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possibilistic inventory and supplier selection model for an assembly system

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第1-4期67卷 575-587页

作者： Assadipour, Ghazal Razmi, Jafar Mem Univ Newfoundland Fac Business Adm St John NF Canada Univ Tehran Coll Engn Dept Ind Engn Tehran Iran

In this paper, the problem of inventory lot-sizing and supplier selection for an assembly system is considered, where the supplier available capacities are assumed as ambiguous dynamic parameters. In this scenario, which is a frequent case in large assembly-based factories such as automobile manufacturers, the final product is assembled from multiple components with different conversion factors, which can be sourced from multi-capacitated suppliers through the multi-period horizon of imprecise demand. Due to high shut-down costs of assembly lines, it is assumed that production never stops even though some components may not be available. Therefore, the unfinished products are transferred to a buffer zone and preserved there until the lacking components become available. In this study, a possibilistic mixed integer mathematical model, with fuzzy objective function and soft constraints, is developed to determine which component in what quantities, from which suppliers, and in which periods should be ordered. The model, inspired by the real case of the Iran Khodro Car Company, aims to maximize the profit while keeping a high customer service level by avoiding shortages. This model also considers the ambiguity of dynamic parameters such as demand, suppliers' available capacities, prices, and holding and shortage costs. To solve the problem, the possibilistic model is first converted into an auxiliary crisp multi-objective model. Through an interactive fuzzy approach, the suggested multi-objective problem is then transformed into an equivalent single-objective model. Finally, a particle swarm optimization is proposed to achieve the overall satisfactory compromise solution. A numerical sample is used to validate the proposed model.

关键词： Assembly systems Supplier selection Inventory lot-sizing possibilistic programming Particle swarm optimization

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Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach

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INFORMATION SYSTEMS FRONTIERS 2018年第4期20卷 759-782页

作者： Kamyabniya, Afshin Lotfi, M. M. Naderpour, Mohsen Yih, Yuehwern Yazd Univ Dept Ind Engn Fac Engn Yazd Iran Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA UTS Fac Engn & Informat Technol Ctr Artificial Intelligence Sydney NSW Australia

Resource sharing, as a coordination mechanism, can mitigate disruptions in supply and changes in demand. It is particularly crucial for platelets because they have a short lifespan and need to be transferred and allocated within a limited time to prevent waste or shortages. Thus, a coordinated model comprised of a mixed vertical-horizontal structure, for the logistics of platelets, is proposed for disaster relief operations in the response phase. The aim of this research is to reduce the wastage and shortage of platelets due to their critical role in wound healing. We present a bi-objective location-allocation robust possibilistic programming model for designing a two-layer coordinated organization strategy for multi-type blood-derived platelets under demand uncertainty. Computational results, derived using a heuristic epsilon-constraint algorithm, are reported and discussed to show the applicability of the proposed model. The experimental results indicate that surpluses and shortages in platelets remarkably declined following instigation of a coordinated disaster relief operation.

关键词： Coordination Disaster relief operations Platelets possibilistic programming Heuristic epsilon-constraint algorithm Demand uncertainty

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Integrated production-distribution planning in two-echelon systems: a resilience view

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2017年第4期55卷 1040-1064页

作者： Khalili, Seyed Mohammad Jolai, Fariborz Torabi, Seyed Ali Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

Global supply chains are increasingly exposed to operational and disruption risks that threaten their business continuity. This paper presents a novel two-stage scenario-based mixed stochastic-possibilistic programming model for integrated production and distribution planning problem in a two-echelon supply chain over a midterm horizon under risk. Operational risks are handled by introducing imprecise (i.e. possibilistic) parameters while disruption risks are accounted for through stochastic disruption scenarios. The proposed model accounts for the risk mitigation options and recovery of lost capacities in an integrated manner. In the first stage, the structure of the chain and proactive risk mitigation decisions are determined, while the second stage specifies the recovery plan of lost capacities in addition to production and distribution plans. Considering extra capacities in the production facilities, backup routes for transportation links and pre-positioning of emergency inventory in distribution centres are considered as feasible options to improve the resilience level of the supply chain. We propose a new indicator for optimising the resilience level of the chain based on restoration of lost capacities. For the sake of robustness, the expected worst case of the second stage's objective function is considered by utilising the conditional value at risk (CVaR) measure. The validation and applicability of the proposed model are examined through several numerical experiments.

关键词： production and distribution planning supply chain resilience two-stage scenario-based stochastic programming possibilistic programming conditional value at risk

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Comparisons of interactive fuzzy programming approaches for closed-loop supply chain network design under uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 125卷 500-513页

作者： Wu, Gen-Han Chang, Chen-Kang Hsu, Lo-Mei Yuan Ze Univ Dept Ind Engn & Management Taoyuan Taiwan Natl Dong Hwa Univ Grad Inst Logist Management Hualien Taiwan

A closed-loop supply chain network design is a facility location problem combined with forward and reverse logistics. To reflect the environmental fluctuations and the market competitiveness, recycled products will be remanufactured and sold to obtain the advantages of achieving green environmental protection and of reducing cost. This study considers the uncertain factors such as the quantity of customer product demand, the quantity of recycled used products, and the facility opening cost to design a fuzzy mixed-integer linear programming model for a closed-loop supply chain network design under uncertainty. A two-stage interactive possibilistic programming procedure is constructed to obtain a preferred compromise solution. Four fuzzy optimization approaches are embedded into the two-stage interactive possibilistic programming model and are compared. After computing experimental testing, we find that this study, based on the TH fuzzy approach, obtains a more efficient balanced compromise solution than other fuzzy approaches.

关键词： Closed-loop supply chain network Uncertainty Facility location Fuzzy programming possibilistic programming

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A fuzzy bi-level programming approach to scarce drugs supply and ration planning problem under risk

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FUZZY SETS AND SYSTEMS 2022年 434卷 48-72页

作者： Aghababaei, Bahareh Pishvaee, Mir Saman Barzinpour, Farnaz

In this paper, a possibilistic bi-objective bi-level programming model is proposed to optimize the management of scarce drugs supply and rationing under difficult situations such as COVID-19 outbreak. Multiple followers and single leader exist in the proposed Stackelberg model in which the followers are divided into two groups, manufacturers and suppliers. The first group has priority over the latter. The competition between these two groups is addressed using a lexicographical procedure. Moreover, the interaction between the individuals in each group is incorporated through a normalized Nash equilibrium. To avoid the discriminative allocations, the disability-adjusted life years (DALY) index is employed to improve the rationing process. Due to the significance of uncertainty in such a problem, a novel risk-based possibilistic expected value approach is devised to control the mean and risk values of the objective functions. Finally, to validate the proposed solution method, the model is solved by applying Karush-Kuhn-Tucker (KKT) conditions and using the data extracted from a real case study. The results reveal the applicability and usefulness of the proposed possibilistic model compared to the conventional possibilistic approach (c) 2021 Elsevier B.V. All rights reserved.

关键词： Fuzzy mathematical programming Scarce resources allocation Bi-level programming Pricing possibilistic programming Multi-objective optimization COVID-19

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A sustainable second-generation biodiesel supply chain network design problem under risk

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2017年第PartB期66卷 258-277页

作者： Babazadeh, Reza Razmi, Jafar Pishvaee, Mir Saman Rabbani, Masoud Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

This paper presents a multi-objective possibilistic programming model to design a second-generation biodiesel supply chain network under risk. The proposed model minimizes the total costs of biodiesel supply chain from feedstock supply centers to customer centers besides minimizing the environmental impact (EI) of all involved processes under a well-to-wheel perspective. Non-edible feedstocks are considered for biodiesel production. Variable cultivation cost of non-edible feedstock is assumed to be non-linear and dependent upon the amount of cultivated area. New formulation of possibilistic programming method is developed which is able to minimize the total mean and risk values of problems with possibilistic-based uncertainty. To solve the proposed multi-objective model, a hybrid solution approach based on flexible lexicographic and augmented e-constraint methods is proposed which is capable to find appropriate efficient solutions from the Pareto-optimal set. The performance of the proposed possibilistic programming method as well as the developed solution approach are evaluated and validated through conducting a real case study in Iran. The outcome of this study demonstrates that high investment cost is required for improving the environmental impact and risk of sustainable biodiesel supply chain network design under risk. Decision maker preferences are required for suitable trade-off among total costs, risk values and environmental impact. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Sustainable development Second-generation biodiesel supply chain Non-edible feedstock possibilistic programming Multi-objective optimization

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A sustainable medical waste management system design in the face of uncertainty and risk during COVID-19

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FUZZY OPTIMIZATION AND DECISION MAKING 2023年第3期22卷 519-554页

作者： Zarrinpoor, Naeme Shiraz Univ Technol Shiraz Iran

COVID-19's developing trend has put the waste management systems of governments all over the world in jeopardy. The increasing rise of infectious medical waste has now become a serious problem. This paper presents a multi-period multi-objective model for designing a medical waste management system during the COVID-19 pandemic. The model aims to reduce total costs of infectious medical waste management while also reducing the environmental impact of treatment centers, disposal centers, and transportation. It also aims to maximize the suitability of treatment technology based on social considerations and reduce the risk associated with processing and transporting COVID-19 waste. Different strategic and operational decisions are taken into account that include the selection of treatment technologies, the location of treatment and disposal centers, the flow of generated medical waste between facilities, and the number of vehicles required for the medical waste transport. The model tackles the uncertainty associated with model parameters, and it uses a credibility-based possibilistic programming method to deal with uncertainties. The suggested model is solved using an interactive fuzzy programming method and the importance of social indicators for selecting treatment technology is determined using the fuzzy best-worst approach. The effectiveness of the model is demonstrated by a practical case study in Shiraz, Iran. The numerical results can help system designers to achieve the most suitable trade-off between the sustainability goals and the safety viewpoint.

关键词： COVID-19 Medical waste management Sustainability Risk Uncertainty possibilistic programming Fuzzy best-worst method

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A Levy flight embedded particle swarm optimization for multi-objective parallel-machine scheduling with learning and adapting considerations

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 91卷 109-128页

作者： Pakzad-Moghaddam, S. H. Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

The study at hand is devoted to schedule jobs on uniform parallel processors which are capable of adapting as well as learning. The problem is formulated into a bi-objective mixed integer mathematical model in which several parameters are supposed to follow triangular possibility distributions. Converting the aforementioned model, an auxiliary equivalent mixed integer crisp one is constructed through an interactive possiblistic programming approach. Due to the NP-hardness of the problem, swarm intelligence is hired by applying a highly modified Particle Swarm Optimization (PSO) method. In the proposed evolutionary method, called Levy Flight Embedded Particle Swarm Optimization (LFEPSO), uniformly distributed walks are replaced by Levy flights. In order to check the validity of the model and the performance of the proposed LFEPSO, twenty-six data sets are generated on a random basis. As the numerical results indicate, embedding Levy flights made a tremendous improvement towards solving the local optima problem of the traditional PSO. Compared to the exact solution method, LFEPSO has shown an outstanding performance in scheduling 8-500 jobs on 3-50 parallel processors. In addition, numerical results reveal the significant role of considering the adapting ability in accurately modeling real-world and huge-sized problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Parallel-machine scheduling Learning effect Adapting ability possibilistic programming Particle swarm optimization Levy flight

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Can the uncertainty in brass casting blending problem be managed? A probability/possibility transformation approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2011年第3期61卷 729-738页

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

Problems about the uncertainty in raw material compositions are a critical issue for the blending problems. It is feared that uncertainty in raw material compositions would often cause percent values of the actual blend to go out of specification limits. In this paper, the aleatory and epistemic uncertainties have been handled simultaneously in a blending optimization problem for brass casting. The aleatory and epistemic uncertainties are modeled by using probability and possibility theories respectively. However, the probabilistic and the possibilistic uncertainties are different from the each other. Therefore to solve the mathematical model, including these uncertainties, a transformation of any type of uncertainty to the other is needed. In this study, probabilistic uncertainties are transformed to the possibilistic uncertainties by considering Rong and Landelma's (2008) and the Dubois, Prade, and Sandri (1993) and Dubois, Foulloy, Mauris, and Prade (2004) transformation approaches. This transformation process converts the former model to a possibilistic model. Then the possibilistic models, obtained from each transformation, are solved by using a cuts approach. The solutions of the two possibilistic models have shown that the model, which uses Dubois's transformation, prepares blends with lower cost than the other model, which uses Rong and Landelma's transformation. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Blending problem Probability/possibility transformation Brass casting possibilistic programming

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A possibilistic location-inventory model for multi-period perishable pharmaceutical supply chain network design

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2018年 138卷 490-505页

作者： Savadkoohi, E. Mousazadeh, M. Torabi, S. Ali Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

In this paper, a location-inventory model is developed for a three-echelon pharmaceutical network design problem. The problem includes several strategic and tactical decisions (i.e. opening of manufacturing and distribution centers, material flows in the network, and the optimal inventory policy taking into account products' perishability) while aiming at minimizing the total cost of the network. A possibilistic programming approach is devised to cope with the imprecise parameters. In order to verify and analyze the proposed model, it is tested on a real case study and useful managerial insights are provided by conducting several sensitivity analyses. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Pharmaceutical supply chain network design Perishability possibilistic programming Me measure

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