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A unique fuzzy multivariate modeling approach for performance optimization of maintenance workshops with cognitive factors

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第1-4期90卷 499-525页

作者： Azadeh, A. Ghaderi, S. F. Pashapour, S. Keramati, A. Malek, M. Rezaei Esmizadeh, M. Univ Tehran Ctr Excellence Intelligent Expt Mech Sch Ind & Syst Engn Tehran Iran Univ Tehran Dept Engn Optimizat Res Coll Engn Tehran Iran

This paper puts forward an integrated fuzzy simulation-fuzzy data envelopment analysis (FDEA)-fuzzy cognitive map (FCM) algorithm for performance optimization of maintenance workshops by incorporating cognitive and time-dependent factors. Due to severe ambiguousness associated with the collected data, the fuzzy set theory is incorporated into the approach. Fuzzy computer simulation is employed for modeling the workshop and providing time-dependent factors. FCM is used for extracting relations between cognitive factors. FDEA is used for ranking scenarios based on inputs and outputs of FCM and the developed computer simulation. Moreover, a recent possibilistic programming approach is used to convert the fuzzy DEA model to an equivalent crisp model. The sensitivity analysis show that alpha = 0.1 is the best alpha-cut level for interpreting data. The proposed algorithm is capable of modeling and optimizing the performance of maintenance workshops in uncertain and non-linear environments. The solution quality is inspected and shown through an actual maintenance workshop. This is the first study that presents an integrated non-crisp algorithm for performance optimization of maintenance activities with cognitive and time-dependent factors.

关键词： Maintenance workshop Cognitive factors Fuzzy simulation Fuzzy data envelopment analysis (FDEA) Fuzzy cognitive map (FCM) possibilistic programming

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A possibilistic aggregate production planning model for brass casting industry

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PRODUCTION PLANNING & CONTROL 2010年第3期21卷 319-338页

作者： Sakalli, Umit Sami Baykoc, Omer Faruk Birgoren, Burak Kirikkale Univ Dept Ind Engn TR-71451 Kirikkale Turkey Gazi Univ Dept Ind Engn TR-06570 Ankara Turkey

This article discusses a possibilistic aggregate production planning (APP) model for blending problem in a brass factory;the problem computing optimal amounts of raw materials for the total production of several types of brass in a planning period. The model basically has a multi-blend model formulation in which demand quantities, percentages of the ingredient in some raw materials, cost coefficients, minimum and maximum procurement amounts are all imprecise and have triangular possibility distributions. A mathematical model and a solution algorithm are proposed for solving this model. In the proposed model, the Lai and Hwang's fuzzy ranking concept is relaxed by using 'Either-or' constraints. An application of the brass casting APP model to a brass factory demonstrates that the proposed model successfully solves the multi-blend problem for brass casting and determines the optimal raw material purchasing policies.

关键词： blending possibilistic programming aggregate production planning brass casting

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Strong guidance on mitigating the effects of uncertainties in the brass casting blending problem: a hybrid optimization approach

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2013年第4期64卷 562-576页

作者： Sakalli, U. S. Baykoc, O. F. Kirikkale Univ TR-71451 Kirikkale Turkey Gazi Univ Ankara Turkey

The scrap charge optimization problem in the brass casting process is a critical management concern that aims to reduce the charge while preventing specification violations. Uncertainties in scrap material compositions often cause violations in product standards. In this study, we have discussed the aleatory and epistemic uncertainties and modelled them by using probability and possibility distributions, respectively. Mathematical models including probabilistic and possibilistic parameters are generally solved by transforming one type of parameter into the other. However, the transformation processes have some handicaps such as knowledge losses or virtual information production. In this paper, we have proposed a new solution approach that needs no transformation process and so eliminates these handicaps. The proposed approach combines both chance-constrained stochastic programming and possibilistic programming. The solution of the numerical example has shown that the blending problem including probabilistic and possibilistic uncertainties can be successfully handled and solved by the proposed approach. Journal of the Operational Research Society (2013) 64, 562-576. doi:10.1057/jors.2012.50

关键词： blending problem stochastic programming possibilistic programming brass casting

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Multiobjective fuzzy mathematical model for a financially constrained closed-loop supply chain with labor employment

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COMPUTATIONAL INTELLIGENCE 2020年第1期36卷 4-34页

作者： Goli, Alireza Zare, Hasan Khademi Tavakkoli-Moghaddam, Reza Sadegheih, Ahmad Yazd Univ Dept Ind Engn Saffayieh Yazd Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

This paper addresses the multiobjective, multiproducts and multiperiod closed-loop supply chain network design with uncertain parameters, whose aim is to incorporate the financial flow as the cash flow and debts' constraints and labor employment under fuzzy uncertainty. The objectives of the proposed mathematical model are to maximize the increase in cash flow, maximize the total created jobs in the supply chain, and maximize the reliability of consumed raw materials. To encounter the fuzzy uncertainty in this model, a possibilistic programming approach is used. To solve large-sized problems, the multiobjective simulated annealing algorithm, multiobjective gray wolf optimization, and multiobjective invasive weed optimization are proposed and developed. The numerical results demonstrate that these algorithms solve the problems within about 1% of the required solving time for the augmented epsilon-constraint and have similar performance and even better in some cases. The multiobjective simulated annealing algorithm with a weak performance takes less time than the other two algorithms. The multiobjective gray wolf optimization and multiobjective invasive weed optimization algorithms are superior based on the multiobjective performance indices.

关键词： financial flow of supply chain multiobjective metaheuristic algorithms possibilistic programming

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A fuzzy mathematical programming approach to the assessment of efficiency with DEA models

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FUZZY SETS AND SYSTEMS 2003年第2期139卷 407-419页

作者： León, T Liern, V Ruiz, JL Sirvent, I Univ Valencia Dept Estadist & Invest Operat E-46100 Valencia Spain Univ Valencia Dept Econ Financiera & Matemat Valencia 46071 Spain Univ Miguel Hernandez Ctr Invest Operat Alicante 03202 Spain

In many real applications, the data of production processes cannot be precisely measured. This is particularly worrying when assessing efficiency with frontier-type models, such as data envelopment analysis (DEA) models, since they are very sensitive to possible data errors. For this reason, the possibility of having available a methodology that allows the analyst to deal with imprecise data becomes an issue of great interest in these contexts. To that end, we develop some fuzzy versions of the classical DEA models (in particular, the BCC model) by using some ranking methods based on the comparison of a-cuts. The resulting auxiliary crisp problems can be solved by the usual DEA software. We show, in a numerical example, how our models become specially useful for detecting sensitive decision-making units. Our approaches can be seen as an extension of the DEA methodology that provides users and practitioners with models which represent some real life processes more appropriately. (C) 2003 Elsevier B.V. All rights reserved.

关键词： data envelopment analysis fuzzy mathematical programming fuzzy intervals possibilistic programming

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Planning of municipal solid waste management under dual uncertainties

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WASTE MANAGEMENT & RESEARCH 2010年第8期28卷 673-684页

作者： Zhang, Xiaodong Huang, Guo H. Nie, Xianghui Chen, Yumin Lin, Qianguo Univ Regina Fac Engn & Appl Sci Environm Syst Engn Program Regina SK S4S 0A2 Canada

Municipal solid waste management is a complex and multidisciplinary problem, involving a number of impact factors associated with various uncertainties. In this study, a hybrid interval-parameter possibilistic programming (IPP) approach was developed and applied for planning municipal solid waste management under dual uncertainties. The IPP improves upon the existing management approaches by allowing possibility distributions of the lower and upper bounds of some interval parameters in the objective function and interval information in the modelling coefficients to be effectively incorporated within its optimization. By introducing the concept of possibilistic interval numbers, the dual uncertainties can be communicated into the optimization process and the resulting solutions, such that the generated decision schemes can effectively reflect the highly complex system features under uncertainty. The results of the case study indicate that useful information can be obtained for providing feasible decision schemes for waste flow allocation. Different decision schemes can be generated by adjusting waste flow allocation patterns within the solution intervals. Lower decision variable values should be used to obtain lower system cost of waste treatment and disposal under advantageous conditions, and higher decision variable values should be used under demanding conditions (worst case conditions). A strong desire to acquire the lower system cost will lead to the decreased probability of meeting the treatment and disposal requirements (i.e. the increased risk of unforeseen conditions);willingness to accept the upper limit of the system cost will guarantee that waste treatment and disposal requirements are met.

关键词： Interval possibilistic programming solid waste management uncertainty optimization

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possibilistic scheduling routing for short-notice bushfire emergency evacuation under uncertainties: An Australian case study

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2017年 72卷 96-117页

作者： Shahparvari, Shahrooz Abbasi, Babak Chhetri, Prem RMIT Univ Sch Business IT & Logist Melbourne Vic 3000 Australia

This paper aims to develop a capacitated vehicle routing solution to evacuate short-notice evacuees with time windows and disruption risks under uncertainties during a bushfire. A heuristic solution technique is applied to solve the triangular possibilistic model to optimise emergency delivery service. The effectiveness of the proposed algorithm is evaluated by comparing it with a designed genetic algorithm on sets of 20 numerical examples. The model is then applied to the real case study of 2009 Black Saturday bushfires in Victoria, Australia. The results show that it is possible to transfer the last-minute evacuees during the Black Saturday bushfires under the hard time window constraint. Network disruptions however have impact on resource utilisation. The modelling outputs will be useful in the development of emergency plans and evacuation strategies to enhance rapid response to last-minute evacuation in a bushfire emergency. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： possibilistic programming Fuzzy sets Uncertainties Wildfire (bushfire) Optimisation Vehicle routing problem Time window Late evacuation And disruption risk

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Interactive multiobjective fuzzy random linear programming: Maximization of possibility and probability

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2008年第2期188卷 530-539页

作者： Katagiri, Hideki Sakawa, Masatoshi Kato, Kosuke Nishizaki, Ichiro Hiroshima Univ Grad Sch Engn Hiroshima 7398527 Japan

This paper considers multiobjective linear programming problems with fuzzy random variables coefficients. A new decision making model is proposed to maximize both possibility and probability, which is based on possibilistic programming and stochastic programming. An interactive algorithm is constructed to obtain a satisficing solution satisfying at least weak Pareto optimality. (c) 2007 Elsevier B.V. All rights reserved.

关键词： fuzzy random variable multiobjective linear programming probability maximization model stochastic programming possibilistic programming interactive algorithm

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A multi-objective healthcare inventory routing problem;a fuzzy possibilistic approach

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2015年 80卷 74-94页

作者： Niakan, Farzad Rahimi, Mohammad INSA Lyon DISE Lab EA4570 Villeurbanne France

This paper presents a new multi-objective mathematical model to address a Healthcare Inventory Routing Problem (HIRP) for medicinal drug distribution to healthcare facilities. The first part of objective function minimizes total inventory and transportation costs, while satisfaction is maximized by minimizing forecast error which caused by product shortage and the amount of expired drugs;Greenhouse Gas (GHG) emissions are also minimized. A demand forecast approach has been integrated into the mathematical model to decrease drug shortage risk. A hybridized possibilistic method is applied to cope with uncertainty and an interactive fuzzy approach is considered to solve an auxiliary crisp multi-objective model and find optimized solutions. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Inventory routing problem Healthcare Fuzzy mathematical programming possibilistic programming Fuzzy multi-objective optimization

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Resilient supplier selection and order allocation under operational and disruption risks

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2015年 79卷 22-48页

作者： Torabi, S. A. Baghersad, M. Mansouri, S. A. Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Virginia Polytech Inst & State Univ Dept Business Informat Technol Blacksburg VA 24061 USA Brunel Univ London Brunel Business Sch London England

This study proposes a bi-objective mixed possibilistic, two-stage stochastic programming model to address supplier selection and order allocation problem to build the resilient supply base under operational and disruption risks. The model accounts for epistemic uncertainty of critical data and applies several proactive strategies such as suppliers' business continuity plans, fortification of suppliers and contracting with backup suppliers to enhance the resilience level of the selected supply base. A five-step method is designed to solve the problem efficiently. The computational results demonstrate the significant impact of considering disruptive events on the selected supply base. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Supply chain disruption/risk management Resilient supplier base Business continuity management possibilistic programming Two-stage stochastic programming Differential evolution

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