作者：
Inuiguchi, MasahiroOsaka Univ
Grad Sch Engn Sci Dept Syst Innovat Div Math Sci Social Syst Toyonaka Osaka 5608531 Japan
In this paper, we treat fuzzy linearprogramming problems with uncertain parameters whose ranges are specified as fuzzy polytopes. The problem is formulated as a necessity measure optimization model. It is shown that ...
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In this paper, we treat fuzzy linearprogramming problems with uncertain parameters whose ranges are specified as fuzzy polytopes. The problem is formulated as a necessity measure optimization model. It is shown that the problem can be reduced to a semi-infinite programming problem and solved by a combination of a bisection method and a relaxation procedure. An algorithm in which the bisection method and the relaxation procedure converge simultaneously is proposed. A simple numerical example is given to illustrate the solution procedure. (C) 2007 Elsevier B.V. All rights reserved.
This work presents a possibilistic linear programming (PLP) method for solving the integrated manufacturing/distribution planning decision (MDPD) problems with multiple imprecise goals in supply chains under an uncert...
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This work presents a possibilistic linear programming (PLP) method for solving the integrated manufacturing/distribution planning decision (MDPD) problems with multiple imprecise goals in supply chains under an uncertain environment. The imprecise PLP model designed here aims to simultaneously minimize total net costs and total delivery time with reference to available supply, capacities, labor levels, quota flexibility and cost budget constraints at each source, as well as forecast demand and warehouse space at each destination. The proposed method achieves greater computational efficiency by employing the simplified triangular distribution to represent imprecise numbers. An industrial case is used to demonstrate the feasibility of applying the proposed method to a real MDPD problem. Overall, the proposed PLP method provides a practical means of solving the multi-objective MDPD problems in an uncertain environment, and can effectively improve manufacturer/ distributor relationships in a supply chain.
This work uses new and recoverable materials to mix into the remanufacturing systems. It implements a possibilistic linear programming model to provide aid in making production decisions in recyclable remanufacturing ...
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This work uses new and recoverable materials to mix into the remanufacturing systems. It implements a possibilistic linear programming model to provide aid in making production decisions in recyclable remanufacturing systems subject to a fuzzy environment which includes multi-component, multi-vendor, multi-source and multi-machine factors. When the related parameters are imprecise in the systems, both the trapezoidal and triangular possibility distributions are used. This work applies fuzzy theory and possibility linearprogramming to set the objective function, demand and CO_2 emission uncertainty. We propose a decision making model to consider the procurement of raw materials, new materials and recycled materials mixed into the remanufacturing systems with reference to the restrictions of supplier/vendor capacity, CO_2 emissions and machine yield in order to minimize total cost, lead-time and CO_2 emissions. In addition, we propose a procedure for solving the fuzzy objective functions and fuzzy constraints. To test the adequacy of model, we use a numerical example from a real case study. The results and suggestions can provide a feasible situation for production planning.
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