| Grant number: | 14/27334-9 |
| Support Opportunities: | Scholarships in Brazil - Master |
| Start date: | August 01, 2015 |
| End date: | April 30, 2017 |
| Field of knowledge: | Engineering - Production Engineering - Operational Research |
| Agreement: | Coordination of Improvement of Higher Education Personnel (CAPES) |
| Principal Investigator: | Mariá Cristina Vasconcelos Nascimento Rosset |
| Grantee: | Camila Pereira dos Santos Tautenhain |
| Host Institution: | Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil |
| Associated research grant: | 10/10133-0 - Cutting, packing, lot-sizing and scheduling problems and their integration in industrial and logistics settings, AP.TEM |
| Associated scholarship(s): | 16/02203-4 - A study of sustainable supply chain management problems, BE.EP.MS |
Abstract The sustainable supply chain management involves the study of all stages related to the production of a product, in order to maximize an economic criterion such as, for example, the profit. The production process of a product, however, generates environmental impacts, such as, for example, energy use, water use and greenhouse gas emissions. Nevertheless, traditionally, the supply chain management does not take into account the environmental impacts caused by the production process. However, customers and governments have increasingly invested in sustainability, one of the reasons for the arise of the sustainable supply chains. The sustainable supply chain management problem aims at, besides the maximization of the economic criterion, the minimization of the environmental impact caused during the production process. In this project, it is intended to present a study and to develop an method for minimizing the environmental impact and maximizing the economic aspect. In this study, the environmental impact will be evaluated by the greenhouse gas emissions, by the amount of carbon dioxide emitted. It is expected that the proposed algorithm, in addition to being efficient, finds viable solutions to case studies also competitive with the state-of-art. (AU) | |
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