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A robust possibilistic programming approach for selecting a clustering-based index tracking portfolio

EXPERT SYSTEMS WITH APPLICATIONS 2025年 279卷

作者： Talaei, Mohammad Torshizi, Ehsan Islamic Azad Univ Coll Engn Sci & Res Branch Ind Engn Grp Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

Traditional mathematical index tracking models increase computational complexity and are not suitable for different capital market conditions. To overcome the problems related to the estimation of stock market asset returns, this study proposes a methodology based on the clustering optimization model of index tracking for Tehran Stock Exchange. The proposed model is developed by imposing turnover and buy-in threshold constraints to provide investment conditions. Then, to further converge the problem towards real conditions, the optimization model is implemented with the approaches of possibilistic chance-constrained programming and robust possibilistic programming to deal with the uncertainty of the model parameters. The parameters of portfolio size and correlation coefficients between pairs of stocks are uncertain, and fuzzy and robust uncertainty frameworks are used to control them. The Pearson's correlation coefficient is used as a similarity operator for the index tracking problem. A real-world case study using Tehran Stock Exchange data is conducted to verify the applicability of the proposed methodology. Several sensitivity analyses have been performed on the optimization model results to prove its theoretical and operational capabilities. These analyses include comparing the proposed model with other similar models, examining the impact of key parameters on the results, evaluating the model under different capital market conditions, and demonstrating the model's performance in certain and uncertain situations. Finally, Based on the findings, the advantages and achievements of the methodology are stated.

关键词： Index tracking Clustering possibilistic programming Chance constraint Robust optimization

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Joint Multi-item Multi-supplier Sustainable Lot-Sizing Model Applying Combined BWM, TOPSIS, possibilistic programming, and e-constraint Method

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2024年 71卷 4530-4547页

作者： Shaw, Krishnendu Lahri, Vijay Shankar, Ravi Ishizaka, Alessio Indian Inst Technol Dhanbad Dept Management Studies Dhanbad 826001 Bihar India Uttaranchal Univ Uttaranchal Inst Management Dehra Dun 248001 Uttarakhand India Indian Inst Technol Delhi Dept Management Studies New Delhi 110016 India NEOMA Business Sch F-76130 Mont St Aignan France

Procurement decisions are critical in sustainable supply chain (SC) transformation. Until now, mainly deterministic supplier selection and lot-sizingmodels have been suggested. Moreover, it is not easy to make a SC sustainable solely by focusing on carbon footprint. Other sustainability factors, such as social, water, andwaste management need to be considered. Therefore, the present study proposed a three-stage multiobjective, multisupplier, multiperiod joint supplier selection, and lot-sizing model, considering carbon footprint, water footprint, solid and liquid waste, and use of recycled materials in a stochastic environment. The model optimizes three objectives (cost, carbon emission, and social sustainability). The model considers different model parameters uncertain, such as costs, emission, solid waste, liquidwaste, recycled material, quality rejection, and capacity. The present study suggests quantifying social sustainability and further using it in the lot-sizing model. The study has applied three stages. First, best-worst method and TOPSIS are used to assess suppliers' social scores. Second, the proposed possibilistic lot-sizing model uses the suppliers' social scores. In the third stage, the epsilon-constraint approach is used to generate tradeoff curves. The model generates optimal solutions for various levels of uncertainty, which are utilized to develop cost, emission, and social tradeoff curves.

关键词： Best-worst method (BWM) carbon footprint possibilistic programming social sustainability TOPSIS epsilon-constraint

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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年第PartA期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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Sustainable supply chain network design problem: Using the integrated BWM, TOPSIS, possibilistic programming, and ε-constrained methods

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114373-114373页

作者： Lahri, Vijay Shaw, Krishnendu Ishizaka, Alessio Indian Sch Mines Indian Inst Technol Dept Management Studies Dhanbad 826004 Bihar India NEOMA Business Sch 1 Rue Marechal JuinBP 215 F-76825 Mont St Aignan France

This paper proposes a two-stage multi-objective possibilistic integer linear programming sustainable supply chain network design model, minimizing the economic, environmental goals and maximizing the social sustainability goals. The proposed model determines the openings of facilities and the amount of flow of goods across the supply chain. It introduces supplier green image factors in the design of the supply chain network. The model has considered epistemic uncertainty to model the unknown capacity, cost, and demand. The proposed study has been carried out in two stages. In the first stage, BWM (Best-Worst method) and TOPSIS are applied to evaluate the green image weights of suppliers. Further, these green weights are being used in the second phase for the supply chain network design. The study has adopted combined possibilistic programming and Epsilon (epsilon) constraint method, which was reported infrequently in the literature. Epsilon (epsilon) constraint method generates distinct Pareto-optimal solutions, which provided a large combination of the trade-off between the cost, emission, and social sustainability. The results facilitate decision-makers to take the decision in an uncertain environment.

关键词： possibilistic programming epsilon-Constraint method Green image Social sustainability Best-Worst method

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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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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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possibilistic programming Approach for Production and Distribution Problem in Milk Supply Chain 3

Possibilistic Programming Approach for Production and Distri...

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3rd IEEE International Conference on Logistics Operations Management (GOL)

作者： Touil, Achraf Echchtabi, Abdelwahed Charkaoui, Abdelkabir Univ Hassan 1st FST Settat Lab Engn Ind Management & Innovat Settat Morocco

ISBN: (纸本)9781467385718

In this paper, a bi-objective possibilistic mixed integer programming model is proposed to deal with the production-distribution problem in a dairy company in Morocco. The model seeks to integrate two conflicting simultaneous objectives: maximizing benefit by considering the shelf life of products and the total cost, including production, storage, and distribution, as well as maximizing the service level, which relates to providing satisfactory services to customers. This is subject to several technological constraints that typically arise in the dairy industry, such as sequence-dependent changeover time, machine speed and storage capacity. Due to highly dynamic and uncertain characteristics related to the aspiration level of objectives, coefficient parameters of objectives and some data, such as process production, transport parameters, and customer demand, an interactive approach is proposed by combining fuzzy goal additive variants and differential evolution metaheuristics to find an efficient compromise solution. Numerical results are reported to demonstrate the efficiency and applicability of the proposed model.

关键词： Dairy industry Production-Distribution problem possibilistic programming Multi-objective Optimization Differential evolution metaheuristic

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A weighted possibilistic programming approach for sustainable vendor selection and order allocation in fuzzy environment

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第5-8期86卷 1785-1804页

作者： Gupta, Pankaj Govindan, Kannan Mehlawat, Mukesh Kumar Kumar, Santosh Univ Delhi Dept Operat Res Delhi India Univ Southern Denmark Dept Technol & Innovat Ctr Sustainable Engn Operat Management DK-5230 Odense M Denmark

This paper focused on the analysis of imprecise information in terms of many critical parameters for a multi-objective multi-item vendor selection-order allocation problem with price-breaks. We used both quantitative and qualitative criteria taking into account the economic, technological, social, environmental factors, and the price-breaks that were offered on order quantity following 'all-unit discount schedule.' We developed an optimization model that integrated fuzzy multi-objective integer linear programming and analytic hierarchy process techniques. A weighted possibilistic programming approach was presented to solve the optimization model, which simultaneously minimized (maximized) the best scenario, the likeliest scenario, and the worst scenario for the imprecise objective functions thereby avoiding situations that may force the solution process to divert. The integrated model and the solution approach were tested on the data set of an industrial case study. A detailed performance analysis and comparisons were done to show superiority of the proposed methodology over the existing related fuzzy programming approaches.

关键词： Sustainable vendor selection Fuzzy decision-making possibilistic programming Multiple-criteria optimization Quantity discounts

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An enhanced possibilistic programming approach for reliable closed-loop supply chain network design

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第5期54卷 1358-1387页

作者： Torabi, S. A. Namdar, J. Hatefi, S. M. Jolai, F. Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Shahrekord Univ Fac Engn Shahrekord Iran

Most of current logistics network design models in the literature typically assume that facilities are always available and absolutely reliable while in practice, they are always subject to several operational and disruption risks. This paper proposes a reliable closed-loop supply chain network design model, which accounts for both partial and complete facility disruptions as well as the uncertainty in the critical input data. The proposed model is of mixed integer possibilistic linear programming type that aims to minimise simultaneously the total cost of opening new facilities and the expected cost of disruption scenarios. An enhanced possibilistic programming approach is proposed to deal with the epistemic uncertainty in input data. Furthermore, the p-robustness criterion is used to limit the cost of disruption scenarios and protect the designed network against random facility disruptions. Several numerical experiments along with sensitivity analyses on uncertain parameters are conducted to illustrate the significance and applicability of the developed model as well as the effectiveness of the proposed solution approach. Our results demonstrate that operational and disruption risks considerably affect the whole structure of the designed network and they must be taken into account when designing a reliable closed-loop logistics network.

关键词： p-robustness possibilistic programming reliable closed-loop logistics network design disruption risks

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