航空公司机上周转品多基地库存优化模型

在民航业日益激烈的市场竞争环境下，航空公司对成本控制的精细化管理显得尤为重要。本文基于航空公司的实际背景，针对为旅客提供的机上周转品，建立了一个多基地库存系统的库存优化模型。该模型以系统的总库存成本最小为目标，在考虑需求、回收、调运、安全库存等因素下，决策一个订货周期内的订货及调运方案。基于最优解的分析，在成本参数满足一定条件下，可以得到系统最优的期初订货量；在此基础上，可以将该模型转化为一个网络流模型，参考最小费用最大流算法，设计了一个多项式求解算法来求解该网络流模型，进而得到各基地之间的最优调运量，并证明该算法的最优性；此外，根据期初订货量可行解的范围，设计了一个求解原库存模型的启发式算法。通过实例分析，验证了该模型与两种算法的有效性。

Optimization Model of Airline in-flight Reusable Items Inventory in Multi-base System

In the increasingly fierce competition environment of civil aviation, the delicacy management of cost control is very important for airlines. The in-flight reusable items are provided for passengers and can use again after cleaning. The inventory of these items is imbalanced in some base locations because of the variation of in-flight meals and the different number of passengers in round-trip flights.So under the demand and return of in-flight reusable items in each base is given, an inventory model of in-flight reusable items is established in a multi-base inventory system and an order and transportation scheme is made, to minimize the total inventory cost (including ordering cost, holding cost, transshipment costand recovery cost) in an ordering cycle. Based on the analysis of the optimal solution of the model, it can be found the optimal total order quantity of the inventory system at the beginning of the period under certain conditions,and the inventory model can also be transformed into a network flow model, then a polynomial algorithm is given to solve thenetwork flow model and to find the optimal transshipment quantity of the system, and the optimality of the algorithm is proved; According to the scope of the feasible solution of the total order quantity, a heuristic algorithm is also given to solve the inventory model. The actual data of in-flight reusable items of a large airline are taken. The validity of themodel and algorithm is tested and verified by the results. And finally, the inventory problem of in-flight reusable items still needs further research, such as considering stochastic demand and the multi-period.