Optimisation of containerised air cargo forwarding plans considering a hub consolidation process with cargo loading
针对需求不确定的航空货运，建立两阶段随机规划模型优化集装箱预订与整合计划，最小化预期总成本，并设计遗传算法求解。
Air cargo plays an important role in supporting global supply chains; this becomes more vital when facing uncertainties in a crisis such as the COVID-19 pandemic. This motivates our study on air cargo forwarding plans, considering demand uncertainties and economic conditions. Cargos are placed into air containers based on weights and volumes, and then flown from regional collection points into a hub, for consolidation before transporting to onward destinations. Decisions are made in advance by cargo forwarders as to the containers to book, both in regions and in the hub, since airlines offer discounts on containers booked in advance; however, cargo quantities are uncertain when advance bookings are made. We develop a two-stage stochastic programming model, where the first stage determines both the quantities and types of air containers to book; the second stage deals with ordering any extra containers, at higher cost, or returning unused containers, as well as making loading and consolidation plans. The objective is to minimise the total expected costs. We then extend it into a multistage case and design a genetic algorithm as the solution method. Experimental results demonstrate that the proposed approaches provide a cost-efficient plan and responsive to demand as it arises.