A fuzzy chance-constrained programming model with type 1 and type 2 fuzzy sets for solid waste management under uncertainty

Ma, Xiaolin; Ma, Cuiluan; Wan, Zhifang; Wang, Kewei

doi:10.1080/0305215x.2016.1230206

Cited by 24 publications

(3 citation statements)

References 27 publications

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“…The authors of [11] discussed perfectly normal interval type-2 trapezoidal fuzzy numbers with their left, right-hand spreads and their core. Ma et al [12] developed a chance-constraint programming model under a fuzzy environment, where waste generation amount are supposed to be type-2 fuzzy variable and treated capacities of facilities are assumed to be type-1 fuzzy variables. Agrawal and Ganesh [13] developed developed a method to solve fuzzy fractional transportation problem in which the parameters of the transportation problem, supply, and demand, are stochastic in nature and considered as a fuzzy random variable that follows the exponential distribution with fuzzy mean and fuzzy variance.…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Stochastic Transportation Problem with Gamma Distribution

Chaini

Ranarahu

2023

IJFIS

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The transportation problem in real-life is an uncertain problem. Particularly when goods are transported from the source to destinations with the best transportation setup that satisfies the decision maker's preferences by taking into account the competing objectives/criteria such as maintaining exact relationships between a few linear parameters, such as actual transportation fee/total transportation cost, delivery fee/desired path, total return/total investment, etc. Due to the uncertainty of nature, such a relationship is not deterministic. In this stochastic transportation problem supplies are considered as fuzzy random variables, which follow fuzzy gamma distribution, with shape parameter α and scale parameter β. Here β is a perfectly normal interval type-2 fuzzy random variable. This paper proposes a solution methodology for solving the fuzzy stochastic transportation problem, where fuzziness and randomness occur under one roof. Therefore, we converted it to an equivalent deterministic mathematical programming problem by applying the following two steps. In the first step of the solution procedure, fuzziness is removed by using alpha-cut technique to obtain stochastic transportation problem. In the second step, the stochastic transportation problem is converted to an equivalent crisp transportation problem using the chance constrained technique. This mathematical model is solved by existing methodology or software. In order to illustrate the methodology a case study is provided.

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Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Stochastic Transportation Problem with Gamma Distribution

Chaini

Ranarahu

2023

IJFIS

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“…Although the BP algorithm can effectively balance the needs of decisionmakers at different levels, it has limitations in representing uncertain parameters in the planning system. Therefore, different types of fuzzy numbers, interval numbers and BP linear programming can be combined to address the uncertainty in the system, and a suitable algorithm can be used to convert the fuzzy number and interval number into clear values and obtain an effective optimization plan (Ma et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Approach for Bi-Level Programming Problems

Bai¹,

Jin²,

Wei³

2022

J ENVIRON INFORM LET

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Bi-level programming problem (BLPP) can make the final decision result is a mutually coordinated scheme among all layers, which can solve complex practical problems well. This paper summarizes and describes the methods to solve the bi-level programming problem, and proposes a fuzzy method to solve the BLPP by using the concepts of membership function and multiobjective optimization to solve the bilevel programming problem, and gives a numerical example to solve it. It also points out that developing a method to solve fuzzy problems and new concepts to solve multi-level programming problems are practical directions for future research.

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“…In recent years, considering that the single uncertain optimization method has its applicable conditions and advantages and disadvantages in solving practical problems so that it cannot deal with complex uncertain systems, the coupling of uncertain optimization methods to solve complex uncertain problems was successfully applied in many fields, such as water resource management, − waste management, − water quality management, , end-of-life vehicle management, , air quality management, − energy system management, , and so on. The coupling of uncertain optimization methods not only integrates the advantages of these methods but also can achieve a certain degree of complementary among their limitations.…”

Section: Introductionmentioning

confidence: 99%

Economic and Flexible Design under Uncertainty for Steam Power Systems Based on Interval Two-Stage Stochastic Programming

Niu

Yin

Feng

2021

Ind. Eng. Chem. Res.

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Design and optimization of steam power systems (SPS) should not only optimize the economic target of the system but also ensure the safe and stable operation of the production process against complex uncertainties. For the uncertainty and complexity existing in SPS, the study proposes an interval two-stage stochastic programming (ITSP) model by merging two-stage stochastic programming (TSP) and interval-parameter programming (IPP). The approach has the advantages that uncertainties are represented by discrete random variables, interval numbers, and their combination and can be effectively communicated into the optimization processes and resulting solutions; in addition, it incorporates the regulated equipment selection policies directly into its optimization process and can put forward corresponding scheduling and adjustment strategies for various policy scenarios caused by uncertainties. The optimal scheme can be obtained as stable interval solutions and provides ample decision-making space for decision makers, which achieves the comprehensive optimum of economy and flexibility.

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A fuzzy chance-constrained programming model with type 1 and type 2 fuzzy sets for solid waste management under uncertainty

Cited by 24 publications

References 27 publications

Type-2 Fuzzy Stochastic Transportation Problem with Gamma Distribution

Type-2 Fuzzy Stochastic Transportation Problem with Gamma Distribution

Fuzzy Approach for Bi-Level Programming Problems

Economic and Flexible Design under Uncertainty for Steam Power Systems Based on Interval Two-Stage Stochastic Programming

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