Streamlining multi-commodity inventory allocation and redistribution

Abstract

To prevent excess unsold goods caused by market fluctuations, retailers can redistribute surplus commodities among their stores to maximise profits. This paper introduces a novel approach to model the common real-world problem of multi-commodity inventory allocation and redistribution. Our unified approach integrates the two problems and seeks to optimise maximum profit. The study encompasses various factors such as inventory capacity, reallocation constraints, vehicle capacity, time windows for pickup and delivery, and a homogeneous fleet of vehicles. We propose two mixed-integer programming paradigms, the integrated and sequential formulations, along with an improved variable neighborhood search (IVNS) algorithm to solve the problem. Computational results demonstrate the effectiveness of the IVNS algorithm, while further analysis highlights the pros and cons of the two formulation paradigms. Notably, the integrated formulation yields superior solutions at the expense of increased computational time.

Keywords:

• Multi-commodity allocation
• redistribution
• pickup and delivery
• variable neighborhood search
• mathematical modeling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

All test instances and their results are available online at https://github.com/pengguo318/M-IARP.

Notes

1 https://map.baidu.com

Additional information

Funding

Peng Guo's research was supported in part by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No. 21YJC630034).

Notes on contributors

Peng Guo

Peng Guo is currently an associate professor at School of Mechanical Engineering, Southwest Jiaotong University. He obtained his BS degree in Industrial Engineering and PhD degree in Mechanical Engineering from Southwest Jiaotong University in 2009 and 2014, respectively. His research interests include operations management of intelligent manufacturing and smart logistics by using operations research and deep reinforcement learning.

Jianyu Xiong

Jianyu Xiong is a master student majoring in industrial engineering at School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China.

Yi Wang

Yi Wang is currently a professor of mathematics in the Department of Mathematics, Auburn University at Montgomery. He earned his second PhD in Mathematics from West Virginia University, USA in 2003, and his first PhD in Mechanical Engineering from Southwest Jiaotong University, China in 1997. His current research interests include engineering optimization and machine learning.

Kun Wen

Kun Wen is a master student majoring in industrial engineering at School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China.