Dataset for A Bi-Objective Optimization VRP Model for Cold Chain Logistics Enhancing Cost Efficiency and Customer Satisfaction study case
With the rapid increase in demand for fresh products, cold chain logistics has become an important mode of transportation. Logistics enterprises are faced with the problem of cost control and improvement of customer satisfaction. In light of this, we present a bi-objective optimization vehicle routing problem model in cold chain logistics, which aims to reduce the total costs and improve customer satisfaction. To solve the intricate model, we propose a hybrid algorithm called the Simulated Annealing Non-dominated Sorting Genetic Algorithm II (SA-NSGA-II) algorithm, which amalgamates the simulated annealing algorithm with the NSGA-II algorithm, enabling efficient resolution of the bi-objective problem. Extensive numerical experiments validate the effectiveness of the proposed model and algorithm by exhibiting solutions with lower costs and higher satisfaction levels. Furthermore, we conduct a sensitivity analysis to explore the impact of vehicle speed on both costs and satisfaction, shedding light on the trade-offs between various optimization objectives.
This data set is adapted from the classic Solomon data set, but the time window is expanded.
- Dataset for A Bi-Objective Optimization VRP Model for Cold Chain Logistics Enhancing Cost Efficiency and Customer Satisfaction study case.xlsx (14.82 kB)