4.7 Article

Sustainable supply chains: An integrated modeling approach under uncertainty

期刊

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.omega.2017.05.006

关键词

Triple bottom line; Sustainability; Closed-loop supply chain; Design and planning; Technology selection; Intermodal transportation; Stochastic optimization

资金

  1. Fundacao para a Ciencia e a Tecnologia (FCT) [PTDC/EMS-SIS/1982/2012, MITPTB/PFM/0005/2013, UID/MAT/00297/2013, SFRH/BD/51947/2012, SFRH/BSAB/128453/2017]
  2. Fundação para a Ciência e a Tecnologia [SFRH/BSAB/128453/2017, SFRH/BD/51947/2012] Funding Source: FCT

向作者/读者索取更多资源

This work presents ToBLoOM - Triple Bottom Line Optimization Modeling, a decision support tool for the design and planning of sustainable supply chains. It consists of a multi-objective mixed integer linear programming model which integrates several interconnected decisions: facility location and capacity determination; supplier selection and purchase levels definition; technology selection and allocation; transportation network definition including both unimodal and intermodal options; supply planning; product recovery and remanufacturing. The three pillars of sustainability are addressed as objective functions: economic, through Net Present Value; environmental through the Life Cycle Analysis methodology ReCiPe; and social through a developed GDP-based metric. Uncertainty is considered using a stochastic ToBloOM. This applied to a case of a European based company with markets in Europe and South America. This work contributes to the literature by building on several identified research gaps such as the need for an integrated approach that allows simultaneous assessment of different interacting supply chain decisions, the need to explicitly assess the environmental impact in closed-loop supply chains, the need to assess the impact of supply chains on society, and the need for a multi-objective tool that includes all the three pillars of sustainability. Strategies towards a more sustainable supply chain are also derived from this work. (C) 2017 Elsevier Ltd. All rights reserved.

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