Unsupervised Learning-Driven Matheuristic for Production-Distribution Problems

Tao Wu (), Canrong Zhang (), Weiwei Chen (), Zhe Liang () and Xiaoning Zhang ()
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Tao Wu: School of Economics & Management, Tongji University, 200092 Shanghai, China
Canrong Zhang: Research Center for Modern Logistics, Shenzhen International Graduate School, Tsinghua University, 518055 Shenzhen, China
Weiwei Chen: Department of Supply Chain Management, Rutgers University, Piscataway, New Jersey 08854
Zhe Liang: School of Economics & Management, Tongji University, 200092 Shanghai, China
Xiaoning Zhang: School of Economics & Management, Tongji University, 200092 Shanghai, China

Transportation Science, 2022, vol. 56, issue 6, 1677-1702

Abstract: In this paper, we study a capacitated production-distribution problem where facility location, production, and distribution decisions are tightly coupled and simultaneously considered in the optimal decision making. Such an integrated production-distribution problem is complicated, and the current commercial mixed-integer linear programming (MILP) solvers cannot obtain favorable solutions for the medium- and large-sized problem instances. Therefore, we propose an unsupervised learning-driven matheuristic that uses easily obtainable solution values (e.g., solutions associated with the linear programming relaxation) to build clustering models and integrates the clustering information with a genetic algorithm to progressively improve feasible solutions. Then we verify the performance of the proposed matheuristic by comparing its computational results with those of the rolling horizon algorithm, a non-cluster-driven matheuristic, and a commercial MILP solver. The computational results show that, under the same computing resources, the proposed matheuristic can deliver better production-distribution decisions. Specifically, it reduces the total system costs by 15% for the tested instances when compared with the ones found by the commercial MILP solver. Additionally, we apply the proposed matheuristic to a related production-distribution problem in the literature and obtain 152 equivalent or new best-known solutions out of 200 benchmark test instances.

Keywords: production-distribution; production planning; lot sizing; facility location; unsupervised learning; cluster analysis; genetic algorithms; machine learning (search for similar items in EconPapers)
Date: 2022
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