Carbon reduction in the location routing problem with heterogeneous fleet, simultaneous pickup-delivery and time windows

Abstract

Along with the concerns about global climate change, more and more researchers pay attention to the low carbon logistics. Since the logistics industry is an important source of carbon emissions, it is becoming more and more important to reduce carbon emissions in logistics operation. In this paper, we study a low carbon for location routing problem with heterogeneous fleet, simultaneous pickup-delivery and time windows and design a two-phased hybrid heuristic algorithm to solve the problem. Firstly, we introduce the concept of temporal-spatial distance and use genetic algorithm to cluster the customer points to construct the initial path. Then, we use variable neighborhood search algorithm for local search. By incorporating the idea of simulated annealing algorithm into the framework of variable neighborhood algorithm, the global optimization ability of the algorithm is improved. At the same time, the vehicle adjustment strategy is added in the optimization process. The computational experiments are implemented to investigate the performance of the proposed heuristic algorithm. Computational results show that the initial solution considering temporal-spatial distance has obvious advantages in the efficiency of the algorithm and the quality of the solution.