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The main goal of this research is to combine the dynamic vehicle routing problem (DVRP), in which new information is received along the day, and the green vehicle routing problem (GVRP), which determines the routes of vehicles to serve a set of customers, while minimizing the total amount of the greenhouse gas. In this study, the customers must be serviced at their locations by a set of vehicles in real time by minimizing the carbon dioxide CO2. For that, the authors present the technique employed to estimate the amount of CO2 emissions and integrate it into the proposed mathematic model. Then they develop the resolution approach based on ant colony optimization (ACO) algorithm and a large neighborhood search (LNS) algorithm. The effectiveness of this approach is tested on a set of the static and dynamic green problems instances.

Logistics sustainability is increasingly becoming a central focus of businesses, when most societies are aware of the influence of industry on both the environment and human health. To address the drawbacks of the way logistics systems have been designed, a new logistics system called Physical Internet has been proposed. This system relies on the creation of hyperconnected logistics systems. It aims to improve in an order of magnitude the way physical objects are transported, handled, stored, supplied, realized, and used to be more sustainable and efficient economically, environmentally, and socially. This paper focuses on the product realization using a hyperconnected mobile production mode in the context of Physical Internet, an open global logistics system. It addresses its dynamic deployment of production modules and resource allocation and sharing. It then proposes a make-to-order bi-objective optimization model which minimizes costs and greenhouse gases (GHGs) related to the product realization of a manufacturer to serve its customers given the availability of the open fabs. Experimental results are presented to identify the computational performance of the established model, as well as the economic and environmental benefits of using the facilities enabled by the PI. Finally, it concludes and provides directions for future research.

The Vehicle Routing Problem (VRP) is a complex and high-level set of routing problems. Two of its important variants are the Dynamic Vehicle Routing Problem (DVRP) and the Green Vehicle Routing Problem (GVRP). The first one has become a challenging research topic in the last two decades, in which not all informations are known in advance, but are revealed as the system progresses. The second one is seen as a new application and solution in new logistics patterns, more specifically, for finding routes of vehicles to serve a set of customers while minimizing the total amount of CO 2 emissions, by increasing the loading rate and reducing the number of empty trips could reduce from 10% to 40% km traveled and therefore CO 2 emissions [1]. In this paper, we combine these two variants (DVRP and GVRP), where we try to minimize, in the dynamic environment, the greenhouse gas especially the carbon dioxide CO 2 , which is the immediate consequence of the depletion of the ozone layer and time duration. We will call this combination the Dynamic Green Vehicle Routing Problem (DGVRP). To study this new problem, we present the technique employed to estimate the amount of CO 2 emissions, the emission matrix and integrate them into the DGVRP model, then we propose the hybridization based on Ant Colony Optimization (ACO) algorithm with a Large Neighborhood Search (LNS) algorithm to solve our problem. The effectiveness of this approach is tested on a set of the dynamic green problems instances, which are adopted in this work from the static GVRP benchmark datasets.