An Improved Optimization Method to Increase the Truck Planning Efficiency of Landside Air Cargo

Abstract

Congestion in the landside air cargo supply chain occurs for different concurring reasons. Lack of coordination between freight forwarders, as example, might create truck congestion on the ground handler side. Horizontal collaboration between forwarders can be introduced and modeled via mathematical programming to mitigate congestion. Resulting models, which are generally variations of Pickup and Delivery Problem with Time Windows (PDPTW), can be solved to optimality only for small-size instances, and the computation is generally time consuming. We therefore propose a simulated annealing (SA)-embedded adaptive large neighborhood search (ALNS) heuristic to address truck route planning in the landside air cargo supply chain. In this work, we allow the search to visit infeasible time-dependent solutions. Accordingly, the objective function minimizes the feasible solution, where total travel distance cost, total travel time cost and unassigned shipments cost, and the time-dependent violation costs. Computational results are reported for 10 instances that were also solved with a mathematical programming approach. Results shows that the meta-heuristic method performs equally or better than the mathematical model given a computational limit for the latter of 2 hours. In addition, the meta-heuristic method was able to find a feasible solution for those cases where the exact model failed to identify a feasible solution.