This study investigates the dynamic-demand green vehicle routing problem with soft time windows (DDGVRPSTW). A two-stage optimization model is developed to minimize total distribution cost, including vehicle operating cost, fixed dispatch cost, fuel consumption cost, carbon emission cost, and time window penalty cost. To solve the model, a hybrid artificial bee colony state-transition algorithm (HABC-STA) is proposed. In the pre-optimization stage, multiple initial routes are generated and refined to obtain an initial distribution plan. In the dynamic optimization stage, customer information is updated at a specified event time, and four state-transition operators are used to search the neighborhood of the current solution and generate a revised routing plan with lower cost. Computational results on Solomon benchmark instances and a real-world case study show that the proposed method effectively reduces both total cost and environmental cost. The results also indicate that selecting an appropriate distribution scheme can significantly reduce fuel consumption and carbon emissions while improving overall routing efficiency.
For the recycling of waste power batteries, a multi-depot soft-time-window vehicle routing problem model was established to minimize fixed costs, transportation costs, and time-window penalty costs. An improved genetic algorithm was designed that integrates adaptive crossover/mutation with 2-opt local search. Compared to the current planning scheme, the optimization solution based on real-world data reduces total costs by 13.7%, shrinks the fleet size by 14.3%, and increases the on-time appointment rate by 3.8 percentage points. Across 30 independent runs, the proposed algorithm achieved the lowest average total cost, the smallest standard deviation, and the shortest computation time, outperforming the standard genetic algorithm and simulated annealing algorithm, thereby validating the necessity of incorporating the improved strategy.
Li-Qun Hu, Ye Tu, En-You Lin et al.· International Conference on...· 0 citations
The vehicle routing optimization model under the customer loss mechanism is established with the objective of minimizing the sum of vehicle fixed costs, variable routing costs, and time window penalty costs and an improved genetic algorithm is employed to solve this model.
This study investigates the collaborative green vehicle routing problem with time-dependent travel speeds (CGVRP-TD), which integrates horizontal collaboration among multiple depots with time-dependent traffic conditions. The problem jointly optimizes customer allocation, vehicle routing, and departure-time decisions to minimize transportation-related carbon emissions subject to vehicle capacity and customer time-window constraints. We formulate the CGVRP-TD as a mixed-integer programming model and develop a two-phase adaptive large neighborhood search algorithm with embedded departure-time optimization. The first phase explores routing and customer-assignment decisions using problem-specific operators, including two speed-related removal operators, while the second phase applies exact departure-time optimization to fixed routes. Computational experiments show that the proposed algorithm obtains high-quality solutions efficiently and that both departure-time optimization and speed-related operators contribute to emission reduction. The results further demonstrate that combining horizontal collaboration with time-dependent travel-speed information can substantially reduce transportation emissions while preserving on-time service. We also discuss emission-savings allocation mechanisms for sustaining collaboration among participating depots.
Juan Li, Yang Yu, Min Huang et al.· Mathematics· 0 citations
The findings demonstrate that metaheuristic techniques consistently outperform traditional algorithms in complicated, constraint-rich situations and emphasize the need of cost-effective, data-driven metaheuristic optimization in current logistics planning.
K. Khaw, C. Tan· International Journal on Rob...· 0 citations