A Parallel Monte-Carlo Tree Search-Based Metaheuristic For Optimal Fleet Composition Considering Vehicle Routing Using Branch & Bound

In this paper, a Monte-Carlo Tree Search (MCTS)-based metaheuristic is developed that guides a Branch & Bound (B&B) algorithm to find the globally optimal solution to the heterogeneous fleet composition problem while considering vehicle routing. Fleet Size and Mix Vehicle Routing Problem with Time Windows (FSMVRPTW). The metaheuristic and exact algorithms are implemented in a parallel hybrid optimization algorithm where the metaheuristic rapidly finds feasible solutions that provide candidate upper bounds for the B&B algorithm which runs simultaneously. The MCTS additionally provides a candidate fleet composition to initiate the B&B search. Experiments show that the proposed approach results in significant improvements in computation time and convergence to the optimal solution.Comment: Submitted to the IEEE Intelligent Vehicles Symposium 202

Hybrid Optimization Algorithm for Vehicle Routing Problem with Simultaneous Delivery-Pickup

In order to provide reasonable and effective decision support for logistics enterprises in vehicle distribu-tion path planning, this paper studies the vehicle routing problem with simultaneous delivery-pickup and time windows (VRPSDPTW) for single distribution center distribution mode, and establishes a mathematical model with the objective of minimizing the total distribution cost. According to the characteristics of the model, a hybrid optimization algorithm (SA-ALNS) based on the combination of simulated annealing (SA) and adaptive large-scale neighborhood search (ALNS) is proposed. An insertion heuristic algorithm based on time and distance weighting is used to construct the initial solution of the problem. A variety of delete and insert operators are introduced to optimize the path with adaptive selection strategy. Through the feedback mechanism, the probability of each operator being selected is gradually adjusted to make the algorithm more inclined to choose the operator with better optimization effect. The Metropolis criterion of simulated annealing mechanism is used to control the solution updating. In the simulation experiment, 56 large-scale examples are tested, and other intelligent optimization algori-thms such as p-SA algorithm, DCS algorithm and VNS-BSTS are compared and statistically analyzed. The results show that the algorithm is feasible and superior in solving the vehicle routing problem with simultaneous delivery-pickup and time windows. The research results greatly enrich the related research of vehicle routing problem (VRP)

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

OPTIMIZING VEHICLE ROUTING WITH A HYBRID SWARM-INTELLIGENT FROG JUMPING OPTIMIZATION ALGORITHM

The issues in Vehicle Routing with Time Windows (VR-TW) are addressed in this study using a novel hybrid swarm-intelligent frog jumping optimisation (HSIFJO) algorithm. The method employs a diversity management strategy for developing memeplexes, which assists in preserving diversity and prevents the premature termination of the search. To increase population diversity and improve solution quality, an enhanced clone selection (CS) process is employed. To maximise the algorithm's potential, an enhanced and extended extremal optimisation (EO) strategy is used, coupled with different move operators. A proposed adaptive soft time windows (ASTW) surcharge approach acknowledges the possibility of impractical solutions during the evolution process. When compared to existing state-of-the-art heuristics, the suggested approach performs exceptionally well in performance evaluation

Optimizing Urban Distribution Routes for Perishable Foods Considering Carbon Emission Reduction

The increasing demand for urban distribution increases the number of transportation vehicles which intensifies the congestion of urban traffic and leads to a lot of carbon emissions. This paper focuses on carbon emission reduction in urban distribution, taking perishable foods as the object. It carries out optimization analysis of urban distribution routes to explore the impact of low carbon policy on urban distribution routes planning. On the base of analysis of the cost components and corresponding constraints of urban distribution, two optimization models of urban distribution route with and without carbon emissions cost are constructed, and fuel quantity related to cost and carbon emissions in the model is calculated based on traffic speed, vehicle fuel quantity and passable time period of distribution. Then an improved algorithm which combines genetic algorithm and tabu search algorithm is designed to solve models. Moreover, an analysis of the influence of carbon tax price is also carried out. It is concluded that in the process of urban distribution based on the actual network information, the path optimization considering the low carbon factor can effectively reduce the distribution process of CO2, and reduce the total cost of the enterprise and society, thus achieving greater social benefits at a lower cost. In addition, the government can encourage low-carbon distribution by rationally adjusting the price of carbon tax to achieve a higher social benefit

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid