The rapid increase in urbanization results in an increase in the volume of municipal solid waste produced every day, causing overflow of the garbage cans and thus distorting the city’s appearance; for this and environmental reasons, smart cities involve the use of modern technologies for intelligent and efficient waste management. Smart bins in urban environments contain sensors that measure the status of containers in real-time and trigger wireless alarms if the container reaches a predetermined threshold, and then communicate the information to the operations center, which then sends vehicles to collect the waste from the selected stations in order to collect a significant waste amount and reduce transportation costs. In this article, we will address the issue of the Dynamic Multi-Compartmental Vehicle Routing Problem (DM-CVRP) for selective and intelligent waste collection. This problem is summarized as a linear mathematical programming model to define optimal dynamic routes to minimize the total cost, which are the transportation costs and the penalty costs caused by exceeding the bin capacity. The hybridized genetic algorithm (GA) is proposed to solve this problem, and the effectiveness of the proposed approach is verified by extensive numerical experiments on instances given by Valorsul, with some modifications to adapt these data to our problem. Then we were able to ensure the effectiveness of our approach based on the results in the static and dynamic cases, which are very encouraging.

High transportation costs and poor quality of service are common vulnerabilities in various logistics networks, especially in food distribution. Here we propose a many-objective Customer-centric Perishable Food Distribution Problem that focuses on the cost, the quality of the product, and the service level improvement by considering not only time windows but also the customers’ target time and their priority. Recognizing the difficulty of solving such model, we propose a General Variable Neighborhood Search (GVNS) metaheuristic based approach that allows to efficiently solve a subproblem while allowing us to obtain a set of solutions. These solutions are evaluated over some non-optimized criteria and then ranked using an a posteriori approach that requires minimal information about decision maker preferences. The computational results show (a) GVNS achieved same quality solutions as an exact solver (CPLEX) in the subproblem; (b) GVNS can generate a wide number of candidate solutions, and (c) the use of the a posteriori approach makes easy to generate different decision maker profiles which in turn allows to obtain different rankings of the solutions.

In this paper, we propose a new approach to dynamic wireless charging that allows electric vehicles to charge wirelessly while in motion in both lanes on highways. The challenge is to locate the charging infrastructure on a highway between origin O and destination S (round trip) with heterogeneous battery vehicles, where each type of vehicle requires its allocation of charging segments on the road. We aim to ensure that each type of vehicle can complete a round trip without running out of battery charge while minimizing the number of charging segments and inverters on the road by studying both lanes simultaneously. We model the problem mathematically and validate it using a CPLEX optimizer for limited instances. Finally, we solve the problem using a hybrid approach that combines genetic algorithms and local search techniques to balance diversification and intensification. We have significantly improved the results found in the literature by reducing the number of inverters, which are expensive components in the charging infrastructure. Our approach takes advantage of utilizing a single inverter for both lanes of the highway, leading to cost savings and improved efficiency.