Resolution of an uncertain closed-loop logistics model: An application to fuzzy linear programs with risk analysis

Wang, Hsiao‐Fan; Hsu, Hsin-Wei

doi:10.1016/j.jenvman.2010.05.009

Cited by 53 publications

(26 citation statements)

References 28 publications

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“…Therefore, different approaches to cope with uncertainty are used in the literature including stochastic programming (e.g., Pishvaee et al 2009;Cardoso et al 2013), fuzzy programming (e.g., Tsai and Hung 2009;Qin and Ji 2010;Wang and Hsu 2010;Pishvaee and Torabi 2010;Pishvaee and Razmi 2012;Pishvaee et al 2012a;Pishvaee et al 2012b;Pinto-Varela et al 2011;Vahdani et al 2013a;Vahdani et al 2012) and robust programming (e.g., Pishvaee et al 2011;Pishvaee et al 2012a;Vahdani et al 2012) approaches. Among these approaches, fuzzy programming methods are mostly utilized in recent years due to their capability in handling both epistemic and vague uncertainties.…”

Section: Green and Reverse Logistics Management Under Uncertaintymentioning

confidence: 99%

Green and Reverse Logistics Management Under Fuzziness

Mousazadeh

Torabi

Pishvaee

2014

Supply Chain Management Under Fuzziness

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Green supply chain management (GrSCM) has its roots in supply chain management (SCM) and environmental management. In fact, adding ''green'' concept into traditional SCM leads to studying environmental impact of SCMrelated processes. Logistics activities which form the main part of SCM-related processes belong to the most influential sources of environmental pollution and greenhouse emissions which may cause harmful impacts both on human health and ecosystem quality. In order to reduce hazardous environmental impacts of logistics activities, the concept of green logistics (GrLog) and reverse logistics (RL) was introduced. Similar to traditional supply chain, uncertainty plays an important role in GrSCM; however, considering the environmental factors beside the quantity and quality of end-of-life products elevates the degree of uncertainty in GrLog and RL problems. In this chapter, designing and planning problems in GrLog and RL are investigated in a fuzzy environment via a systematic review and analysis of recent literature. Three selected fuzzy mathematical models from the recent literature are elaborated. A real industrial green logistics case study is described and investigated and a number of avenues for further research are finally suggested.

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Section: Green and Reverse Logistics Management Under Uncertaintymentioning

confidence: 99%

Green and Reverse Logistics Management Under Fuzziness

Mousazadeh

Torabi

Pishvaee

2014

Supply Chain Management Under Fuzziness

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“…So, in recent years a few number of papers used more flexible approaches such as fuzzy programming (e.g. Wang & Hsu, 2010b).…”

Section: Closed-loop Supply Chain Network Designmentioning

confidence: 99%

A new fuzzy mathematical model for green supply chain network design

Amalnick¹,

Saffar

2017

10.5267/j.ijiec

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The environmental changes caused by industrial activities have spurred a significant interest in designing supply chain networks by considering environmental issues such as CO2 emission. The pivotal role of taking uncertainty and risk into account in closed-loop supply chain networks has induced numerous researchers and practitioners to develop appropriate decision making tools to cope with these issues in such networks. To design a supply chain regarding environmental impacts under uncertainty of the input data and to cope with the operational risks, this paper proposes a multi objective possibilistic optimization model. The proposed model minimizes traditional costs such as cost of products shipment, purchasing machines and so on, as well as minimizing the environmental impact, and as a results strikes a balance between the two objective functions. Furthermore, in order to solve the proposed multi objective fuzzy mathematical programming model, an interactive fuzzy solution approach is applied. Numerical experiments are used to prove the applicability and feasibility of the developed possibilistic programming model and the usefulness of the applied hybrid solution approach.

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“…Therefore, in recent years, a few number of papers use more flexible approaches such as fuzzy programming (e.g. Wang & Hsu, 2010a). El-Sayed et al (2010) present an integrated designing network under probabilistic approaches, which determined distribution centers, suppliers, re-assembly centers, and re-distribution centers.…”

Section: Introductionmentioning

confidence: 99%

“…In order to solve supply chain design problems, a great numbers of heuristic algorithms (e.g., Wang & Hsu, 2010a) and meta heuristics such as simulated annealing (e.g., , genetic algorithm (e.g., Min et al, 2006), scatter search (e.g., Du & Evans, 2008) tabu search (e.g., Lee & Dong, 2007) are applied and developed to solve these models. Jabal ameli et al (2009) Syam (2002) apply simulated annealing operation to solve the model considering logistic costs in supply chain.…”

Section: Introductionmentioning

confidence: 99%

A new multi objective optimization model for designing a green supply chain network under uncertainty

Saffar¹,

Shakouri²,

Razmi³

2015

10.5267/j.ijiec

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Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with -constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE) is applied.

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Resolution of an uncertain closed-loop logistics model: An application to fuzzy linear programs with risk analysis

Cited by 53 publications

References 28 publications

Green and Reverse Logistics Management Under Fuzziness

Green and Reverse Logistics Management Under Fuzziness

A new fuzzy mathematical model for green supply chain network design

A new multi objective optimization model for designing a green supply chain network under uncertainty

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