Abstract
ABSTRACT Sustainable Supply Chain is nowadays an increasing area of concern for both academia and industry that aims to design, plan and operate supply chains that guarantee market needs while considering not only profit objectives but also environmental and social concerns in a solution of compromise. Being supply chains complex systems the pursuing of such compromise solution calls for the use of decision tools that can support decision makers. Such tools should be based on quantitative models where Operational Research (OR) methods ought to be explored and where optimization, in particular, has a role to play. In this paper, the main characteristics of such optimization models, focusing on the design and planning of sustainable supply chains are discussed and the main issues to be considered when addressing such problems are identified. A framework, SusFrame, developed to guide practitioners and researchers in the development of optimization models for the design and planning of sustainable supply chains, is presented and applied to the solution of a set of case-studies, demonstrating the large applicability of such tool and how optimization methods can be used to help the sustainable supply chain decision process.
Highlights
Chains play a vital role in companies’ performance
Mota et al (2015) propose a closed-loop supply chain multi-objective model which integrates the three pillars of sustainability: the economic pillar is assessed through supply chain costs, the environmental pillar is assessed by applying ReCiPe LCA method to transportation and entity installation activities, and the social pillar is assessed through a developed indicator related with population density
ToBLoOM, a developed optimization-based tool, is in line with this concern and its overall goal is to determine simultaneously the supply chain network and the associated planning decisions, while assuring a solution of compromise that accounts for the three sustainability objectives: economic, environmental and social (Mota et al, 2018)
Summary
Chains play a vital role in companies’ performance. With today’s level of competitiveness, a strong, innovative product or service is not enough for a company to succeed. In order to support such decisions quantitative models, appear as powerful decision support tools to address supply chain complex problems since they allow a better understanding of the interactions, dynamics and trade-offs of the different variables involved (Cardoso et al, 2013; Dekker & Fleischmann, 2004; Mota et al, 2015b, 2018; Salema et al, 2010). Such models can be embedded in software systems so as to automate and optimize the described decisions.
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