Uncertain random multilevel programming with application to production control problem

Ke, Hua; Su, Taoyong; Ni, Yaodong

doi:10.1007/s00500-014-1361-2

Cited by 71 publications

(24 citation statements)

References 28 publications

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“…Differing from the literatures above, this paper addresses the pricing equilibria under circumstances with only belief degrees being available, which can be described by uncertainty theory. By far, the new theory has been successfully applied to deal with many uncertain decision-making problems, e.g., option pricing [30,31], facility location [32], portfolio selection [33], inventory problem [34], project scheduling problem [35][36][37], and production control problem [38]. Recently, Huang and Ke [8] applied uncertainty theory to a pricing decision problem in a supply chain with one common retailer.…”

Section: Literature Reviewmentioning

confidence: 99%

Pricing Decision in a Two-Echelon Supply Chain with Competing Retailers Under Uncertain Environment

et al. 2017

J. Uncertain. Anal. Appl.

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This paper explores a supply chain pricing competition problem in a two-echelon supply chain with one manufacturer and two competing retailers. The manufacturing costs, sales costs, and market bases are all characterized as uncertain variables whose distributions are estimated by experts' experienced data. In consideration of channel members' different market powers, three decentralized game models are employed to explore the equilibrium behaviors in corresponding decision circumstances. How the channel members should choose their most profitable pricing strategies in face of uncertainties is derived from these models. Numerical experiments are conducted to examine the effects of power structures and parameters' uncertain degrees on the pricing decisions of the members. The results show that the existence of dominant powers in the supply chain will increase the sales prices and reduce the profit of the whole supply chain. It is also found that the supply chain members may benefit from higher uncertain degrees of their own costs while the other supply chain members will gain less profits. Additionally, the results demonstrate that the uncertainty of the supply chain will make end consumers pay more. Some other interesting managerial highlights are also obtained in this paper.

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Section: Literature Reviewmentioning

confidence: 99%

Pricing Decision in a Two-Echelon Supply Chain with Competing Retailers Under Uncertain Environment

et al. 2017

J. Uncertain. Anal. Appl.

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“…As extensions, Zhou et al [37] proposed uncertain random multi-objective programming for optimizing multiple, incommensurable, and conflicting objectives. After that, uncertain random programming was developed steadily and applied widely; Qin [38] proposed uncertain random goal programming in order to satisfy as many goals as possible in the order specified, and Ke [39] proposed uncertain random multilevel programming for studying decentralized decision systems. In order to quantify the rise of uncertain random systems in which the leader and followers may have their own decision variables and objective functions, Liu and Ralescu [40] invented the tool of uncertain random risk analysis.…”

Section: Theorem 5 (Liumentioning

confidence: 99%

Chance distribution of the maximum flow of uncertain random network

Sheng

Gao

2014

J. Uncertain. Anal. Appl.

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A network is called uncertain random network if some arc capacities of the network are uncertain variables and others are random variables. The main purpose of this paper is to study the maximum flow in an uncertain random network. Under the framework of chance theory, this paper obtains chance distribution of the maximum flow of an uncertain random network. At the same time, the expected value of maximum flow is given for an uncertain random network. A new method is derived to calculate chance distribution of the maximum flow for an uncertain random network.

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“…As an efficient tool for dealing with indeterminate phenomena, uncertainty theory has been studied by many researchers. It has been applied to many fields, such as uncertain programming (Ke et al 2015;Zhong et al 2017;, uncertain process (Yao and Li 2012;Yao andZhou 2016, 2017), uncertain network (Zhang et al 2013;Zhou et al 2014a, b), uncertain logic (Li and Liu 2009;Zhang and Li 2014), uncertain finance Ji and Zhou 2015b;Zhou et al 2017), uncertain differential equation (Ji and Zhou 2015a;Su et al 2016), uncertain agency problem (Wu et al 2014;Yang et al 2014), among others.…”

Section: Introductionmentioning

confidence: 99%

The covariance of uncertain variables: definition and calculation formulae

Zhao

Liu

Ralescu

et al. 2017

Fuzzy Optim Decis Making

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Uncertainty theory as a branch of axiomatic mathematics has been widely used to deal with human uncertainty. The two commonly used numerical characteristics of uncertain variables, the expected value and the variance together with their mathematical properties have been discussed and applied to real optimization problems in an uncertain environment. As a further study, in this paper, we focus on the covariance and correlation coefficient of uncertain variables. The definitions and calculation formulae of covariance and correlation coefficient of two uncertain variables are suggested by means of their inverse distributions. Then we show that the correlation coefficient of uncertain variables is essentially a measure of the relevance of distributions of uncertain variables. Finally, the relation between variance and covariance is analysed and represented with some equalities and inequalities.

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Uncertain random multilevel programming with application to production control problem

Cited by 71 publications

References 28 publications

Pricing Decision in a Two-Echelon Supply Chain with Competing Retailers Under Uncertain Environment

Pricing Decision in a Two-Echelon Supply Chain with Competing Retailers Under Uncertain Environment

Chance distribution of the maximum flow of uncertain random network

The covariance of uncertain variables: definition and calculation formulae

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