基于混合大灭绝粒子群算法的座位控制模型

Hybrid Particle Swarm Optimizer with Mass Extinction Algorithm for Airline Seat Inventory Control

座位优化控制是航空公司增加利润的有效方法。基于旅客的需求预测,可以利用数学规划模型为不同的航段和票价组合计算座位销售上限或者销售竞价,从而达到单个航班收入最大化的目的。常用的方法可分为确定模型和概率模型,但是对多航段多舱位的优化问题,由于出现了复杂的组合和约束,这些模型必须简化。提出了基于混合大灭绝粒子群算法的座位优化控制模型,并和常用的优化方法进行了仿真对比。研究结果表明,混合大灭绝粒子群算法应用于座位优化,可得到满意的解,同时,该算法简化了复杂的约束关系,易于实现,具有明显的优势。

Airline seat inventory control is a very profitable tool in the airline industries. Mathematical programming models provide booking limits or bid -prices for all itineraries and fare classes based on demand forecasts. The general models include deterministic approximation methods and probabilistic approximation methods, but these models are hard to solve them if the number of decision variables and constraints is large. We present a new model for seat inventory control based on a genetic algorithm in this paper, and simulating results was compared between the new model and general models. Study results shows that the hybrid particle swarm optimizer with mass extinction is profitable for seat inventory control, and it is easy to implement.