Research on job integration of multi-agent in multimodal transportation with time windows

This study investigates the optimal model of Job Integration of Multi-agent in Multimodal Transportations with Time Windows (JIMMTTW). The model is formulated firstly using the graph structure. The problem is NP-Hard. An optimal strategy including two layers is proposed after analysing the characteristics of the formulated model. At the first layer, a combination of shift move, swap move and loop move is adopted to effectively assign jobs to agents. At the second layer, a genetic algorithm based on Taguchi method is adopted to find the optimal route with multimodal transportations through K-shortest paths. A numerical example with different time windows is performed to validate the formulated model and the proposed algorithm. The results indicate that the integration of jobs can only be carried out among several jobs for the same agent due to the effect of time window, and the total cost will increase with the restraint increment of time window, which is similar to the practical application. The results also demonstrate that the proposed algorithm can effectively and efficiently solve the JIMMTTW.