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Imprecise multi-objective production/distribution planning decisions using possibilistic programming method

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2013年第1期25卷 219-230页

作者： Liang, Tien-Fu Hsiuping Univ Sci & Technol Dept Ind Engn & Management Taichung 412 Taiwan

This work develops a possibilistic programming method to solve multi-product and multi-time period production/distribution planning decision (PDPD) problems involving imprecise goals, forecast demand and cost/time coefficients in uncertain environments. The proposed method attempts to simultaneously minimize total production and distribution costs and total delivery time in relation to inventory levels, available machine capacity and labor levels at each source, as well as forecast demand and available warehouse space at each destination, and considering the constraint of available budget. An industrial case is used to demonstrate the feasibility of applying the proposed method to real-world PDPD problems. Moreover, several significant management implications regarding the practical application of the proposed method are presented.

关键词： Fuzzy sets supply chains production/distribution planning decisions possibilistic programming

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AN EXTENSION TO possibilistic LINEAR-programming

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FUZZY SETS AND SYSTEMS 1994年第2期64卷 195-206页

作者： JULIEN, B Centre de Recherche Infomatique de Montréal Montréal Québec H3A 2N4 Canada

This paper proposes an extension to the solution of possibilistic linear programming problems with fuzzy number parameters (introduced by Buckley [Fuzzy Sets and Systems 31 (1989) 329-341]). The method relies on alpha-cuts of the fuzzy number parameters to generate a succession of pairs of classical linear programs. The solutions of these linear programs produce a convex possibility distribution of the optimal objective values with the corresponding levels of the decision variables. The approach is illustrated with an imprecise water quality management problem.

关键词： possibilistic programming LINEAR programming DECISION MAKING ENGINEERING WATER MANAGEMENT

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Order allocation and procurement transport planning in apparel supply chain: A utility-based possibilistic-flexible programming approach

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FUZZY SETS AND SYSTEMS 2020年 398卷 1-33页

作者： Yaghin, R. Ghasemy Darvishi, Fateme Amirkabir Univ Technol Dept Text Engn Clothing Engn & Management Grp 424 Hafez Ave Tehran Iran

The purpose of this paper is to study the integrated decisions of the physical supply channel on the global scale including the supplier selection, order allocation, and transportation planning under uncertainty. In detail, this study addresses the supply decision-making problem in a multi-source, multi-product, and multi-mode transportation in accordance with the price discounts. To this end, a multi-objective mixed integer linear programming model is developed to jointly determine the best suppliers, and purchasing quantities along with the number and type of transportation modes. Three important objective functions, i.e., total logistics costs, total late deliveries and total value of purchasing are optimized in our proposed mathematical model. Then, a fuzzy multi-choice goal programming model with the utility function is presented to solve the multi-objective formulation. A novel flexible-possibilistic programming approach is proposed to cope with the epistemic uncertainty and the flexibility of constraints based on the expected value and chance constraint programming concept. Finally, a numerical study in the apparel supply chain is carried out to show the model usefulness and the solution algorithm effectiveness. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Fuzzy mathematical programming Apparel supply chain Procurement-transportation planning Order allocation possibilistic programming Flexible programming

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An application of possibilistic programming to production sequencing of mining parcels

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2017年第2期12卷 79-88页

作者： Kumral, Mustafa Sari, Yuksel Asli McGill Univ Dept Min & Mat Engn 3450 Univ St Montreal PQ Canada

The mineral industries have recently experienced low commodity prices and escalating operational costs. This forces mining companies to find more efficient and productive ways to operate mines. The production sequence of mining blocks significantly affects the profitability of a mining project. Under capacity and access constraints, high quality blocks are forced to be produced during earlier periods to take advantage of the time value of money. However, financial and quality characteristics (e.g. grades, ore and waste tonnages of a block, ore prices) are imprecise at the planning stage. This research aims to address the uncertainties associated with grade and financial parameters so as to obtain a robust solution. Within this scope, the sequencing problem of mining parcels is formulated as a multi-objective optimization problem in fuzzy form, and subsequently is converted to a maximin (maximize the minimum) formulation. This formulation is then solved using a mixed integer programming solver. Two case studies show that the proposed fuzzy possibilistic model can generate an efficient compromise solution while considering uncertainties.

关键词： Mine extraction fuzzy decision-making and optimization possibilistic programming maximin operator

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A possibilistic programming approach to analyze a closed-loop polyethylene tanks supply chain based on decision tree and discounted cash flow

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2020年第2期15卷 106-121页

作者： Shamsi, Farzam Mahdavi, Iraj Paydar, Mohammad Mahdi Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Babol Noshirvani Univ Technol Dept Ind Engn Babol Sar Iran

According to environmental considerations, the traditional supply chain has replaced with a closed-loop supply chain. This study attempts to design the polyethylene tanks closed-loop supply chain network under uncertainty. The proposed network contains various levels such as suppliers, manufacturing plants, distribution warehouses, and retailers. A mixed integer linear programming model is developed with a single-objective, multiple products and multiple periods. Besides, after-sales service is considered that has economic and environmental effects on the supply chain and leads to customers' satisfaction and loyalty. To deal with uncertainty, possibilistic programming is used. Therefore, some parameters of the model are considered as a fuzzy number with a triangular probability distribution. Also, a method based on decision tree and discounted cash flow is utilized to analyze the problem in multiple periods. Furthermore, to validate and show the applicability of the proposed model, a real case study is performed in the polyethylene tanks industry. Finally, the sensitivity analysis is used to check the robustness of the proposed model and framework. The results show that the net present value of the supply chain under new condition is increased by 0.8%. The results of this research can be useful for managers of the organizations.

关键词： Closed-loop supply chain polyethylene tank decision tree discounted cash flow uncertainty possibilistic programming

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Considering the circular economy for designing closed-loop supply chain under hybrid uncertainty: A robust scenario-based possibilistic-stochastic programming

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 238卷

作者： Dehshiri, Seyyed Jalaladdin Hosseini Amiri, Maghsoud Allameh Tabatabai Univ Fac Management & Accounting Dept Ind Management Tehran Iran

Due to the significance of environmental and economic topics and limited resources, integrating Circular Economy (CE) principles is necessary for Supply Chain (SC) to improve sustainable competitive advantage. The integration of CE in SCs leads to a Closed-Loop Supply Chain Network Design (CLSCND) and a circular SC to achieve the economic, environmental, and social aspects of outputs and processes. On the other hand, the integration of CE in CLSCND faces hybrid uncertainties in different time horizons and scenarios due to the strategic and long-term nature of decisions. Due to the impact of random and cognitive uncertainties in the long term, it is necessary to consider these factors in the integration of CE in CLSCND. So, the aim of the present study is CLSCND to achieve the principles of CE and provide a robust scenario-based possibilistic-stochastic programming approach to consider cognitive and random uncertainties simultaneously. The contributions and innovations of the present study are the integration of CE in CLSCND, considering cognitive and random uncertainties at the same time, developing the Me criterion to achieve flexible solutions based on the convex combination of opinions of experts, and proposing the use of the absolute possible deviation to consider the possibilistic deviation. A case study was investigated for CLSCND in the paper industry to assess the presented approach, and the results indicated the accuracy and robustness of the solutions. The numerical simulation results demonstrated the appropriate performance of the proposed method, that the lowest average and standard deviation values of the constraints violation were 601 and 48, respectively, in the developed approach. Analytical findings show that implementing CE in the paper CLSCND provides insights for managers in demand and capacity constraint violations based on different risk levels. Also, this study offers a comprehensive framework for presenting robustness and flexible solutions i

关键词： Circular economy Mixed integer linear programming possibilistic programming Closed -loop supply chain Stochastic programming Robust optimization

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A New Combination of Robust-possibilistic Mathematical programming for Resilient Supply Chain Network under Disruptions and Uncertainty: A Real Supply Chain

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INTERNATIONAL JOURNAL OF ENGINEERING 2018年第4期31卷 612-621页

作者： Hamidieh, A. Khamseh, A. Arshadi Naderi, B. Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran

Nowadays, the design of a strategic supply chain network under disruption is one of the most important priorities of the governments. One of the strategic purposes of managers is to supply the sustainable agricultural products and food in stable conditions which require the production of soil nutrients. In this regard, some disruptions such as sanctions and natural disasters have a destructive effect on the supply of raw materials and the uncertainty of input parameters plays an undesirable impact on the decision-making levels including strategic, tactical, and operational levels. The present study introduced a new model of resilient supply chain network which was compatible with the realities of the structure of the supply chain for fertilizer in Iran. Notably, the effectiveness of the designed system was promoted by the dominant strategies of reliability. Further, a new robust possibilistic approach was proposed which guaranteed the optimality and feasibility robustness through the efficient solution to deal with the parametric uncertainty. Finally, the results showed that the proposed new robust possibilistic combination promoted the optimality robustness and its effectiveness using an optimal average cost and minimum standard deviation.

关键词： Supply Chain possibilistic programming Disruption Robustness Uncertainty

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DESIGNING A SUSTAINABLE CLOSED-LOOP SUPPLY CHAIN USING ROBUST possibilistic-STOCHAS TIC programming IN PENTAGONAL FUZZY NUMBERS

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TRANSPORT 2024年第4期39卷 323-349页

作者： Dehshiri, Seyyed Jalaladdin Hosseini Amiri, Maghsoud Hajiaghaei-keshteli, Mostafa Keshavarz-ghorabaee, Mehdi Zavadskas, Edmundas Kazimieras Antucheviciene, Jurgita Allameh Tabatabai Univ Dept Ind Management Tehran Iran Monterrey Inst Technol & Higher Educ Dept Ind Engn Puebla Mexico Gonbad Kavous Univ Dept Management Gonbad Kavous Iran Vilnius Gediminas Tech Univ Inst Sustainable Construct Vilnius Lithuania Vilnius Gediminas Tech Univ Dept Construct Management & Real Estate Vilnius Lithuania

The lack of information and hybrid uncertainties in Supply Chain (SC) parameters affect managerial decisions. It is inevitable to consider random uncertainty based on fuzzy scenarios and cognitive uncertainty to prehensiveness and accuracy than triangular and trapezoidal fuzzy numbers due to taking into account higher uncertainty, less lack of information, and taking into account maximum subjectivity Decision-Makers (DMs). There is a gap in the literature regarding the use of PFNs in SCLSC problems. This research presents a new model using PFNs to solve deficiencies in stochastic-possibilistic programming. Developing a Robust Stochastic-possibilistic (RSP) based on PFNs under fuzzy scenarios, presenting measures of necessity, possibility, and credibility for making decisions founded on different levels of DMs' risk, and proposing global solutions through providing linear programming models are the main innovations and contributions of the present research. An actual case study evaluates the presented approach to reduce the cost and carbon pollution in the stone paper SC. In the suggested method, trade-offs could be formed between the mean of objective functions and risk by modifying the robustness coefficients. According to the proposed approach, an optimal value of confidence is specified. Additionally, robustness deviations are controlled in the model, which results in more accurate and reliable results. Numerical simulations confirmed the efficacy of the robust approach proposed.

关键词： closed-loop supply chain possibilistic programming fuzzy scenarios robust approach sustainable design stochastic programming

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A hybrid fuzzy-stochastic multi-criteria ABC inventory classification using possibilistic chance-constrained programming

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SOFT COMPUTING 2021年第2期25卷 1065-1083页

作者： Razavi Hajiagha, Seyed Hossein Daneshvar, Maryam Antucheviciene, Jurgita Khatam Univ Fac Management & Finance Dept Management Tehran Iran Vilnius Gediminas Tech Univ Dept Construct Management & Real Estate Sauletekio Ave 11 LT-10223 Vilnius Lithuania

Inventory classification is a fundamental issue in the development of inventory policy that assigns each inventory item to several classes with different levels of importance. This classification is the main determinant of a suitable inventory control policy of inventory classes. Therefore, a great deal of research is done on solving this problem. Usually, the problem of inventory classification is considered in a multi-criteria and uncertain environment. The proposed method in this paper inspired by the notion of heterogeneous decision-making problems in which decision-makers deal with different types of data. To this aim, a mathematical modeling-based approach is proposed considering different types of uncertainty in classification information. Demand information is considered to be stochastic due to its time-varying nature and cost information is considered to be fuzzy due to its cognitive ambiguity. A hybrid algorithm based on chance-constrained and possibilistic programming is proposed to solve the problems. Considering the stochastic nature of demand information, solving the proposed model using the hybrid algorithm, the classification of items to three classes of extremely important, class A, moderately important, class B, and relatively unimportant, class C, items are determined along with a minimum inventory level required to deal with the stochasticity of demands information. The proposed approach is applied to a case study of classifying 51 inventory items. The obtained results assigned 22%, 39%, and 39% of the items to A, B, and C classes, respectively.

关键词： Multi-criteria inventory classification Chance-constrained programming possibilistic programming

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The Flexible possibilistic-Robust Mathematical programming Approach for the Resilient Supply Chain Network: An Operational Plan

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JOURNAL OF ADVANCED MANUFACTURING SYSTEMS 2021年第3期20卷 473-498页

作者： Hamidieh, Alireza Arshadikhamseh, Alireza Payamnoor Univ Engn Dept Tehran Iran Kharazmi Univ Engn Dept Tehran Iran

Nowadays, the management of soil resources for global food security is one of the strategic purposes of the countries. In this regard, creating soil nutrition's supply chain network for the fertility of agricultural fields is one of the most critical priorities of the government. In the above strategic network, we are faced with uncertain parameters that show a complex behavior under time, geography, and market changes. This parametric uncertainty is aggravated by the occurrence of disruptions such as sanctions and natural disasters, with devastating effects on strategic, tactical, and operational decisions. In this paper, by adopting a set of reactive and preventive strategies, the resilient supply chain network of dependent petrochemical industries is presented, consistent with the structure of its production cycle in Iran. To cope with the uncertainty, a robust programming approach was extended into flexible-possibilistic programming. The final model controls the uncertainty and risk-aversion of output decisions and effectively confronts the adverse effects of disruptions. Finally, using a real industrial study, the effectiveness and applicability of the final model are explained. Furthermore, by providing efficient solutions, mean cost and minimum standard deviations were achieved.

关键词： Supply chain robust resilient possibilistic programming uncertainty

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