Modelling the Shortest Path for Inner Warehouse Travelling Using the Floyd–Warshall Algorithm

Order picking is referred as a critical process of selecting items requested by a customer in a warehouse. Meeting the demand of every customer is the main objective in this area. Large warehouses pose a challenge since the order-picking process is slowed considerably by the lengthy time it takes to transport items across the warehouse. Throughout the study, the system is hoped to develop proper procedures in the order-picking process. In handling this scenario, the decision-makers need to take any possible action to ensure the warehouses can keep operating and meeting the requirements and satisfaction of the customers. Due to this, the study’s main objective is to determine whether the Floyd–Warshall algorithm or the dynamic programming method will give the most accurate shortest path and minimum travel distance for order pickers. Two data sets (nine nodes and nineteen nodes) are used to determine the optimal path and minimum travel distance for the order picker to meet and satisfy customer orders for the warehouse. The two models were modified and applied to address real-world case studies from the automotive manufacturing company in Malaysia. The results show a big difference between the total distance by 113.48% for 19 nodes. Through this finding, the company may choose which method suits their preferences. Concurrently, this study may also contribute to problem-solving issues in any warehouse operation with a similar procedure.

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Publication Details

Journal
Mathematics
Published
2024-08-29
DOI
https://doi.org/10.3390/math12172698
Citations
6
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
2.09
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article

Modelling the Shortest Path for Inner Warehouse Travelling Using the Floyd–Warshall Algorithm

Ilyas Masudin, S. Sarifah Radiah Shariff, Siti Suzlin Supadi, Noraimi Azlin Mohd Nordin
6 citations
Mathematics
Advanced Manufacturing and Logistics Optimization
2.09
article

Modelling the Shortest Path for Inner Warehouse Travelling Using the Floyd–Warshall Algorithm

Ilyas Masudin, S. Sarifah Radiah Shariff, Siti Suzlin Supadi, Noraimi Azlin Mohd Nordin
article en
6 citations

Abstract

Order picking is referred as a critical process of selecting items requested by a customer in a warehouse. Meeting the demand of every customer is the main objective in this area. Large warehouses pose a challenge since the order-picking process is slowed considerably by the lengthy time it takes to transport items across the warehouse. Throughout the study, the system is hoped to develop proper procedures in the order-picking process. In handling this scenario, the decision-makers need to take any possible action to ensure the warehouses can keep operating and meeting the requirements and satisfaction of the customers. Due to this, the study’s main objective is to determine whether the Floyd–Warshall algorithm or the dynamic programming method will give the most accurate shortest path and minimum travel distance for order pickers. Two data sets (nine nodes and nineteen nodes) are used to determine the optimal path and minimum travel distance for the order picker to meet and satisfy customer orders for the warehouse. The two models were modified and applied to address real-world case studies from the automotive manufacturing company in Malaysia. The results show a big difference between the total distance by 113.48% for 19 nodes. Through this finding, the company may choose which method suits their preferences. Concurrently, this study may also contribute to problem-solving issues in any warehouse operation with a similar procedure.

MathematicsVol. 12(17)
University of Malaya (MY), Universitas Muhammadiyah Jember (ID), Universiti Teknologi MARA (MY)
Openalex Percentile: Top 12%
Advanced Manufacturing and Logistics Optimization
2.09
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Modelling the Shortest Path for Inner Warehouse Travelling Using the Floyd–Warshall Algorithm — Ilyas Masudin, S. Sarifah Radiah Shariff, et al. · Mathematics (2024) | TGRS Research Map | TGRS