多式联运网络中的托盘共用调度方法

为解决多式联运网络中的托盘共用调度决策难题，采用非线性规划、混合整数规划和改进粒子群算法（particle swarm optimization，PSO）等方法优化了托盘共用系统，研究了在管理者可以选择水路、公路和铁路3种运输模式运输托盘时，管理者应采取的调度方法. 首先分析了多式联运网络中的托盘共用调度流程；其次构建了多式联运网络中的托盘共用调度混合整数非线性规划模型，模型中考虑了转运时间、转运成本、作业批量、托盘毁坏率、时间约束等因素；然后设计了带惯性因子的粒子群算法 （PSO with inertia weigh，IPSO） 和带压缩因子的粒子群算法 （PSO with constriction factor，CPSO）；最后通过实验分析验证了模型和算法的有效性. 研究结果表明:较单一的公路运输，采用多式联运网络中的托盘共用调度模型总成本降低了69.1%；与CPSO相比，IPSO得到的调度总成本降低了1.82%；客户的期望满足时间越宽松时，采用多式联运的方式调度托盘越能节约调度成本；多式联运所耗费的转运时间和转运成本直接影响托盘共用调度决策. 

Methodologies of Pallet Allocation Over Pallet Pool in Intermodal Transportation Network

In order to solve the problems in pallet allocation of intermodal transportation network (ITN) over a pallet pool, by the use of non-linear programming, mixed-integer programming, and modified particle swarm optimization (PSO), the pallet allocation system were optimized in the cases of three available transport modes, i.e., shipping, highway, and railway. Firstly, the pallet allocation process was analyzed. Then, a mixed-integer non-linear optimization model for pallet allocation was built, in which the transit time, transit cost, handling batch, ratio of damaged pallets and time constraints were considered. Furthermore, the particle swarm optimization with inertia weigh (IPSO) and the particle swarm optimization with constriction factor (CPSO) were designed. Finally, the model and algorithm were verified by tests. The results show that compared with pallet transport through highway, the allocation cost could be reduced by 69.1% with pallet allocation in ITN. The IPSO outperforms the CPSO with the total allocation cost reduced by 1.82%. The larger the lead time of customer demand is, the more allocation cost can be saved by intermodal transportation. The transit time and cost of intermodal transportation affect pallet allocation decisions over a pallet pool.