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Integrated possibilistic linear programming with Beta-Skewness Degree for a Fuzzy Multi-Objective Aggregate Production Planning Problem Under Uncertain Environments

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FUZZY INFORMATION AND ENGINEERING 2020年第3期12卷 355-380页

作者： Sutthibutr, Noppasorn Chiadamrong, Navee Thammasat Univ Sch Mfg Syst & Mech Engn Pathum Thani Thailand

This study proposes an improved Fuzzy programming (FP) approach to optimise multi-objective Aggregate Production Planning (APP) problem under uncertain environments. The proposed approach integrates the concept of possibilistic linear programming (PLP) with Beta-Skewness Degree that decision-makers can manipulate the best level of data fuzziness as well as maintain such fuzziness in the optimisation process (by not turning it to deterministic data too early). The effectiveness of the proposed approach is demonstrated through a case study by minimising the highest overall deviation from the ideal solution of total costs under imprecise operating costs, customer demand, labour level, and machine capacity. Our comparative result clearly shows that the obtained solution outperforms the solutions from traditional defuzzification methods. The proposed approach also helps decision-makers not only to know and optimise the most likely situation, but also realise the outcomes in the optimistic and the pessimistic business situations so that decision makers can prepare and take necessary actions for future uncertainty.

关键词： Aggregate Production Planning possibilistic linear programming defuzzification methods Beta-Skewness Degree Fuzzy programming

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Supply Chain for Perishable Agriculture Products by possibilistic linear programming 6

Supply Chain for Perishable Agriculture Products by Possibil...

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6th IEEE International Conference on Industrial Engineering and Applications (ICIEA)

作者： Jarernsuk, Saran Phruksaphanrat, Busaba Thammasat Univ Fac Engn ISO RU Dept Ind Engn Pathum Thani Thailand

ISBN: (纸本)9781728108513

This paper proposes mathematical programming models for supply chain of perishable products using possibilistic linear programming (PLP) and Preemptive possibilistic linear programming (PPLP) models. The possible ranges of unit price and unit costs were used to increase flexibility of obtaining the better supply chain imprecise information. Three crisp objective functions were generated, which are maximizing the most possible value of profit, minimizing the risk of obtaining the lower profit and maximizing the opportunity of obtaining the higher profit. The objective functions of the presented model looked for the maximum total profit with low risk in getting the lower profit and higher opportunity to get the better profit. Product shelf-life, time of all processes and quantity of products were determined. The numerical experiment was illustrated for an aromatic coconut supply chain. PPLP model is easier to select the preferred solution than PLP model. These models can find the effective solution for the problem.

关键词： component perishable product supply chain possibilistic linear programming preemptive priority

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Combining possibilistic linear programming and fuzzy AHP for solving the multi-objective capacitated multi-facility location problem

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INFORMATION SCIENCES 2014年 268卷 185-201页

作者： Ozgen, Dogan Gulsun, Bahadir Yildiz Tech Univ Mech Fac Dept Ind Engn TR-34349 Istanbul Turkey

The capacitated multi-facility location problem is a complex and imprecise decision-making problem which contains both quantitative and qualitative factors. In the literature, many objectives for optimizing many types of logistics networks are described: (i) minimization objectives such as cost, inventory, transportation time, environmental impact, financial risk and (ii) maximization objectives such as profit, customer satisfaction, and flexibility and robustness. However, only a few papers have considered quantitative and qualitative factors together with imprecise methodologies. Unlike traditional cost-based optimization techniques, the approach proposed here evaluates these factors together while considering various viewpoints. Decision-makers must deal both factors together to model complex structure of real-world applications. In this paper, a two-phase possibilistic linear programming approach and a fuzzy analytical hierarchical process approach have been combined to optimize two objective functions ("minimum cost" and "maximum qualitative factors benefit") in a four-stage (suppliers, plants, distribution centers, customers) supply chain network in the presence of vagueness. The results and findings of this method are illustrated with a numerical example, and the advantages of this methodology are discussed in the conclusion. (C) 2014 Elsevier Inc. All rights reserved.

关键词： possibilistic linear programming Fuzzy AHP Supply chain network design Capacitated multi-facility location problem Multi-objective programming

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A possibilistic linear programming model for supply chain network design under uncertainty

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IMA JOURNAL OF MANAGEMENT MATHEMATICS 2013年第2期24卷 209-229页

作者： Bouzembrak, Y. Allaoui, H. Goncalves, G. Bouchriha, H. Baklouti, M. Univ Lille Nord France F-59000 Lille France LGI2A UArtois F-62400 Bethune France Natl Sch Engineers Tunis Dept Ind Engn Tunis 1002 Tunisia

In this paper, we develop a possibilistic linear programming model for supply chain network design with imprecise inputs: market demands, supplied quantities, transportation costs, opening costs, treatment and storage costs are modelled as fuzzy numbers. An efficient possibilistic linear programme is constructed with fuzzy objective function that minimizes the sum of investment costs and operating costs of the supply chain. A method for solving the programming problem with fuzzy parameters is proposed. Application to a supply chain problem at a European textile company illustrates our methodology. Numerical results show that the performance of the proposed model in handling data uncertainty is better when compared to a classical deterministic model.

关键词： supply chain network design possibilistic linear programming fuzzy number uncertainty

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A possibilistic mathematical programming model to control the flow of relief commodities in humanitarian supply chains

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 157卷 107305-107305页

作者： Ismail, Israa Istanbul Tech Univ Dept Ind Engn TR-34367 Istanbul Turkey

In emergency situations, disaster relief organizations are faced with the difficult decision of how to allocate scarce resources in an efficient manner in order to provide the best possible relief action. This paper aims to provide an analytical model that will help relief organizations in reducing human suffering following a disaster while maintaining an acceptable level of cost efficiency. A mathematical model is introduced to optimize the relief distribution problem which considers the social cost -the total sum of logistics and deprivation costs. The fuzzy nature of the deprivation cost function is addressed with possibilistic mixed integer programming with fuzzy objectives to reflect variation in deprivation costs perceptions. The model is solved using the Rolling Horizon method in a sequence of iterations. In each iteration, part of the planning horizon is modeled in detail and the rest of the time horizon is represented in an aggregated manner. The model is tested both empirically and on a case study of internal displacement in northwest Syria. Computational results showed that considering the demographic structure in affected areas and reflecting it to the deprivation cost function helped to reach better prioritization in distribution of commodities. The rolling horizon methodology is also found to be efficient in solving large scale instances and in capturing the dynamic changes in demand and supply parameters.

关键词： Humanitarian logistics possibilistic linear programming Rolling horizon Deprivation cost Inventory allocation

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possibilistic linear programming Using General Necessity Measures Preserves the linearity

Possibilistic Linear Programming Using General Necessity Mea...

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8th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2011)

作者： Inuiguchi, Masahiro Osaka Univ Grad Sch Engn Sci Toyonaka Osaka 5608531 Japan

ISBN: (纸本)9783642225888

In this paper, a robust optimization approach to possibilistic linear programming problems is studied. After necessity measures and generation processes of logical connectives are reviewed, the necessity tractile optimization model of possibilistic linear programming problem is introduced as a robust optimization model. This problem is reduced to a linear semi-infinite programming problem. Assuming the convexity of the right parts of membership functions of fuzzy coefficients and the concavity of membership functions of fuzzy constraints, we investigate conditions on logical connectives for the problems to be reduced to linear programming problems. Several examples are given to demonstrate that necessity tractile optimization models are often reduced to linear programming problems.

关键词： possibilistic linear programming necessity measure implication function conjunction function

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Lot Sizing for Recoverable Remanufacturing Decisions with Cost, Lead Time and Carbon Dioxide Emissions

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管理与系统 2018年第4期25卷 457-491页

作者：蘇泰盛吳靖純黃立雯

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 linear programming 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.

关键词： Remanufacturing Systems Fuzzy Theory possibilistic linear programming Lead-Time CO_2 Emissions

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possibilistic linear-programming approach for supply chain networking decisions

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2011年第3期209卷 253-264页

作者： Kabak, Ozgur Ulengin, Fusun Istanbul Tech Univ Fac Management Dept Ind Engn TR-80626 Istanbul Turkey Dogus Univ Fac Engn Dept Ind Engn Istanbul Turkey

Supply chain networking decisions are very important for the medium- and long-term planning success of manufacturing companies. The inputs to supply chain planning models are subject to environmental and system uncertainties. In this paper, a fuzzy set theory-based model is proposed to deal with those uncertainties. For this purpose, a possibilistic linear programming (PLP) model is used to make strategic resource-planning decisions using fuzzy demand forecasts and fuzzy yield rates as well as other inputs such as costs and capacities. The objective of the proposed PLP is to maximize the total profit of the enterprise. The model is applied to Mercedes-Benz Turk, one of the largest bus-manufacturing companies in the world, and conclusions and suggestions for further research are provided. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Supply chain management Fuzzy modelling possibilistic linear programming Supply chain strategy Networking

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An improvement in possibilistic linear programming approach for imprecise production/transportation planning decisions

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JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2009年第5期30卷 995-1017页

作者： Cheng, Hung-Wen Hsiuping Inst Technol Dept Ind Engn & Management 11 Gungye Rd Taichung 412 Taiwan

In many existing applications of possibilistic linear programming (PLP) model, the decision makers (DM) are asked to specify the positive ideal solution (PIS) and negative ideal solution (NIS) for defining the linear membership function proposed by Zimmermann. In practice, this is a real difficulty for an ordinary DM without possessing acute sense of arithmetic operation and adequate comprehension of PLP model. Furthermore, this task is obviously subjective and will influence the search route of the solution process via the linear membership functions. This study provides an objective method to specify appropriate PIS and NIS for defining efficient linear membership functions so as to rapidly approach a better compromise solution, and also presents a simple way for further improvement of the compromise solution to reach a satisfactory solution. The proposed approaches aim to facilitate the decision making process and promote computational efficiency as well as improve the compromise solution for the PLP method. Additionally, an integrated production/transportation planning decision (PTPD) problem is used to demonstrate the feasibility of applying the proposed methods to real industrial case. Not only the simplicity and objectivity in specifying PIS and NIS but also the computational efficiency, the results of this study show definite advantage of the proposed methods in solving PLP problem.

关键词： possibilistic linear programming multi-objective linear programming integrated production/transportation planning decision fuzzy linear programming

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Integrated manufacturing/distribution planning decisions with multiple imprecise goals in an uncertain environment

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QUALITY & QUANTITY 2012年第1期46卷 137-153页

作者： Liang, Tien-Fu Hsiuping Inst Technol Dept Ind Engn & Management Dali City 412 Taichung Taiwan

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.

关键词： Manufacturing/distribution planning decisions possibilistic linear programming Supply chains Fuzzy set Multi-objective linear programming

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