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.

Ambient temperature has been identified as the most common reason for quality loss of most high value perishable goods. In actual operation, it is not cost-effective to cool perishable products in active and/or passive containers when their storage time in the warehouse is short. In order to o reduce the quality loss caused by the temperature, this research proposed a control strategy to find suitable temporary storage locations of the warehouse in advance for different types of perishable goods. The control strategy consists of indoor temperature prediction model, quality loss model and location choice model. By comparing with the actual case data, it is found that when the indoor average temperature increases, the proposed control strategy can significantly reduce the quality loss.

Airports are trying to increase their airside efficiency by implementing the concept of Airport Collaborative Decision Making (A-CDM). At the heart of A-CDM lies the Target Off-Block Time (TOBT). The TOBT is the time at which the aircraft is estimated to be ready, with all doors closed and boarding bridges removed. The
TOBT is used by other airport stakeholders to plan their activities. The TOBT is updated by the turnaround coordinator based on the information he has on the progress of the turnaround processes such as catering, cleaning, fuelling and boarding of passengers. However, a large part of these TOBT updates take place within the last 10 minutes of the turnaround. These last minute changes can cause disruptions to the schedules of many stakeholders at the airport, which makes it less efficient. The goal of this research is to get a better understanding of how turnaround coordinators manage the TOBT and explore how the information position of turnaround coordinators managing the TOBT can be improved.

Crowdshipping has emerged as an avenue to provide sustainable option for the
future of urban package delivery. The main challenge in crowdshipping realm is to maintain sustainable network of customers (demand) and couriers (supply). To help resolving the challenge, this research offers a comprehensive approach to examine market potential of bicycle crowdshipping through concurrent evaluation of its supply and demand side, which thus far has been absent from the literature. By deploying discrete choice models, the study generated supply, demand, and market equilibrium model to aid crowdshipping platform in understanding influential service attributes and its effect to the market penetration level. The research concluded adjustable delivery time window as a key differentiating attribute to entice customer preference towards crowdshipping. According to the model. demand side of bicycle crowdshipping was found to be highly elastic towards price, while oppositely supply side of crowdshipping is rather inelastic towards monetary compensation.