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

Wu, Gen-Han; Chang, Chen-Kang; Hsu, Lo-Mei

doi:10.1016/j.cie.2018.09.022

Cited by 36 publications

(13 citation statements)

References 35 publications

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“…Some researchers have developed fuzzy models for handling closed-loop supply chain networks aiming to minimise the total supply chain costs and maximise profit (Amin and Zhang 2012 ; Dai and Li 2017 ; Darbari et al 2017 ; Farrokh et al 2018 ; Jindal and Sangwan 2013 ; Özceylan and Paksoy 2013 ; Pishvaee and Torabi 2010 ; Pourmehdi et al 2020 ; Ramezani et al 2014 ; Subulan et al 2015 ; Talaei et al 2016 ; Vahdani et al 2013 ; Wu et al 2018 ). Tabrizi and Razmi ( 2013 ) developed a fuzzy MINLP model of a supply chain network, representing uncertain risk sources by the fuzzy set theory.…”

Section: Results Of the Systematic Literature Reviewmentioning

confidence: 99%

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Wofuru-Nyenke

Briggs

Aikhuele

2022

Process Integr Optim Sustain

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Managers at various echelons of supply chains are continuously faced with problems of examining and improving supply chain processes with the aim of improving productivity and customer service level, while reducing emissions and total costs simultaneously. This study is aimed at presenting the trends in sustainable manufacturing supply chain modelling in order to establish the modelling approaches which have predominantly been used for improving manufacturing supply chains from years 2010 to 2020. The study employs the systematic literature review methodology for reviewing articles published within the 11-year period. We proffer a classification approach for manufacturing supply chain models, grouping these models into mathematical models, simulation models, hybrid models, and their subcategories. The results showed that though there is a rising trend in the use of simulation and hybrid models, mathematical models have been used more for sustainable manufacturing supply chain modelling. The rise in the use of simulation and hybrid models can be explained by the fact that these models tend to handle uncertain and stochastic data better than mathematical models, which perform better with deterministic data. This research will aid other researchers in recognising the current gaps in manufacturing supply chain modelling in order to identify future research directions.

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Section: Results Of the Systematic Literature Reviewmentioning

confidence: 99%

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Wofuru-Nyenke

Briggs

Aikhuele

2022

Process Integr Optim Sustain

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“…The second is the fuzzy programming approach. Wu et al ( 2018 ) comprehensively considered the uncertainty of customer product demand, recycled products, and facility opening costs and designed a fuzzy interactive possibilistic programming model. Meanwhile, Hocine et al ( 2018 ) formulated a new multi-segment fuzzy goal programming model to analyze the uncertainty of the multi-standard renewable energy portfolio.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust optimization model of anti-epidemic supply chain under technological innovation: learning from COVID-19

et al. 2022

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The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation.

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“…Stochastic MPMs are applicable when, some or all, those parameters are random with known probability distributions (Gren et al, 2012;Kenne et al, 2012;Roghanian and Pazhoheshfar, 2014;Garrido et al, 2015;Yu and Foggo, 2017;Moreno et al, 2018;Snoeck et al, 2019). Possibilistic MPMs are suitable when the system parameters are uncertain (Wu et al, 2018). Such models are riskier than stochastic ones.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of reverse logistics networks under uncertainty: Multi-objective approach

Tosarkani¹

2022

Preprint

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A reverse logistics network (RLN) is defined as the backward flow of products, specifically the products that are returned for recycling. Several entities are involved in a recycling process such as regional collection depots, recovery centers, remanufacturing plants, and disposal centers. The main objective of RLN design is to facilitate the reclamation of used products for the purpose of saving cost, energy, resources, and diverting waste from landfills and waterways. On this matter, decision-makers should consider different types of parameters (i.e., fixed and variable costs, the quantity of demand and return, and the quality of returned products) affecting the configuration of facility location models. In real life, there are a variety of ambiguities associated with mentioned parameters that stem from either internal or external factors (e.g., volatility in market demand, rate of the returned products, unit transportation cost). The main objective of this dissertation is to develop multi- objective optimization models under uncertainty. In this regard, some integrated solution methodologies are introduced to address different types of uncertainty in five stewardship programs (i.e., electronic recycling association (ERA), Canadian battery association (CBA), beverage container stewardship program regulation (BCSPR), Ontario electronic stewardship (OES), wastewater management in hydraulic fracturing) in Canada. To consider the environmental impact of such stewardship programs, the proposed mathematical models are extended to the multi-objective optimization models. In this regard, the proposed solution methodologies make decision-makers capable of optimizing the environmental aspects (e.g., green practices of third parties, carbon emissions) associated with RLNs.

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

Cited by 36 publications

References 35 publications

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Robust optimization model of anti-epidemic supply chain under technological innovation: learning from COVID-19

Design of reverse logistics networks under uncertainty: Multi-objective approach

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