Multi-Period Distribution Network Design with Boundedly Rational Customers for the Service-Oriented Manufacturing Supply Chain: A 4PL Perspective

Nov 11, 2022·
Yuxin Zhang
,
Zheming Gao
,
Min Huang*
Songchen Jiang
Songchen Jiang
,
Mingqiang Yin
,
Shu-Cherng Fang
· 0 min read
Abstract
With the service level playing an increasingly essential role in the service-oriented manufacturing (SOM) supply chain, the distribution network design can be heavily affected by the customer behaviour based on their satisfaction of services. In this paper, we consider the service level for service time and delivery quantity separately. A novel mixed integer non-linear programming model is proposed to design the multi-period distribution network from a fourth-party logistics (4PL) perspective. The customer satisfaction based on prospect theory is maximised while considering the investment budget and the service level. A scenario-based linear reformulation is proposed to find the optimal solution when the problem scale is small. For a large-scale problem, we propose an individual-driven Q-learning based memetic particle swarm optimisation algorithm. Numerical experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm. Furthermore, the impact of service modes, different customer behaviour, and customer satisfaction evaluation periods on distribution network is investigated. We find that the length of evaluation periods leads to differences in customer satisfaction due to different perceptions of ‘small loss’ and ‘big gain’ by boundedly rational customers.
Type
Publication
International Journal of Production Research
publications
Songchen Jiang
Authors
Researcher in Operations Research
I am a joint Ph.D. student in the College of Information Science and Engineering at Northeastern University, China, supervised by Prof. Min Huang, and the Institute of Operations Research and Analytics at the National University of Singapore, supervised by Prof. Chung-Piaw Teo. My research lies at the intersection of data-driven optimization, distributionally robust optimization, stochastic modeling, and supply chain analytics. I am particularly interested in developing tractable optimization models and algorithms for decision-making under uncertainty, with applications in inventory systems, supply chain network design, logistics planning, and operations management.