Multi-objective optimization in uncertain random environments

Zhou, Jian; Yang, Fan; Wang, Ke

doi:10.1007/s10700-014-9183-3

Cited by 73 publications

(29 citation statements)

References 17 publications

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“…In addition, Sheng and Yao [32] provided some formulas to calculate the variance of uncertain random variables through chance distribution and inverse chance distribution. Zhou et al [33] proposed uncertain random multi-objective programming for optimizing multiple, non-commensurable, and conflicting objectives.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal consumer search with prospect utility in hybrid uncertain environment

Zhou

Zhao

2015

J. Uncertain. Anal. Appl.

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This paper studies a consumer search model with prospect utility in a hybrid uncertain environment. Hybrid uncertainty consists of the uncertainty of the consumer's valuation for each product and the randomness of stockout. An optimal search strategy is designed for the consumer to search or buy a product. The results show that the presence of prospect utility can result in low purchase threshold. By comparative statics, we demonstrate that the consumer's purchase threshold for low-value product increases with the risk coefficients for gains and losses, while the purchase threshold for high-value product decreases with the risk coefficients for gains and losses. In addition, we find that the consumer who is more sensitive to the loss will search for much less products. Finally, some numerical examples are given to illustrate the relationships between the model without and with prospect utility and further verify the effectiveness and credibility of the conclusions.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal consumer search with prospect utility in hybrid uncertain environment

Zhou

Zhao

2015

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.…”

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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“…To model uncertain random event, Liu [8] introduced the chance theory to networks optimization problem. Some scholars derived properties of uncertain random entropy [9,10]. By employing chance theory, an uncertain random project scheduling problem is presented by Ke et al [11].…”

Section: Introductionmentioning

confidence: 99%