A Bi-Objective Green Truck Routing and Scheduling Problem in a Cross Dock with Learning Effect: Archived Multi-Objective Simulated Annealing

This paper presents a bi-objective model for a green truck scheduling and routing problem at a cross-docking system. This model determines three key decisions at the cross dock as follows: 1) defining a sequence and schedule of inbound trucks at the receiving door, 2) specifying a sequence and schedule of outbound trucks at the shipping door, and 3) determining the routes of the outbound truck while serving customers. The first objective function is related to responsiveness of the network that minimizes time window violations and the second objective function minimizes total fuel consumption of trucks in order to consider the environmental factor of the network. Also, a learning effect is considered in loading and unloading process times. To solve the bi-objective model, an archived multi-objective simulated annealing (AMOSA) is used and modified. Finally, 10 test problems are solved and the efficiency of the proposed AMOSA is compared with an ɛ-constraint method.

Keywords:

Green truck routing and scheduling , Cross docking , Learning effect , Meta-heuristic algorithm