A Robust Model for 3PLS to Design a Remanufacturing Logistics Network under the Uncertain Environment

Zhang, Yong; Xie, Lei; Wen, Hao; Cui, Xiaoyan

doi:10.1109/ical.2007.4338589

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…Based on the criteria, we select 22 articles [1][2][3][4][5][6][7][8][19][20][21][22][23][24][25][26][27][28][29][30][31][32] to undertake the literature review. There are two articles [2,32] published by Journal of Transportation Systems Engineering & Information Technology that is indexed by an Engineering Village Compendex Database.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are two articles [2,32] published by Journal of Transportation Systems Engineering & Information Technology that is indexed by an Engineering Village Compendex Database. Three articles [1,23,28] published by conference proceedings can be found, including one published by the IEEE conference [23] and two published by Elsevier Procedia [1,28]. The rest of the articles were all published by journals with impact factors, in which Computer & Industry Engineering published three references [5,6,21] that have the largest number of selected articles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The logistics center location and allocation problem under an uncertain environment receives extensive attention from the logistics planning field. There have been many studies that concern one source of uncertainty, in which demand uncertainty related to the logistics center location and allocation problem (logistics network design problem) has received the widest attention, e.g., Zhang et al [23], Zhang and Xu [24], Pishvaee et al [25], Amin et al [26], Hatefi and Jolai [27], and Shimizu et al [28]. Studies that consider supply uncertainty can also be found, e.g., Lieckensab and Vandaeleab [29].…”

Section: Review Of the Logistics Center Location And Allocation Problmentioning

confidence: 99%

“…Similar to the modification of Equation (11) into Equations (17) and (18), fuzzy chance constraint Equation (13) can be reformulated as Equations (22) and (23), which are also crisp and linear.…”

Section: Step 3: Crisp and Linear Reformations Of Fuzzy Chance Constrmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy Linear Programming Models for a Green Logistics Center Location and Allocation Problem under Mixed Uncertainties Based on Different Carbon Dioxide Emission Reduction Methods

Sun

Zhang

2019

Sustainability

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This study explores a foundational logistics center location and allocation problem in a three-stage logistics network that consists of suppliers, logistics centers, and customers. In this study, the environmental sustainability of the logistics network is improved by optimizing the carbon dioxide emissions of the logistics network based on multi-objective optimization and carbon tax regulation. Mixed uncertainties in the planning stage, including the supply capacities of suppliers, operation capacities of logistics centers, and demands of customers, are modeled using triangular fuzzy numbers based on the fuzzy set theory to order to enhance the reliability of the logistics center location and allocation planning. To solve the green logistics center location and allocation problem under mixed uncertainties, we establish two fuzzy mixed integer linear programming models. The fuzzy credibilistic chance-constrained programming is then adopted to obtain the crisp and linear reformulations of the fuzzy programming models. A numerical case is given to verify the feasibility of the proposed methods, in which the performance of carbon tax regulation in reducing carbon dioxide emissions is then tested based on the benchmark provided by the multi-objective optimization. Lastly, sensitivity analysis and fuzzy simulation are utilized to reveal the effect of the mixed uncertainties on the logistics location and allocation planning and further determine the best confidence level in the fuzzy chance constraints to provide decision makers with a crisp plan.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Review Of the Logistics Center Location And Allocation Problmentioning

confidence: 99%

Section: Step 3: Crisp and Linear Reformations Of Fuzzy Chance Constrmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Linear Programming Models for a Green Logistics Center Location and Allocation Problem under Mixed Uncertainties Based on Different Carbon Dioxide Emission Reduction Methods

Sun

Zhang

2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…Then they used simulation technique to study uncertainty into their model, and the model was solved using hybrid genetic algorithm. Zhang et al (2007) presented a fuzzy model to design a network for reproduction logistics, from 3PL point of view. This model was under reverse logistics process.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi-objective location-inventory model for 3PL providers with sustainable considerations under uncertaint

Daghigh¹,

Jabalameli²,

Amiri³

et al. 2016

10.5267/j.ijiec

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Modular recycling supply chain under uncertainty: a robust optimisation approach

Shahparvari

Chhetri

Chan

et al. 2018

Int J Adv Manuf Technol

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A Robust Model for 3PLS to Design a Remanufacturing Logistics Network under the Uncertain Environment

Cited by 7 publications

References 19 publications

Fuzzy Linear Programming Models for a Green Logistics Center Location and Allocation Problem under Mixed Uncertainties Based on Different Carbon Dioxide Emission Reduction Methods

Fuzzy Linear Programming Models for a Green Logistics Center Location and Allocation Problem under Mixed Uncertainties Based on Different Carbon Dioxide Emission Reduction Methods

A multi-objective location-inventory model for 3PL providers with sustainable considerations under uncertaint

Modular recycling supply chain under uncertainty: a robust optimisation approach

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