linear bi-level programming (LBLP) is a useful tool for modeling decentralized decision-making problems. It has two-level (upper-level and lower-level) objectives. Many studies have shown that the LBLP problem is NP-h...
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linear bi-level programming (LBLP) is a useful tool for modeling decentralized decision-making problems. It has two-level (upper-level and lower-level) objectives. Many studies have shown that the LBLP problem is NP-hard, meaning it is difficult to find a global solution in polynomial time. In this paper, we present a novel cognitively inspired computing method based on the state transition algorithm (STA) to obtain an approximate optimal solution for the LBLP problem in polynomial time. The proposed method is applied to a supply chain model that fits the definition of an LBLP problem. The experimental results indicate that the proposed method is more efficient in terms of solution accuracy through a comparison to other metaheuristic-based methods using four problems from the literature in addition to the supply chain distribution model. In this study, a cognitively inspired STA-based method was proposed for the LBLP problem. In the future, we expect to extent the proposed method for linear multi-levelprogramming problems.
In this paper, we consider a class of linear bi-level programming with random fuzzy coefficients, which has no mathematical meaning because of the uncertain factors. So in order to make it solvable, we introduced the ...
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ISBN:
(纸本)9783662472415
In this paper, we consider a class of linear bi-level programming with random fuzzy coefficients, which has no mathematical meaning because of the uncertain factors. So in order to make it solvable, we introduced the linear chance constrained bi-level model. And some theorems are proposed to obtain the equivalent model. Then we employ the interactive programming technique to deal with the bi-level equivalent model. At last an illustrative example is present to show the efficiency.
In this paper, we consider a class of linear bi-level programming with random fuzzy coefficients, which has no mathematical meaning because of the uncertain factors. In order to make it solvable, we introduced the lin...
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ISBN:
(纸本)9781479953721
In this paper, we consider a class of linear bi-level programming with random fuzzy coefficients, which has no mathematical meaning because of the uncertain factors. In order to make it solvable, we introduced the linearbi-level model with expected objectives and chance constraints, and propose some theorems to obtain the equivalent models. Then we employ the interactive programming technique to deal with the bi-level equivalent model. And finally we present an illustrative example to show the efficiency of the models and approaches.
A home supplies manufacturer manufactures many products and each requires workers with different skills. The manufacturer invites a contracting company to supply workers with different skills for each phase of the pro...
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A home supplies manufacturer manufactures many products and each requires workers with different skills. The manufacturer invites a contracting company to supply workers with different skills for each phase of the production process. The problem becomes a production and work force assignment problem, which can be considered as a bi-levelprogramming problem. The supplier, as the upper decision maker, aims to achieve the objective of maximizing gross revenue by making decisions concerning production levels. The contracting company, as the lower-level decision maker, regards the target to be maximizing profit by making decisions concerning the number of assigned workers. There are uncertainties during the production process and therefore the problem has random fuzzy coefficients. To deal with the uncertainties, a general linearbi-level model with random fuzzy variables is introduced and several properties and crisp equivalents are proposed. Then an interactive programming method is applied to deal with the derived expected bi-levelprogramming problem;after several iterations, the interactive solutions converge to the optimal one. Lastly, a numerical example is also presented to demonstrate the proposed optimization methods.
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