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Link to original content: https://doi.org/10.1007/s12652-018-1094-7
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Robust coordination of supply chain with loss aversion

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Abstract

This paper considers a supply chain consisting of a risk-neutral supplier and a loss-averse retailer under limited distributional information of demand. The robust approach is applied to deal with the integrated decision model and the decentralized decision model when only the mean and variance of demand distribution is assumed to be known. A combined buy-back and loss-sharing contract (BL contract) is designed to coordinate the supply chain and arbitrarily allocate the expected profit between two sides of the supply chain. Specially, the explicit expression of the unique buy-back credit that can coordinate the supply chain is provided when the loss-sharing fraction equals to the reciprocal of the loss aversion degree. Several numerical experiments are performed to illustrate the effects of parameters on the decision.

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Acknowledgements

This research was supported by National Natural Science Foundation of China (Grant no. 71571024), by China Postdoctoral Science Foundation (Grant no. 2015M572450), by Humanities and Social Science Research Program Foundation of the Ministry of Education of China (Grant nos. 16YJC630010, 15YJA630058, 14YJA630087), by Chongqing Postdoctoral Science Foundation of China (Grant no. XM2015101), by Chongqing Research Programof Basic Research and Frontier Technology (Grant no. cstc2018jcyjA2306) and by Scientific Research Foundation of Chongqing University of Technology.

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Authors and Affiliations

  1. Postdoctoral Research Center of Business Administration, Chongqing University, Chongqing, 400030, China

    Jia Zhai

  2. School of Business Administration, Chongqing University of Technology, Chongqing, 400054, China

    Jia Zhai

  3. School of Economics and Business Administration, Chongqing University, Chongqing, 400030, China

    Hui Yu

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  1. Jia Zhai
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Correspondence to Jia Zhai.

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Zhai, J., Yu, H. Robust coordination of supply chain with loss aversion. J Ambient Intell Human Comput 10, 3693–3707 (2019). https://doi.org/10.1007/s12652-018-1094-7

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