Uncertain Optimization of Discrete Supply Networks with Order Delivery Disruption and Risk Preference in the Postepidemic Era

Author:

Song Yang12ORCID,Liu Yan-qiu1,Sun Qi34ORCID,Xu Hai-tao5ORCID,Chen Ming-fei6ORCID

Affiliation:

1. School of Management, Shenyang University of Technology, Shenyang, China

2. School of Economics and Management, Liaoning Finance Vocational College, Shenyang, China

3. Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai, China

4. Zhejiang Ribon Intelligent Equipment Co., Ltd., Jiaxing, China

5. Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, USA

6. SLZY (Shenyang) Hi-Tech Co., Ltd., Shenyang, China

Abstract

Epidemic blockade leads to increased uncertainty and dynamic supply network disruption. This study considers an uncertain optimization of dynamic supply networks with risk preference and order delivery disruption. Taking the subjective utility of downstream enterprises as a reference point for the utility measurement of order delivery disruption and risk preference, this study constructs a biobjective optimization model with the goal of maximizing the downstream firm’s subjective utility and minimizing the manufacturer’s cost. The influence of each parameter in the downstream firm’s subjective utility function on the integrated optimization was analysed. The research found that the uncertain optimization model with the risk preference of downstream firms for order delivery disruption better controls the actual manufacturer’s order allocation and distribution problems when considering the downstream firms’ behaviour preference characteristics under bounded rationality. When allocating orders, manufacturers should consider that changes in order delivery disruption will cause changes in the subjective utility of downstream enterprises. In the process of multiperiod cooperation between manufacturers and downstream firms, they can obtain downstream firm risk preferences through repeated investigations.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Modeling and Simulation

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