Companies worldwide are nowadays facing a growing pressure to reduce environmental and social impacts of their production and distribution activities. However, the strategies employed to achieve these goals are not clearly defined because of the (very often) conflicting relations with financial goals (i.e., economic dimension). These three dimensions of performance measurement are used in the literature to categorise sustainability. The management of the sustainable enterprise can be understood as a decision-making process looking to simultaneously achieve these three goals. As a consequence, accurate methodologies are required to help decision-makers designing the most economically-, socially- and environmentally-efficient supply chain. This paper presents a quantitative methodology, based on mixed-integer linear programming (MILP), for the inclusion of sustainability metrics in the design of supply chains. Numerical results show the impact that valuation of the objective function (each of the three dimensions of sustainability) may have on the final supply chain design decision. These results are promising and open opportunities for further research.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Designing sustainable supply chains based on the Triple Bottom Line approach


    Contributors:


    Publication date :

    2015-05-01


    Size :

    584933 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Die "triple bottom line"

    Moo, André von | IuD Bahn | 1998


    Designing Around the Bottom Line

    Ballantyne, S. | British Library Conference Proceedings | 1994



    Meta Data for the Transport Triple Bottom Line

    Smith, N. / Trinidad, G. / Salim, V. et al. | British Library Conference Proceedings | 2005


    Sustainability assessment of inland transportation in China: A triple bottom line-based network DEA approach

    Stefaniec, Agnieszka / Hosseini, Keyvan / Xie, Jianhui et al. | Elsevier | 2020