Optimized management of green supply chains by the use of Ant Colonies multi-objective algorithm: The integration of the economic, environmental and social impacts of multimodal transport

In a global context marked by climate change and by peak globalization materializing through highly interconnected and interdependent chains of value. Our subject is naturally aligned in a global dynamic characterized by existential issues and public-level challenges. This motivates us to contribute in this global reflection by proposing concrete and viable responses and measures to safeguard our planet and to perpetuate our heritage for future generations. It is our duty and collective responsibility. Indeed, the balance between environmental, economic and social impacts has become the optimum objective in all contemporary strategies, policies, actions and decisions. This balance is critical in logistics and transport field because it is one of the largest emitters of greenhouse gases directly causing the acceleration of global warming. And also because it's one of the most crucial and sensitive sectors for mankind due to the fact that our survival and comfort depend on it! Our aim is to build a decision aid tool in order to optimally choose a route (also called path) in a multimodal transport network of goods and/or passengers. The system should be as efficient as possible: with a path that causes the least damage and aggression to the environment while being economically and socially beneficial to man. For a multimodal transport network, a mathematical model is established in order to calculate the ecological and socio-economic criteria to be considered‥. Then a multi-objective optimization algorithm is built to find the shortest path by optimizing the defined criteria: An ant colony algorithm is chosen because it is the most optimal and efficient in a complex scenario that takes into account a large number of variable parameters and criteria. Naturally, an implementation on a multimodal transport network is carried out in order to assess the algorithm's performances. Finally, problematic questions are asked in order to incite reflection and explore future perspectives. And because of the subject's richness, it can be used as a starting point for further development and expansion.