Using Multi Agent Systems in Green Supply Chain
Mohamed Dif El Idrissi1, Abdelkabir Charkaoui2, Abdelwahed Echchatbi3
1Mohamed Dif El Idrissi*, Mechanical, Industrial Management and Innovation Laboratory, Faculty of Sciences and Technologies, University Hassan First, Settat Morocco.
2Abdelkabir Charkaoui, Mechanical, Industrial Management and Innovation Laboratory, Faculty of Sciences Technologies, University Hassan First, Settat, Morocco.
3Abdelwahed Echchatbi, Mechanical, Industrial Management and Innovation Laboratory, Faculty of Sciences and Technologies, University Hassan First, Settat, Morocco.
Manuscript received on March 28, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 1827-1830 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8019049420/2020©BEIESP | DOI: 10.35940/ijeat.D8019.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Green Supply Chain Management (GSCM) is one the key concepts that organizations need to implement in order to reduce the environmental impact of their activities due to the increase of environmental awareness. However, its implementation is full of constraints in terms of the coordination and collaboration between all stakeholders. Therefore, many GSCM models and frameworks, like the multi agent systems (MAS) are proposed to reduce the complexity level in the Supply Chain. The main objective of this paper is to study the multi agent systems usage for GSCM modelling. The used research methodology is based on a literature review of 50 articles published between 2012 and 2019 in 10 different journals. 5 articles are selected out of 50 because of their accordance with the research subject. The contribution of this paper is to show the benefits of using multi agents systems for GSCM modelling problems.
Keywords: Supply Chain Management, Green Supply Chain Management, Multi agent systems.