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19-06-2018 | Original Article | Issue 7/2019

International Journal of Machine Learning and Cybernetics 7/2019

Optimal pricing decision for supply chains with risk sensitivity and human estimation

Journal:
International Journal of Machine Learning and Cybernetics > Issue 7/2019
Authors:
Weimin Ma, Rong Cheng, Hua Ke, Zhang Zhao
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Abstract

In reality, consumption markets are changeable and uncertain in terms of consumer demands, product manufacturing costs, sales costs and so on. When facing these markets, the small and medium manufacturers are usually weak and risk sensitive vis-à-vis their counterpart power retailers, like Walmart, Carrefour, and TESCO. Therefore, human estimations on uncertain market information given by experienced experts and risk sensitivity of the weak manufacturers are of great significance for the optimal pricing strategies. Accordingly, this paper investigates the pricing decision problem in an uncertain supply chain where two risk-sensitive manufacturers distribute substitutable products into the same market through a common dominant and risk-neutral retailer. Uncertainty theory is employed to deal with human-estimation information and chance-constrained programming models are proposed to formulate the pricing decision problem with risk sensitivity. Moreover, numerical experiments are provided to validate the effectiveness of the proposed model. Interestingly, we show that the results critically depend on the comparison between how much the demands and manufacturing costs of two competing products change with the manufacturers’ risk-sensitivity levels. Correspondingly, based on the estimated uncertainty distributions of uncertain variables by experts, the weak manufacturers are suggested to adjust their risk-sensitivity levels strategically for their profitability.

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