An extended multi-objective mixed integer programming for water resources management through possibility theory

In this study, we develop an extended multi-objective mixed integer programming (EMOMIP) approach for water resources management under uncertainty, in which the parameters are fuzzy random variables while the decision variables are interval variables. Furthermore, some alternatives are considered to...

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Vydáno v:Ecological informatics Ročník 54; s. 100992
Hlavní autoři: Nematian, Javad, Movahhed, Sevda Rezazadeh
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.11.2019
Témata:
decision making
Mixed integer programming
Multi-objective programming
Possibility theory
uncertainty
water allocation
Water resources management
Mixed integer programming
Possibility theory
Water resources management
Multi-objective programming
ISSN:1574-9541
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Shrnutí:In this study, we develop an extended multi-objective mixed integer programming (EMOMIP) approach for water resources management under uncertainty, in which the parameters are fuzzy random variables while the decision variables are interval variables. Furthermore, some alternatives are considered to retrieve the difference between the quantities of promised water-allocation targets and the actual allocated water. Then, the proposed EMOMIP for the problem is solved by a new method using fuzzy random chance-constrained programming based on the idea of possibility theory. This method can satisfy both optimistic and pessimistic decision makers simultaneously. Finally, a real example is given to explain the proposed method. •An extended multi-objective formulation is proposed to the water resource problem through possibility theory•Fuzzy chance-constrained programming is used to convert the problem to the deterministic mixed integer programming.•Some alternatives are applied for retrieving water shortages.•The real case study in Tabriz city, Iran is considered in this paper.•The results show the better and acceptable performance of the proposed method.
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ISSN:1574-9541
DOI:10.1016/j.ecoinf.2019.100992