstract
This paper offers an integrated approach for correlated storage assignment strategy (CSAS) to optimize travel distance by considering item correlation, picking frequency, zoning, and slot constraints to improve operational efficiency in distribution centers. We simplify the mixed integer non-linear programming (MINLP) model and incorporate integrated heuristic procedures for faster convergence. We introduce positive and negative centroid deviations as techniques to guide the model convergence and explore different scenarios. In the second stage of item assignment, we prioritize items within zones using a ranking formula that is optimal for both single-item and multiple-item orders. By analyzing the distribution of correlated item across zones and the impact of their picking frequency on optimizing travel distance, we propose an improved CSAS that minimize travel distance by strategically placing items based on their proximity to the depot. Our model reduces trips in distant zones by prioritizing larger average order sizes, while maximizing trips for frequently ordered smaller lists in closer zones. This method significantly reduces travel distance by minimizing the need for pickers in distant zones to frequently travel to the depot. Simulation results show reductions of up to 36.75% for fitted data and 33.59% for recent data.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
