考虑病人爽约的门诊预约号源分配优化策略研究

我国病人时间观念较差,经常出现预约病人爽约的现象,而采取超订策略可以抵消由此带来的损失。基于此,本文在病人爽约的情况下,建立考虑病人收益、医生加班时间和空闲时间的随机动态规划模型,得到模型的目标函数具有单峰性的特点,并以此为依据,提出门诊预约号源分配优化策略,最后通过数值算例来验证模型的适用性和有效性。     

策略乘客行为下航空供应链竞争协调研究

在已有的航空公司销售供应链研究中,中介代理商只是一个普通的跟随者,甚至有被直销取代的趋势。本文在供大于求且面临策略乘客的竞争市场环境下,研究了由中介主导并决定收益分享比例的双航班竞争销售供应链模式。通过建立双航班竞争的博弈模型,求解出航空公司和中介的收益及其分享比例,并分析了该供应链的有效性。研究结果表明:在乘客购买偏好较小时,该模式对航空公司有利。从收益的视角看,该模式存在的情况下优于直销模式。     

电池置换站运营收益模型与策略

为了使运营收益最大化,本文采用电池置换站的策略,从运营商的角度出发,建立了针对电池置换站运营的整数线性规划模型。该模型综合考虑了置换费用、置换站的电池拥有量、置换站的充放电能力以及置换站服务水平等因素。模型的求解主要采用Lingo编程技术,分析了模型的影响因素,得出了不同参数情况下对应收益的最大值和各置换站在各时间段对电池进行充放电的数量,同时进一步分析这些参数与运营收益之间的敏感关系。通过上述分析可以发现,该模型的提出可以使得运营商的投资风险最小化和收益的最大化,从而为制定合理有效的运营策略和电池管理提供有力指导。     

登记入住概率分布已知条件下酒店客房的超售策略研究

互联网的普及应用,使得酒店客房传统的销售模式不断被网络的销售模式所取代,网络预订已成为酒店客房销售的主要方式。但网络预订需求的不确定性,No-Show现象时常出现,酒店空房率居高不下,直接影响了酒店的营业利润。为提高酒店客房的入住率,酒店管理者往往在预订期内采取超售策略,以弥补因游客No-Show给酒店带来的损失。本文假设旅客登记入住率r概率分布已知情况下,通过最大化期望收益优化处理模型,得出酒店客房最优的超订水平,在此基础上,对酒店最优超订水平的影响因素进行了灵敏度分析。     

基金投资收益的数学模型

本文将基金投资的收益问题最终归结为线性规划问题求解 .鉴于国库券发行的随机性 ,本文分别推导了应用确定型数学模型和概率均值的方法求出了近似公式 ,从而简化了问题的求解 .文章还对误差进行了估计 ,最后 ,根据银行确定利率的原则 ,改进为一个更简单的模型 ,经检验是可行的     

DVD在线租赁

针对网站DVD在线租凭问题，我们建立了以线性规划为基础的单目标、多目标及混合整数规划等多个数学模型，很好的解决了DVD的分配问题。对问题一，我们建立了单目标整数模型，利用lindo数学软件包求解，求出了两种不同情况下DVD的最小购买量：对问题二，首先我们对所给的表格数据进行了规范化处理，采用倒数公式得到了每个人对每种DVD的满意度系数矩阵，然后建立了单目标混合整数规划的数学划模型，为使DVD的购买量最小且会员满意度最大，引进了权重系数ψ，通过加权组合构造了一个新的单目标的混合整数规划模型，最后使用lingo数学软件包求得了每种DVD的最小购买量以及此时DVD的分配方案。     

基于稳定分布的EPGARCH模型

本文对EGARCH模型进行了推广,得到了EPGARCH模型。对该模型的参数估计采用了带约束条件的非线性规划方法。利用直方图、时序图和Q统计量检验等方法对沪深指数收益序列进行了特征分析,得出收益序列具有高峰厚尾和波动聚集性。通过对沪深指数的VaR计算,得到在金融风险度量中基于稳定分布的EPGARCH模型比基于正态分布的EPGARCH模型更加有效。     

自主取消航班下多目标时隙二次分配的改进食物链退火算法

对航空公司在协同决策机制下时隙充分时的自主取消航班问题的研究，能够有效地缓解因不正常航班带来的延误损失。针对此问题，建立了航空公司总延误成本最低及旅客总延误时间最小的多目标模型。在已有改进食物链算法的基础上，提出了改进食物链退火算法，求解了兼顾航空公司和旅客双方利益的Pareto解集。以某机场的不正常航班数据仿真，验证了算法的可行性。最后通过与改进食物链算法对比，证明了改进后的算法得到的解的个数更多并且结果更优。该模型和算法能够为航空公司自主取消航班下时隙二次分配问题做出合理决策提供参考。     

基于轮辐式航线网络的航班舱位控制动态优化

为使航班舱位控制更贴近旅客实际订座需求,有效提高航空公司收益,基于轮辐式航线网络结构的特点,从航班实际运行的角度出发,同时考虑需求的不确定性以及动态性,建立舱位控制动态优化模型。通过模拟仿真得到各订座阶段旅客随机到达数量,运用遗传算法求得各航班舱位等级的座位保护数,根据票价价值进行等级嵌套。结果表明,该方法与单独使用遗传算法的舱位控制方法相比较,能将总收益提高2.35%,具有一定的参考意义。     

基于合作博弈的医联体转诊策略及利益分配方案

针对由同一区域内不同等级的多家医院组成的医联体的双向转诊和利益分配问题，考虑患者的转诊意愿，以期望收益最大化为目标建立了医联体合作决策优化模型，利用卡罗需-库恩-塔克（Karush-Kuhn-Tucker,KKT）条件得到了最优的转诊策略。应用合作博弈理论，将医联体利益分配问题构建成医联体合作博弈模型，证明了该博弈模型满足超可加性和平衡性，并提出了一种基于市场均衡转诊价格的核心利益分配方案。最后通过数值算例进一步检验了该核心利益分配方案的性质，讨论了患者转诊意愿对医联体合作收益的影响。结果表明：参与医联体合作可以有效地提高各医院的期望收益，且合作的总收益大于各医院独立运营的收益之和，医院有动机参与医联体合作；医联体合作收益随着患者转诊意愿的增加而增加。     

Simulation optimization for revenue management of airlines with cancellations and overbooking

This paper develops a model-free simulation-based optimization model to solve a seat-allocation problem arising in airlines. The model is designed to accommodate a number of realistic assumptions for real-world airline systems—in particular, allowing cancellations of tickets by passengers and overbooking of planes by carriers. The simulation–optimization model developed here can be used to solve both single-leg problems and multi-leg or network problems. A model-free simulation–optimization approach only requires a discrete-event simulator of the system along with a numerical optimization method such as a gradient-ascent technique or a meta-heuristic. In this sense, it is relatively “easy” because alternative models such as dynamic programming or model-based gradient-ascent usually require more mathematically involved frameworks. Also, existing simulation-based approaches in the literature, unlike the one presented here, fail to capture the dynamics of cancellations and overbooking in their models. Empirical tests conducted with our approach demonstrate that it can produce robust solutions which provide revenue improvements over heuristics used in the industry, namely, EMSR (Expected Marginal Seat Revenue) for single-leg problems and DAVN (Displacement Adjusted Virtual Nesting) for networks.     

质量差别产品的收益管理描述及定价策略分析

收益管理是一种以细分市场为基础,通过差别定价实现收益最大化的技术.通过深入分析收益管理的核心思想,提出了质量差别歧视下产品的收益管理定义和描述,并以两种质量水平产品的定价和定质为研究对象,建立了一个基于质量差别产品的简单单周期收益定价和定质模型.首先按照产品的质量和价格建立产品的售出概率模型,其次以售出概率模型为基础建立了以收益最大化为目标的非线性规划模型,从而确定不同质量水平产品的质量和最优价格.最后通过一个算例进一步对定价策略进行了直观分析.     

基于CVaR的航空公司机票超售策略

机票超售是航空公司提高收益的有效手段。针对风险规避环境下的机票超售问题,构建了基于CVaR风险度量方法的超售模型,并以风险最小化为目标对模型进行优化处理,得到了最优超售水平满足的条件。研究结果表明：最优超售水平依赖于订票乘客到达率的分布情况及决策者对风险规避的程度,同时机票价格和单位超售损失对最优策略有重要影响。     

A reinforcement learning approach to a single leg airline revenue management problem with multiple fare classes and overbooking

The airline industry strives to maximize the revenue obtained from the sale of tickets on every flight. This is referred to as revenue management and it forms a crucial aspect of airline logistics. Ticket pricing, seat or discount allocation, and overbooking are some of the important aspects of a revenue management problem. Though ticket pricing is usually heavily influenced by factors beyond the control of an airline company, significant amount of control can be exercised over the seat allocation and the overbooking aspects. A realistic model for a single leg of a flight should consider multiple fare classes, overbooking of the flight, concurrent demand arrivals of passengers from the different fare classes, and class-dependent, random cancellations. Accommodating all these factors in one optimization model is a challenging task because that makes it a very large-scale stochastic optimization problem. Almost all papers in the existing literature either accommodate only a subset of these factors or use a discrete approximation in order to make the model tractable. We consider all these factors and cast the single leg problem as a semi-Markov Decision Problem (SMDP) under the average reward optimizing criterion over an infinite time horizon. We solve it using a stochastic optimization technique called Reinforcement Learning. Not only is Reinforcement Learning able to scale up to a huge state-space but because it is simulation-based it can also handle complex modeling assumptions such as the ones mentioned above. The state-space of the numerical test problem scenarios considered here is non-denumerable; its countable part being of the order of 10⁹. Our solution procedure involves a multi-step extension of the SMART algorithm which is based on the one-step Bellman equation. Numerical results presented here show that our approach is able to outperform a heuristic, namely the nested version of the EMSR heuristic, which is widely used in the airline industry. We also present a detailed study of the sensitivity of some modeling parameters via a full factorial experiment.     

Optimisation of multi-stage JIT production-pricing decision: centralised and decentralised models and algorithms

This research studies the optimal decision for product pricing, production lot sizing in a multi-stage serial just-in-time production system with kanban-controlled policy. A decentralised decision model and a centralised decision model of this problem are formulated as a mixed-integer nonlinear programming problem. In order to solve the models, three algorithms are developed. The first one is an approximate procedure which solves the decentralised decision model; the second one is a proximate optimal procedure using two-phase search technique that solves the centralised decision model, and the third one is an approximate method using meta-heuristic technique which is used for both decentralised and centralised models. Numerical example shows that centralised decision can obtain higher economic benefit with lower cost and higher revenue and profit. Meanwhile, when demand is more price sensitive, centralised decision can achieve significant profit enhancement. Computational results attribute to different characteristics of the problem and solution superiority.     

Unit commitment in oligopolistic markets by nonlinear mixed variable programming

In the paper we consider the unit commitment problem in oligopolistic markets. The formulation of the problem involves both integer and continuous variables and nonlinear functions as well, thus yielding a nonlinear mixed variable programming problem. Our formulation takes into account all technical constraints for the generating units, such as ramp rate and minimum up and down time constraints, considers the uncertainty related to the selling prices and allows modeling their dependence on the total output of a producer. The objective function is the expected value of the revenue over the different scenarios minus a term which takes into account the risk related to the decision. To solve the problem we adopt a recently proposed method for mixed integer nonlinear programming problems and use a derivative free algorithm to solve the continuous subproblems. We report results for two operators: one managing a single unit and the other managing three units. Numerical results give evidence to the features of the modeling and show viability of the adopted algorithm.     

O&;D revenue management in cargo airlines—a mathematical programming approach

In this paper we present a mathematical programming based approach for revenue management in cargo airlines. The approach is based on a modified version of a multicommodity network flow model which has been developed in a decision support approach for schedule planning in cargo airlines. We think that using the same concept for planning and revenue management is essential for consistency of planning and operation. To meet the real-time requirements of revenue management special computational strategies for solving the large models are necessary.     

A new trust region–sequential quadratic programming approach for nonlinear systems based on nonlinear model predictive control

In this article, a superlinearly convergent trust region–sequential quadratic programming approach is first proposed, developed and investigated for nonlinear systems based on nonlinear model predictive control. The method incorporates a combination algorithm that allows both the trust region technique and the sequential quadratic programming method to be used. If the attempted search of the trust region method is not accepted, the line search rule will be adopted for the next iteration. Also, having to resolve the quadratic programming subproblem for nonlinear constrained optimization problems is avoided. This gives the potential for fast convergence in the neighbourhood of an optimal solution. Moreover, additional characteristics of the algorithm are that each quadratic programming subproblem is regularized and the quadratic programming subproblem always has a consistent point. The main result is illustrated on a nonlinear system with a variable parameter and a bipedal walking robot system through simulations and is utilized to achieve rapidly stability. Numerical results show that the trust region–sequential quadratic programming algorithm is feasible and effective for a nonlinear system with a variable parameter and a bipedal walking robot system. Therefore, the simulation results demonstrate the usefulness of the trust region–sequential quadratic programming approach with nonlinear model predictive control for real-time control systems.     

Designing public storage warehouses with high demand for revenue maximisation

The design of public storage warehouses needs to fit market segments to increase the average revenue in an environment of high demand. This paper presents a revenue model integrated with queuing and price-demand theories to solve the design and pricing problem for public storage warehouses. We consider two demand cases in the model, which are exponential demand and piecewise linear demand. We also develop a solution based on dynamic programming techniques to solve the problem. Using data from a warehouse, we conduct numerical experiments. Results show that our approach can improve the expected revenue of public storage warehouses with high demand by 16.6% on average. We further conduct sensitivity analysis on price, and investigate the relation between revenue and price.     

球度误差包容评定的高精度实现方法

依据数学规划理论,构造一种以线性包容评定模型的迭代运算去逼近非线性的精确包容评定模型优化解的球度误差评定算法,并建立了适用于计算判别的球度误差包容评定最优条件判别准则。算法具有评定精度高,收敛速度快、计算稳定等优点。