Bi-objective optimization approach to the design and planning of supply chains: Economic versus environmental performances

Pinto‐Varela, Tânia; Barbosa‐Póvoa, Ana Paula; Novais, Augusto Q.

doi:10.1016/j.compchemeng.2011.03.009

Cited by 123 publications

(39 citation statements)

References 19 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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“…This set of solutions are non-dominated, thus no solution is able to increase a performance measure without decreasing at least one of the other objectives at the same time. (Pinto-Varela et al 2011;Brandenburg et al 2014). …”

Section: Biofuel Green Supply Chainsmentioning

confidence: 96%

Multi-objective Optimisation Incorporating Life Cycle Assessment. A Case Study of Biofuels Supply Chain Design

Páez

Guillén‐Gosálbez

2016

Alternative Energy Sources and Technologies

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This chapter describes how to optimise energy systems considering their economic and environmental performance simultaneously. To this end, we follow an approach that combines life cycle assessment with multi-objective optimisation. We illustrate how to apply such a framework to the strategic design and planning of biofuel supply chains. The problem is formulated in mathematical terms as a multi-objective mixed-integer linear programme. The aim of the design/planning task is to maximise the net present value while the environmental impact is minimised simultaneously. Eco-indicator 99 is the life cycle assessment methodology incorporated in the model to quantify the environmental damage. The implementation of the algorithm in a case study based on the Argentine industry reveals the conflictive trade-off between economic and environmental objectives. The proposed framework provides valuable insight into the incidence of key operational features in the optimal biofuel supply chain network.

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“…For this purpose we have reviewed database libraries of ''Science Direct'', ''Web of Science'' and ''IEEE'' for bibliographic references of the last decade (Chen and Lee 2004;Xie et al 2006;Yugang and Guang 2006;Ozkarahan 2006, 2008;Wang and Shu 2007;Aliev et al 2007;Chen et al 2007;Xu et al , 2009Jing-min et al 2008;Petrovic et al 2008;Zhai 2008, 2010;Liu et al 2008;Yuansheng et al 2008;Handfield et al 2009;Gong et al 2009;Lau et al 2009;Ghatee and Hashemi 2009;Peidro et al 2009;Mahnam et al 2009;Gumus et al 2009;Mitra et al 2009;Pishvaee and Torabi 2010;Miller and John 2010;Bilgen 2010;Qin and Ji 2010;Wong 2010;Mula et al 2010;Pinto-Varela et al 2011;Jazemi et al 2011;Kabak and Ülengin 2011;Chandran and Kandaswamy 2012;Kubat and Yuce 2012;Björk 2012;Zheng and Ling 2013;Pishvaee and Razmi 2012;Liang 2012;Nepal et al 2012;Vahdani et al 2012;Makkar et al 2011;Arikan 2013;Özkır and Bas ßlıgil 2013). The journals which are corresponded in this search and could be iden...…”

Section: Introductionmentioning

confidence: 99%

Recent Models and Solution Methodologies for Optimization Problems in Supply Chain Management Under Fuzziness

Yanık

Altay

2014

Supply Chain Management Under Fuzziness

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Supply chain (SC) involves collaborating with business partners which uniquely specialize on only a few key strategic activities. The network structures formed in SC's have emerged in the last decade with the accelerated developments in globalization, outsourcing and information technology. The complex network structures have introduced novel problems to both industry and academia while traditional complications are yet investigated. The intensification points of SC problems are mainly configuration of distribution networks, forming distribution strategies, trade-off analyses, managing inventory and cash-flow. One of the main challenges in modeling and solving these problems is to deal with the uncertainties involved in the complex nature of SC. Demand has been the main uncertain aspect of the problems of the related literature followed by internal parameters, supplier related parameters, environmental parameters and price. The uncertainty issues have been commonly dealt with fuzzy approaches in the literature. Fuzzy approaches become beneficial under uncertainties such as the absence of data, use of qualitative data or the need for subjective judgments. Hence, fuzzy techniques in SC optimization problems are vastly implemented in the literature. The purpose of this study is basically to summarize the fuzzy techniques employed for SC optimization models, their past applications, solutions algorithms and offer directions for future research.

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Bi-objective optimization approach to the design and planning of supply chains: Economic versus environmental performances

Cited by 123 publications

References 19 publications

Green and Reverse Logistics Management Under Fuzziness

Green and Reverse Logistics Management Under Fuzziness

Multi-objective Optimisation Incorporating Life Cycle Assessment. A Case Study of Biofuels Supply Chain Design

Recent Models and Solution Methodologies for Optimization Problems in Supply Chain Management Under Fuzziness

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