A multi-objective model for the closed-loop supply chain network design with a price-dependent demand, shortage and disruption

Ghomi-Avili, Morteza; Khosrojerdi, Amir; Tavakkoli–Moghaddam, Reza

doi:10.3233/jifs-181051

Cited by 16 publications

(4 citation statements)

References 31 publications

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“…Refs. [155][156][157][158][159][160][161][162] applied the uncertainty method based on probability theory. Gholizadeh et al [156] studied a multi-layered CLSC for a disposable appliance recycling network, using discrete stochastic situations with uncertainty in demand and cost parameters.…”

Section: Study Of Hazardous Materials Transportation To Minimize Risk...mentioning

confidence: 99%

Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review

et al. 2023

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In recent years, there have been frequent cases of impact on the stable development of supply chain economy caused by uncertain events such as COVID-19 and extreme weather events. The creation, management, and impact coping techniques of the supply chain economy now face wholly novel requirements as a result of the escalating level of global uncertainty. Although a significant literature applies uncertainty analysis and optimization modeling (UAO) to study supply chain management (SCM) under uncertainty, there is a lack of systematic literature review and research classification. Therefore, in this paper, 121 articles published in 44 international academic journals between 2015 and 2022 are extracted from the Web of Science database and reviewed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Bibliometric analysis and CiteSpace software are used to identify current developments in the field and to summarize research characteristics and hot topics. The selected published articles are classified and analyzed by author name, year of publication, application area, country, research purposes, modeling methods, research gaps and contributions, research results, and journals to comprehensively review and evaluate the SCM in the application of UAO. We find that UAO is widely used in SCM under uncertainty, especially in the field of decision-making, where it is common practice to abstractly model the decision problem to obtain scientific decision results. This study hopes to provide an important and valuable reference for future research on SCM under uncertainty. Future research could combine uncertainty theory with supply chain management segments (e.g., emergency management, resilience management, and security management), behavioral factors, big data technologies, artificial intelligence, etc.

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Section: Study Of Hazardous Materials Transportation To Minimize Risk...mentioning

confidence: 99%

Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review

et al. 2023

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“…Burton et al ( 2005 ) analyzed the cost effects of two lateral (intra-echelon) transshipment approaches in a two-echelon supply chain network, with a single supply source at the higher echelon and multiple retail locations at the lower, using a series of simulation experiments under different operating conditions. Ghomi-Avili et al ( 2019 ) proposed a multi-objective model for designing the CLSCN with a price-dependent demand while considering random disruptions and shortage. They have considered various resilience strategies, including lateral transshipment among production centers and multi-source allocation to model the resilient supply chain network design problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a sustainable closed-loop supply chain network considering lateral resupply and backup suppliers using fuzzy inference system

Momenitabar

Ebrahimi

Arani

et al. 2022

Environ Dev Sustain

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Sustainability is key factor for transforming traditional supply chain networks into modern ones. This study, for the first time, considers the impacts of the backup suppliers and lateral transshipment/resupply simultaneously on designing a Sustainable Closed-Loop Supply Chain Network (SCLSCN) to decrease the shortage that may occur during the transmission of produced goods in the network. In this manner, the fuzzy multi-objective mixed-integer linear programming model is proposed to design an efficient SCLSCN resiliently. Moreover, the concept of circular economy has been studied in this paper to reduce environmental effects. This study aims to optimize total and environmental costs, including energy consumption and pollution emissions, while increasing job opportunities. A demand uncertainty component is considered to represent reality more closely. Due to the importance of demand, this parameter is estimated using the Fuzzy Inference System (FIS) as an input into the proposed mathematical model. Then, the fuzzy robust optimization approach is applied in a fuzzy set’s environment. The model is tackled by a Multi-Choice Goal Programming Approach with Utility Function (MCGP-UF) to be solved in a timely manner, and the equivalent auxiliary crisp model is employed to convert the multi-objective function to a single objective. The proposed model is tested on the case study of the tire industry in terms of costs, environmental impacts, and social effects. The result confirmed that considering the concept of lateral resupply and backup supplier could considerably decrease the total costs and reduce shortages on the designed SCLSCN. Finally, sensitivity analysis on some crucial parameters is conducted, and future research directions are discussed.

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“…A scenario-based cross-entropy method was used to solve the proposed model. Some research studies have addressed the integrated forward and reverse logistics network design under uncertain disruption by a scenario-based stochastic method, including two-stage stochastic programming [33][34][35] and scenario-based robust optimization [36][37][38][39][40]. In fact, none of the above studies considered risk measures to cope with disruption uncertainties.…”

Section: Reverse Logistics Network Design Under Disruption Riskmentioning

confidence: 99%

“…A numerical example was used to analyze the performances of the risk-averse twostage stochastic programming models. 1 MILP Metaheuristics Min and Ko [16] 2 MINLP Metaheuristics Lee et al [18] 1 MILP Metaheuristics Lee et al [19] 1 MILP Metaheuristics Lee et al [20] 2 MINLP GAMS Ko and Evans [5] 2 MINLP Metaheuristics Ghafarimoghadam et al [21] 3 RO GAMS Ayvaz et al [22] 5 TSSP CPLEX Trochu et al [23] 5 TSSP -Yu and Solvang [24] 5 TSSP Lingo Kara and Onut [4] 5 TSSP GAMS Fonseca et al [25] 5 TSSP CPLEX Yu and Solvang [26] 5 TSSP Lingo Roudbari et al [27] 5 TSSP Metaheuristics Fattahi and Govindan [28] 5 TSSP Metaheuristics Pishvaee et al [29] 5 TSSP Lingo Vahdat and Vahdatzad [30] 5 TSSP Exact Algorithm Sugimura and Murakami [31] 1 MILP Linear Programming Kit Govindan and Gholizadeh [32] 4 SBRO Metaheuristics Ghomi-Avili et al [33] 5 TSSP GAMS Yavari and Zaker [34] 5 TSSP -Yavari and Zaker [35] 5 TSSP -Hatefi and Jolai [36] 4 SBRO GAMS Torabi et al [37] 4 SBRO GAMS…”

mentioning

confidence: 99%

Reverse Logistics Network Design under Disruption Risk for Third-Party Logistics Providers

Chen

2022

Sustainability

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Reverse logistics is attracting attention due to the increasing concerns over environmental issues and the important economic impacts. The design of a reverse logistics network is a major strategic problem in the field of reverse logistics. As cost pressures in product returns continue to mount, a growing number of manufacturers have begun to outsource reverse logistics operations to third-party logistics (3PL) providers. On the other hand, considering disruption risks caused by natural or man-made factors in the reverse logistics network design is inevitable. This paper studies third-party reverse logistics network designs under uncertain disruptions. The problem is formulated as a risk-averse two-stage stochastic programming model with a mean risk objective. Two types of risk measures, value at risk (VaR) and conditional value at risk (CVaR), were examined, respectively. Finally, the sensitivity analysis of the model was carried out. The validity of the mean risk criteria is proved by comparison with risk-neutral approach. Moreover, the performance of the proposed model was examined by stochastic measures.

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A multi-objective model for the closed-loop supply chain network design with a price-dependent demand, shortage and disruption

Cited by 16 publications

References 31 publications

Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review

Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review

Designing a sustainable closed-loop supply chain network considering lateral resupply and backup suppliers using fuzzy inference system

Reverse Logistics Network Design under Disruption Risk for Third-Party Logistics Providers

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