Determining optimal pollution control policies: An application of bilevel programming

Amouzegar, Mahyar A.; Moshirvaziri, Khosrow

doi:10.1016/s0377-2217(98)00336-1

Cited by 65 publications

(22 citation statements)

References 9 publications

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“…Let the feasible set of control vector x be defined by the chance constraint, Cr {a (x, ~) < 0} > n0, (9) where/~0 is a confidence level at which it is desired that the fuzzy constraints hold. Then, for each decision x chosen by the leader, the feasible set of control vector Yi of the ith follower should be dependent on not only x but also Yl,-.., Y~-I, Yi+l,..., Y,~, and generally represented by the chance constraints,…”

Section: Chance-constrained Multilevel Programmingmentioning

confidence: 99%

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Fuzzy multilevel programming with a hybrid intelligent algorithm

Gao

Liu

2005

Computers & Mathematics with Applications

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In order to model fuzzy decentralized decision-making problem, fuzzy expected value multilevel programming and chance-constrained multilevel programming are introduced. Furthermore, fuzzy simulation, neural network, and genetic algorithm are integrated to produce a hybrid intelligent algorithm for finding the Stackelberg-Nash equilibrium. Finally, two numerical examples are provided to illustrate the effectiveness of the hybrid intelligent algorithm. (~)

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Section: Chance-constrained Multilevel Programmingmentioning

confidence: 99%

“…In literature, most applications of multilevel programming are in the economics area, particularly the central economic planning [9][10][11][12]. But in practice, resources, costs, demands, and many other elements are often subject to fluctuations and difficult to measure.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy multilevel programming with a hybrid intelligent algorithm

Gao

Liu

2005

Computers & Mathematics with Applications

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“…France favors incineration, meanwhile Germany goes for recycling (Bloemhof-Ruwaard et al [1996]). An example of applications of Game Theory in Legislation is found in Amouzegar and Moshirvaziri [1999].…”

Section: Economic Parametersmentioning

confidence: 99%

Designing and evaluating sustainable logistics networks

Neto

Bloemhof-Ruwaard

Nunen

et al. 2008

International Journal of Production Economics

340

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AND KEYWORDS AbstractThe objective in this paper is to shed light into the design of logistic networks balancing profit and the environment. More specifically we intend to i) determine the main factors influencing environmental performance and costs in logistic networks ii) present a comprehensive framework and mathematical formulation, based on multiobjective programming, integrating all relevant variables in order to explore efficient logistic network configurations iii) present the expected computational results of such formulation and iv) introduce a technique to evaluate the efficiency of existing logistic networks.The European Pulp and Paper Industry will be used to illustrate our findings.

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“…In the context of BLPP, the decisions maintain a hierarchy from leader to the follower. BLPPs have been successfully applied to various hierarchical decision making situations such as traffic planning [3], pricing and fare optimization in the airline industry [4], management of hazardous materials [5], aluminum production process [6], pollution control policy determination [7], tax credits determination for biofuel producers [8], pricing in competitive electricity markets [9], supply chain planning [10], facility location [11], defense problem [12] and so forth. Most of the developments on BLPPs are based on vertex enumeration method [1] and transformation approaches [2] which are effective only for very simple types of problems.…”

Section: Introductionmentioning

confidence: 99%

A new methodological development for solving linear bilevel integer programming problems in hybrid fuzzy environment

Biswas¹,

Kumar²

2016

ntmsci

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This paper deals with fuzzy goal programming approach to solve fuzzy linear bilevel integer programming problems with fuzzy probabilistic constraints following Pareto distribution and Frechet distribution. In the proposed approach a new chance constrained programming methodology is developed from the view point of managing those probabilistic constraints in a hybrid fuzzy environment. A method of defuzzification of fuzzy numbers using α−cut has been adopted to reduce the problem into a linear bilevel integer programming problem. The individual optimal value of the objective of each DM is found in isolation to construct the fuzzy membership goals. Finally, fuzzy goal programming approach is used to achieve maximum degree of each of the membership goals by minimizing under deviational variables in the decision making environment. To demonstrate the efficiency of the proposed approach, a numerical example is provided.

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Determining optimal pollution control policies: An application of bilevel programming

Cited by 65 publications

References 9 publications

Fuzzy multilevel programming with a hybrid intelligent algorithm

Fuzzy multilevel programming with a hybrid intelligent algorithm

Designing and evaluating sustainable logistics networks

A new methodological development for solving linear bilevel integer programming problems in hybrid fuzzy environment

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