Solving a low-carbon routing problem for perishable distribution food

Abstract

This research article presents a low-carbon and environmental protection viewpoint that combines the idea of a cold chain. In this case, the optimisation problem Food distribution system is solved by developing a low-carbon Routing Problem (LRP) model. The researchers seek to identify the minimum costs and carbon emission costs while Carbon tax policies are also presented to examine their effects on carbon emissions. The model is solved by using a hybrid genetic algorithm with heuristic rules, after which the algorithm’s efficiency is tested with a globally recognised Proudhon data set. The Cycle evolutionary algorithm can quickly and effectively seek the optimal solution of the model. We combine two kinds of operators, inversion mutation and insertion mutation, in the genetic algorithm to form the combined operator of a hybrid genetic algorithm. Then, the approximate optimal solution is obtained. Meanwhile, many experiments are carried out by setting different carbon tax values and the critical interval values of carbon emissions and optimal. A case study from a third-party logistics company is used to test the applicability of the model in the real world. Results showed that the model and carbon tax policies will benefit business operations and the environment.

Keywords

• Logistics distribution
• location-routing problem
• genetic algorithm
• cycle evolutionary algorithm
• vehicle routing problem