Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are ...
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Aggregate production planning (APP) addresses matching supply to forecast demand, with varying customer orders over the intermediate planning horizon. In real-world APP problems, input data and related parameters are commonly imprecise because information is incomplete or unavailable, and the decision maker (DM) must simultaneously consider conflicting objectives. This study develops an interactive possibilistic linear programming (i-PLP) approach to solve multi-product and multi-time period APP problems with multiple imprecise objectives and cost coefficients by triangular possibility distributions in uncertain environments. The imprecise multi-objective APP model designed here seeks to minimise total production costs and changes in work-force level with reference to imprecise demand, cost coefficients, available resources and capacity. Additionally, the proposed i-PLP approach provides a systematic framework that helps the decision-making process to solve fuzzy multi-objective APP problems, enabling a DM to interactively modify the imprecise data and parameters until a set of satisfactory solutions is derived. An industrial case demonstrates the feasibility of applying the proposed approach to a practical multi-objective APP problem.
The goal and model inputs when applies any of conventional techniques to APP decisions are generally assumed to be deterministic/crisp, and the major limitation of these techniques is that they do not consider the tim...
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The goal and model inputs when applies any of conventional techniques to APP decisions are generally assumed to be deterministic/crisp, and the major limitation of these techniques is that they do not consider the time value of money for each of operating cost categories. This work presents an interactive possibilistic linear programming (PLP) method for the solving the aggregate production planning (APP) problems with imprecise goal, forecast demand, related capacities and operating costs in uncertain environments. The imprecise APP model designed here attempts to minimize total manufacturing costs with reference to inventory, labor, overtime, subcontracting and backordering levels, machine capacity, warehouse space, and the time value of money for each of the operating cost categories. The proposed PLP method achieves greater computational efficiency by employing the simplified triangular distribution to specify imprecise goal and related coefficients. The analytical results obtained by implementing a real industrial case indicate that the proposed PLP method is practically applicable for solving the practical APP problems in uncertain environments.
This study develops an interactive possibilistic linear programming (PLP) method for solving the integrated production/transportation planning decision (PTPD) problem with imprecise objective and constraints in an unc...
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This study develops an interactive possibilistic linear programming (PLP) method for solving the integrated production/transportation planning decision (PTPD) problem with imprecise objective and constraints in an uncertain environment. The proposed PLP method attempts to minimize the total net costs with reference to available supply, machine capacity, labor level and budget constraints at each source, as well as forecast demand and warehouse space at each destination. A systematic framework that facilitates the fuzzy decision-making process is designed;enabling a decision maker to interactively modify the imprecise data and related parameters until obtain a satisfactory solution. Additionally, an industrial case is used to demonstrate the feasibility of applying the proposed method to real PTPD problems and several significant features of the proposed method that distinguish it from the existing programming methods are presented. On the whole, the proposed PLP method provides greater computational efficiency and more flexible doctrines, and can effectively integrate producer/distributor relationships within a supply chain.
In this paper, possibilistic linear programming problems are investigated. After reviewing relations among conjunction and implication functions, necessity fractile optimization models with various implication functio...
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ISBN:
(纸本)9789899507968
In this paper, possibilistic linear programming problems are investigated. After reviewing relations among conjunction and implication functions, necessity fractile optimization models with various implication functions are applied to the possibilisticlinear problems. We show that the necessity fractile optimization models are reduced to semi-infinite linearprogramming problems. A simple numerical example is given to demonstrate the correctness of the result. The paper is concluded with some remarks for further developments.
This work presents a novel interactive possibilistic linear programming (PLP) approach for solving the multi-product aggregate production planning (APP) problem with imprecise forecast demand, related operating costs,...
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This work presents a novel interactive possibilistic linear programming (PLP) approach for solving the multi-product aggregate production planning (APP) problem with imprecise forecast demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. The proposed approach uses the strategy of simultaneously minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining lower total costs, and minimizing the risk of obtaining higher total costs. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed PLP approach yields an efficient APP compromise solution and overall degree of decision maker (DM) satisfaction with determined goal values. Particularly, several significant management implications and characteristics of the proposed PLP approach that distinguish it from the other APP decision models are presented. (c) 2004 Elsevier B.V. All rights reserved.
In real-world project management (PM) decision problems, input data and/or related parameters are frequently imprecise/fuzzy over the planning horizon owing to incomplete or unavailable information, and the decision m...
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In real-world project management (PM) decision problems, input data and/or related parameters are frequently imprecise/fuzzy over the planning horizon owing to incomplete or unavailable information, and the decision maker (DM) generally faces a fuzzy multi-objective PM decision problem in uncertain environments. This work focuses on the application of fuzzy sets to solve fuzzy multi-objective PM decision problems. The proposed possibilistic linear programming (PLP) approach attempts to simultaneously minimise total project costs and completion time with reference to direct costs, indirect costs, relevant activities times and costs, and budget constraints. An industrial case illustrates the feasibility of applying the proposed PLP approach to practical PM decisions. The main advantage of the proposed approach is that the DM may adjust the search direction during the solution procedure, until the efficient solution satisfies the DM's preferences and is considered to be the preferred satisfactory solution. In particular, computational methodology developed in this work can easily be extended to any other situations and can handle the realistic PM decision problems with simplified triangular possibility distributions.
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.
A production management system contains many imprecise natures. The conventional deterministic and/or stochastic model in a computer integrated production management system (CIPMS) may not capture the imprecise nature...
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A production management system contains many imprecise natures. The conventional deterministic and/or stochastic model in a computer integrated production management system (CIPMS) may not capture the imprecise natures well. This study examines how the imprecise natures in the CIPMS affect the planning results. possibilistic linear programming models are also proposed for the aggregate production planning problem with imprecise natures. The proposed model can adequately describe the imprecise natures in a production system and, in doing so, the CIPMS can adapt to a variety of non-crisp properties in an actual system. For comparison, the classic aggregate production planning problem given by Holt, Modigliani, and Simon (HMS) is solved using the proposed possibilistic model and the crisp model of Hanssmann and Hess (HH). Perturbing the cost coefficients and the demand allows one to simulate the imprecise natures of a real world and evaluate the effect of the imprecise natures to production plans by both the possibilistic and the crisp HH approaches. Experimental results indicate that the possibilistic model does provide better plans that can tolerate a higher spectrum of imprecise properties than those obtained by the crisp HH model.
In this paper, several kinds of possibility distributions of fuzzy variables are studied in possibilistic linear programming problems to reflect the inherent fuzziness in fuzzy decision problems. Interval and triangul...
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In this paper, several kinds of possibility distributions of fuzzy variables are studied in possibilistic linear programming problems to reflect the inherent fuzziness in fuzzy decision problems. Interval and triangular possibility distributions are used to express the non-interactive cases between the fuzzy decision variables, and exponential possibility distributions are used to represent the interrelated cases. possibilistic linear programming problems based on exponential possibility distributions become non-linear optimization problems. In order to solve optimization problems easily, algorithms for obtaining center vectors and distribution matrices in sequence are proposed. By the proposed algorithms, the possibility distribution of fuzzy decision variables can be obtained. (C) 2000 Elsevier Science B.V. All rights reserved.
A system of linear constraints in the form of inequalities with possibilistic coefficients is investigated. A method is obtained for constructing a deterministic system of linear constraints that is equivalent to the ...
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A system of linear constraints in the form of inequalities with possibilistic coefficients is investigated. A method is obtained for constructing a deterministic system of linear constraints that is equivalent to the original system. The results obtained are illustrated with a model example.
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