机场巴士企业如何在竞争中获得快速发展

随着国家的不断进步,搭乘飞机出国、办事的国民越来多。机场的需求量增长很快,机场和市场的交通络续方式也越来越重要,机场巴士也成为其中一项极其重要的运输方式。当然,机场运营的不断发展,同时也推动着机场巴士企业的发展。越来越多的机场巴士企业逐鹿中原,这让巴士企业竞争不知不觉步入微利时代。任何一家巴士企业都想在竞争企业多如牛毛的机场巴士市场中脱颖而出,这单单不是靠以往单一的运营模式、墨守成规的运营方案。本文就机场巴士企业在竞争中存在的问题进行归纳和分析,对机场巴士企业的管理模式和经营理念做出探究,并就机场巴士的客运运输提出意见。     

高速铁路客运专线的收益管理模型

本文在分析铁路客运收益管理的研究进展的基础上,提出了一个适合铁路客运专线的收益管理优化模型。模型以列车运营总收益最大化为目标,优化列车的席位控制和发车间隔,将席位分配与运营能力优化统一在一个模型中。利用随机生成数据进行的模型试验表明,模型可以在较短的时间内求解较大规模的收益管理优化问题。     

基于VaR控制预留缺口的资产负债管理优化模型

以利率变化后的VaR风险限额约束条件,以资产组合的利息收入最大为目标函数,建立资产负债组合优化模型。本文的创新与特色一是通过预设持续期缺口使银行的资产组合在利率变动的有利条件下增加银行净值。这弥补了现有的零缺口免疫条件的资产组合不能使银行股东权益在利率变化中增加的缺陷。二是利用VaR技术建立约束条件控制预设的持续期缺口。在利率变动的不利条件下,通过在一定置信水平下的最大损失限额控制资本损失,把银行可能面临的利率风险限定在银行的净利息收入范围内。这种优化配给控制了资本损失,保护了股东权益,开辟了资产优化配置研究的新思路。三是利用银行间市场7天回购利率(R07D)的历史数据,估计了未来的市场利率波动量的概率分布,解决了由于影响因素多而难以刻画市场利率变动情况的问题。     

当代中国管理科学与工程的学科发展与创新

自20世纪80年代以来我国管理科学与工程得到了快速发展并已经建立了较为完善的学科体系.管理科学与工程具有学科交叉与知识融合,基础学科与领域的拓展性,理论研究与应用的结合等区别于其他管理学科的特征.当代管理科学与工程学科有必要在以下几方面努力优化学科资源,完善教育体系与方法,以开放、学习与创新为特征强化管理研究,以服务社会为使命,以全面建设小康社会为契机,强化管理科学与工程的应用研究.     

基金公司竞争策略研究——基于产品创新和机制创新视角

本文以2004-2012年提供开放式基金的基金公司为研究对象,以基金公司市场份额为切入点,利用动态面板模型,检验了基金公司历史业绩、产品创新策略和机制创新策略在基金公司竞争中的有效性。实证结果显示历史业绩是影响基金公司市场竞争力的重要因素,基金投资者偏好具有产品创新能力和机制创新精神的基金公司,并不热衷于投资成立时间久、旗下基金数量众多的老牌金公司,对基金公司股东背景也没有显著偏好。进一步细分市场发现,在股票型基金市场上,实施创造明星策略有利于获得更高市场份额。在债券型基金市场上,提高新基金供给量和相对供给强度是最优策略。与股票型基金市场相同,在混合型基金市场上提供具有实质性创新特征的新基金、增加基金从业者持基的基金数量,有助于获得更大市场份额。因此,基金公司决策层在制定竞争战略时,应当考虑基金投资者对产品创新认知能力的提高和对利益绑定机制的诉求,依据基金类型的不同,设计具有针对性的竞争策略。     

多枢纽轴辐式协同物流网络任务-路径优化匹配研究

针对物流网络运营中规模效益低、运营商与客户利益冲突等问题，本文提出了基于多枢纽轴辐式协同物流网络的任务-路径优化匹配策略。在网络结构确定的条件下，从物流集成服务商角度出发，综合考虑资源发车时刻、路径能力限制、动态成本折扣系数以及任务延时惩罚，构建了以总物流成本最小和时间最短为优化目标的轴辐式协同物流网络任务-路径优化匹配数学模型。根据问题特点，设计了基于三层编码的遗传算法对模型进行求解，同时配合多点交叉、变异机制提高求解效率。研究结果表明，集并运输能够大幅度降低任务集执行成本，但当集并带来的交货延迟、客户满意度降低等负面效应超过其正面效应时，需适当匹配直达线路以提高整体效益。     

网络效应、新兴产业演化与生态位培育——来自电动汽车行业的 ABM 仿真研究

有限的需求规模是制约电动汽车产业发展的关键因素,如何促进市场培育是电动汽车产业演化中亟待解决的重要问题.本文在分析汽车产业网络效应的特征及其存在性基础上,利用ABM框架构建了异质性厂商和消费者的决策模型,模拟仿真了网络效应下汽车产业演化的基本过程和市场需求的分布演化特征,进而讨论了充电基础设施建设对于电动汽车市场培育的差异化作用.结果表明:汽车产业中既存在着与用户规模有关的直接网络效应,也存在着与配套基础设施有关的间接网络效应;在汽车产业的演化过程中,传统汽车始终占据市场主导地位,电动汽车的市场份额十分有限;空间分布上,电动汽车市场需求呈现出"分散—集中"的态势;充电设施建设通过增强网络效应有效地刺激了消费者购买,其政策效果在电动汽车需求分布由分散趋于集中的阶段体现出来;与全局建设相比,局部建设依靠提高特定区域充电站的密度集中强化网络效应,从而增加了潜在消费者购买电动汽车的动力,加速了电动汽车市场需求规模的扩大.     

中小企业如何开展网络虚拟经营

一、中小企业发展网络虚拟经营的战略意义网络经济下实施虚拟经营的实质就是组建一个动态联盟,其实也就是指企业群体为了某一市场机遇,能够迅速、及时的响应和把握市场机会,满足需求市场的需要.用最快的流程推出高质量、高科技含量高清意度的产品来占领或获取更多的市场份额,以     

基于全部资产负债利率风险免疫优化的增量资产组合决策模型

市场利率的变化导致银行资产与负债的价值均发生变化,进而导致银行的所有者权益发生变化引起银行股东财富的风险.因此利率风险免疫对商业银行具有极其重要的作用.本文通过建立银行净值的变化与增量资产负债和存量资产负债持续期的函数关系式,提出了全部组合资产负债利率风险免疫优化条件.以全部组合资产负债的持续期缺口与零的偏差最小为第一目标,以银行全部资产的利息收益最大为第二目标,以监管当局的政策法规为约束务件,建立了基于全部资产负债利率风险免疫优化的增量资产组合决策模型.本文的创新与特色一是通过构建银行净价值与全部资产负债组合持续期的函数关系来建立利率免疫条件,解决了银行增量资产组合配给时、资产增量组合与存量组合构成的全部组合的总体风险控制问题.这就改变了现有研究仅仅立足增量资产与负债的局部风险控制的做法.二是通过用存量资产负债的剩余期限替换现有研究持续期表达式中的期限,得到了既反映增量资产负债持续期、又反映存量资产负债持续期的通用表达式,解决了资产负债增量组合与存量组合构成的全部组合的风险的测量问题.     

飞行频率、拥挤成本和互补型航空联盟:中枢轮辐网络机场拥挤分析

本文在基于飞行频率、拥挤成本和互补型航空联盟的基础上系统分析了中枢轮辐网络机场拥挤问题。研究表明:航空公司的利润最大化使得航班飞行频率存在过度供给,飞机大小也是次优的,这进一步恶化了中枢机场的机场拥挤;航班飞行频率与航空公司的拥挤成本系数、旅行计划延迟效用系数正相关,与飞机每次起降的固定成本负相关;互补型航空联盟使得联盟体利润增加,航空联盟对连接市场乘客有利,对本地市场乘客不利,航空联盟还可以内化伙伴间拥挤,航班飞行频率得以优化,机场拥挤得以缓解。     

A practical guide to robust optimization

Robust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations. It is therefore remarkable that real-life applications of robust optimization are still lagging behind; there is much more potential for real-life applications than has been exploited hitherto. The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do׳s and don׳ts for using it in practice. We use many small examples to illustrate our discussions.     

Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results

Given a (combinatorial) optimization problem and a feasible solution to it, the corresponding inverse optimization problem is to find a minimal adjustment of the cost function such that the given solution becomes optimum.Several such problems have been studied in the last twelve years. After formalizing the notion of an inverse problem and its variants, we present various methods for solving them. Then we discuss the problems considered in the literature and the results that have been obtained. Finally, we formulate some open problems.     

Dynamic-objective particle swarm optimization for constrained optimization problems

This paper firstly presents a novel constraint-handling technique, called dynamic-objective method (DOM), based on the search mechanism of the particles of particle swarm optimization (PSO). DOM converts the constrained optimization problem into a bi-objective optimization problem, and then enables each particle to dynamically adjust its objectives according to its current position in the search space. Neither Pareto ranking nor user-defined parameters are involved in DOM. Secondly, a new PSO-based algorithm—restricted velocity PSO (RVPSO)—is proposed to specialize in solving constrained optimization problems. The performances of DOM and RVPSO are evaluated on 13 well-known benchmark functions, and comparisons with some other PSO algorithms are carried out. Experimental results show that DOM is remarkably efficient and effective, and RVPSO enhanced with DOM exhibits greater performance. In addition, besides the commonly used measures, we use histogram of the test results to evaluate the performance of the algorithms.This research is supported by National Natural Science Foundation of China (No. 10371028) and Scientific Research Fund of Southern Yangtze University (No. 0003182).     

Optimization of the Aflatoxin Monitoring Costs along the Maize Supply Chain

An optimization model was used to gain insight into cost‐effective monitoring plans for aflatoxins along the maize supply chain. The model was based on a typical Dutch maize chain, with maize grown in the Black Sea region, and transported by ship to the Netherlands for use as an ingredient in compound feed for dairy cattle. Six different scenarios, with different aflatoxin concentrations at harvest and possible aflatoxin production during transport, were used. By minimizing the costs and using parameters such as the concentration, the variance of the sampling plan, and the monitoring and replacement costs, the model optimized the control points (CPs; e.g., after harvest, before or after transport by sea ship), the number of batches sampled at the CP, and the number of samples per batch. This optimization approach led to an end‐of‐chain aflatoxin concentration below the predetermined limit. The model showed that, when postharvest aflatoxin production was not possible, it was most cost‐effective to collect samples from all batches and replace contaminated batches directly after the harvest, since the replacement costs were the lowest at the origin of the chain. When there was aflatoxin production during storage, it was most cost‐effective to collect samples and replace contaminated batches after storage and transport to avoid the duplicate before and after monitoring and replacement costs. Further along the chain a contaminated batch is detected, the more stakeholders are involved, the more expensive the replacement costs and possible recall costs become.     

一种差异工件单机批调度问题的蚁群优化算法

由于在利用蚁群算法构建差异工件(即工件有尺寸差异)单机批调度问题的解时,批的加工时间是不确定的.从而不能类似于经典调度问题的蚁群算法把批加工时间的倒数作为蚁群算法中的启发式信息,引入批的利用率和批的负载均衡率作为蚁群算法中的启发式信息,提出了JACO(ant colony optimization based a job sequence)和BACO(ant colony optimization based a batch sequence)两种蚁群优化算法.在算法JACO中,解的编码为工件序列,它对应着用BF(best fit)分批规则生成的调度方案,信息素代表工件间的排列顺序;在算法BACO中,解的编码为批序列,信息素代表工件间的批相关性,由此信息素通过中间信息素量来构造相应的解,并引入特定的局部优化策略,提高了算法的搜索效率.实验表明,与以往文献中的SA(simula-ted annealing)、GA(genetic algorithm)算法以及FFLPT(first-fit longest processing time)、BFLPT (best-fit longest processing time)启发式规则相比,算法JACO和BACO明显优于它们,且BACO算法比JACO算法效果更好.     

A joint optimal pricing and order quantity model under parameter uncertainty and its practical implementation

We consider a robust optimization model of determining a joint optimal bundle of price and order quantity for a retailer in a two-stage supply chain under uncertainty of parameters in demand and purchase cost functions. Demand is modeled as a decreasing power function of product price, and unit purchase cost is modeled as a decreasing power function of order quantity and demand. While the general form of the power functions are given, it is assumed that parameters defining the two power functions involve a certain degree of uncertainty and their possible values can be characterized by ellipsoids. We show that the robust optimization problem can be transformed into an equivalent convex optimization which can be solved efficiently and effectively using interior-point methods. In addition, we propose a practical implementation of the model, where the stochastic characteristics of parameters are obtained from regression analysis on past sales and production data, and ellipsoidal representations of the parameter uncertainties are obtained based on a combined use of genetic algorithm and Monte Carlo simulation. An illustrative example is provided to demonstrate the model and its implementation.     

Staffing and scheduling flexible call centers by two-stage robust optimization

We study the shift scheduling problem in a multi-shift, flexible call center. Differently from previous approaches, the staffing levels ensuring the desired quality of service are considered uncertain, leading to a two-stage robust integer program with right-hand-side uncertainty. We show that, in our setting, modeling the correlation of the demands in consecutive time slots is easier than in other staffing approaches. The complexity issues of a Benders type reformulation are investigated and a branch-and-cut algorithm is devised. The approach can efficiently solve real-world problems from an Italian call center and effectively support managers decisions. In fact, we show that robust shifts have very similar costs to those evaluated by the traditional (deterministic) method while ensuring a higher level of protection against uncertainty.     

震后伤员救援车辆两阶段规划模型及算法研究

不确定情景下的应急物资配送和车辆路径优化问题是目前国内研究的一大热点,其中 震后伤员运送车辆调度及路径优化问题随着近几年地震等自然灾害的频繁发生也逐渐引起人 们的重视． 本文采用聚合优化算法对灾区进行应急救援区域划分,建立了以总救援时间最短和 相对综合救援权重值最大为目标的救援车辆两阶段数学规划模型,设计了带三角函数变异的 离散型萤火虫优化算法进行求解,并与常规算法求解结果做了对比分析． 通过算例进行模拟实 验表明,所提出的模型和算法可以有效解决地震情景下的伤员救援车辆调度问题,且改进算法 的全局搜索性能更优．     

Stock screening with use of multiple criteria decision making and optimization

A new method for the stock ranking based on the multiple criterion decision making and optimization is proposed. Two general criteria are used in the analysis. The first of them is based on the financial indices and may be treated as the criterion of firm's “health” or its financial performance. The second one is the two-criteria performance of firm based on the stock prices. It represents the firm's market success. The method rests on the selection of the stocks with a great correlation of the firm's financial performance and its market success. The local criteria are built in the form of the membership function of corresponding fuzzy subsets. Two different strategies for stock ranking and three most popular methods for local criteria aggregation are compared. As the example the values of financial rations and prices from database comprising the data of 162 firms from subsector of the biotechnology of US economy were used. It is shown that the proposed method makes it possible to select a small group of “good” stocks characterized by a great coincidence of firm's financial performance and its market success. The method rejects from the consideration all the “unsafe” firms, i.e., such ones that their market success is based rather on the public relations, rumors and other rather unreliable information. The method is addressed to those who prefer to select for a portfolio only the firms which demonstrate the closeness of their overall financial performance in the past year and success in the Stock Exchange in the following year.     

信息披露最优化与交易效率——一个弱势个体与强势个体交换的例证

本文以中小企业融资市场为例,应用数理模型揭示了弱势个体通过集体形式披露信息从而提高交易效率的微观机理。研究发现,弱势个体在同强势个体进行交易时,不仅存在信息披露最优化问题,还存在着规模效应不足的问题,这致使两者在现实中难以实现交易。研究表明:1.一定程度上信息不对称能使弱势个体与强势个体在交易中实现双赢;2. 弱势个体在无法正常披露信息时,通过捆绑以强势集体形式披露信息,不仅可以改善强弱双方的信息不对称状况,而且可以把弱势个体隐匿型信息内部化,以利于同强势个体交易,从而提高交易效率;3. 集体披露信息机制还能够创造规模效应。