通胀市场下多设备租赁的在线策略分析

现实租赁市场中,企业同时租赁多台设备的现象大量存在,但经营者面临的最大难题是如何对这多台设备进行在线租赁的组合优化,从而降低决策成本,而通货膨胀又进一步增加了决策难度。本文运用在线算法和竞争分析法建立多设备投资的风险控制策略,并分析通胀对决策的影响。首先在Karp经典模型上给出通胀因素下多设备投资的最优在线和离线策略;接着建立设备租赁在连续可分情形下的最优风险控制模型,进一步结合实际投资中设备必须以离散整数租赁的特点,对CR策略进行调整和优化,得到近似的CRJ策略,使得策略更符合实际投资活动。最后给出具体实例分析,结果显示,当物价指数逐渐增大时,最优决策日期相应提前,对应最优策略的竞争比也逐渐增大,进一步说明物价指数因素和多设备投资因素的引入对投资者的决策有着重要的影响,为多设备在线租赁问题的研究提供了新的解决思路。     

一类占线融资租赁问题的最优竞争策略与风险补偿模型

运用占线算法与竞争分析方法,研究了资产所有权在一定的租赁时间后转移给承租人的融资租赁问题的最优竞争策略与风险补偿模型。首先给出了该问题的最优离线解;然后,根据约定的租赁时限与购买价格及租赁费用的大小关系,分别给出了3种情形的占线策略及相应的竞争比分析;最后,在AL-BINALI提出的占线风险补偿分析框架下,给出了2种预期形式及相应最优占线收益策略。     

在线设备更新问题的竞争策略及其风险补偿模型

本文基于一种设备在线租赁的基础上,提出了在线设备更新问题。运用传统竞争比方法分别研究了两阶段设备更新问题在有无利率情形下的在线竞争策略,并建立了相应的风险补偿模型,从而在线投资者可以根据自己的风险容忍度和预期选择最优的更新策略。市场利率的引入使得在线设备更新模型更复杂但更贴近于现实中的设备更新问题。最后,通过具体实例进一步说明了市场利率下在线竞争比更小,而且竞争比关于市场利率递减;同时也说明了风险补偿模型中最优约束竞争比要小的多。因此,投资者若考虑到资金的收益及市场风险因素后将会采取更加谨慎稳健的投资策略。     

具有线性交易成本的耐用性设备在线租赁策略分析

在设备寿命周期内,购买和使用结束后出售旧设备需要支付交易成本,交易成本与设备价格具有线性关系。分析显示:对于具有线性交易成本的耐用性设备在线租赁问题,如果不考虑在设备使用结束后出售旧设备的残值以降低成本,从租赁模式转换到购买模式的在线策略的转换时机,总是晚于考虑在设备使用结束后出售旧设备的在线策略;如果不考虑购买和使用后出售的交易成本,在线策略名义上的竞争比小于实际竞争比。     

随机选择设备获得方式的可折旧设备在线租赁

基于可折旧设备在线租赁的特点,首先研究了随机选择租赁和购买两种设备获得方式的可折旧设备在线竞争算法.商品的多样化使得具有同种功能的设备往往具有不同的折旧和购买价格,针对这一特点,进一步提出了转化随机策略,用来解决随机选择多种设备获得方式的可折旧设备在线租赁问题.转化随机策略将随机选择多种设备获得方式转化为随机选择两种设备获得方式,并得到了与折旧相关的竞争比上界.与经典租赁问题的随机性策略相比,折旧的引入和转化随机策略的提出使得竞争比进一步减小,进而竞争性能得到提高.     

基于任务跟踪的在线租赁问题与竞争策略研究

现实经济活动中,有许多租赁融资决策既非纯在线租赁问题也非纯离线租赁问题,而是介于二者之间的具有部分已知信息的在线租赁问题.基于此研究了工作任务总量已知而工作任务进展序列未知的在线设备租赁问题,以租赁工作所需设备的费用最小为优化目标,建立了该问题的基本数学模型,提出了任务跟踪策略,给出并证明了基于这一策略的算法的竞争比.最后,把该在线租赁问题的竞争比与经典的在线租赁问题的竞争比做了比较分析,结果表明在线租赁问题的算法优于经典的在线算法,竞争性能得到了较大的提高.     

消费者对产品估值不确定与后悔效应下在线零售商退货策略研究

本文针对策略型消费者网络购物问题,在考虑策略型消费者对产品估值不确定以及具有预期后悔行为下,研究了退货策略对消费者购买决策以及在线零售商运作决策的影响。以最大化在线零售商利润为目标,本文分别构建了退货策略与不退货策略下的利润模型。研究表明：消费者对产品估值的不确定性水平会降低零售商的利润,预期后悔会加剧零售商利润的降低;退货策略有利于缓解估值不确定与预期后悔对零售商的损害,且在退货策略下,消费者的预期后悔行为有利于增加零售商的利润。因此在线零售商的最优决策受产品匹配率以及高价值消费者比例的影响,无论零售商是否实施退货策略,零售价较低时市场完全覆盖下的最优订购量与最优利润并不总是优于零售价较高时市场部分覆盖策略下的,从而为在线零售商的退货略选择提供决策支持。     

可折旧设备在线租赁的随机性竞争策略

应用在线算法与竞争分析研究在线租赁问题是近年来国内外的一个研究热点.在一般设备在线租赁的基础上,提出了可折旧设备在线租赁问题.针对离线人具有遗忘性竞争对手的特点分别给出了可折旧设备在线租赁在有无利率情形下的随机性竞争策略.基于在线-离线成本比值矩阵分别证明了有无利率下随机性策略的竞争比,说明了折旧因素的引入使得可折旧设...     

租金费用和购买价格连续可变的在线租赁竞争策略分析

本文运用竞争分析方法研究了占线金融租赁决策问题,已往的研究都是基于租赁设备的租用费用和购买价格不变的情形给出最优投资策略,本文给出了当价格在有界范围内连续可变时的占线投资策略,并对有无利率两种情形分析进行了竞争策略分析,分别给出了其竞争比的上下界。     

局内租赁问题的风险补偿模型及其竞争分析

将风险的概念引入局内租赁问题中,建立了该问题的风险补偿模型,并对存在和不存在 利率情况下的局内租赁问题作了分析. 和局内问题中传统的竞争比分析不同的是,竞争比分析 只反映局内策略与基准算法(局外最优算法) 的相对绩效,但这往往忽略了很多的有用信息,且 分析模型很不灵活. 然而在风险补偿模型中,投资者可以控制风险,根据自己不同的风险容忍 度和未来预期选择最优的租赁策略     

Online MapReduce processing on two identical parallel machines

In this work we investigate the online over-list MapReduce processing problem on two identical parallel machines, aiming at minimizing the makespan. Jobs are revealed one by one, and each job consists of one map task and one reduce task. The map task can be arbitrarily split and processed on both machines simultaneously, while the reduce task has to be processed on a single machine and it cannot be started unless the map task has been completed. We first show that the general case of the problem reduces to the classical two machine online scheduling model with an optimal competitive ratio of 3/2. For a special case where the map task is at least as long as the reduce task, we prove that no online algorithm can be less than 4/3-competitive. An optimal Greedy algorithm with a matching competitive ratio is proposed as well.     

Fleet and revenue management in car rental companies: A literature review and an integrated conceptual framework

This paper aims to present, define and structure the car rental fleet management problem, which includes operational fleet management issues and problems traditionally studied under the revenue management framework. The car rental business has challenging and distinctive characteristics, which are mainly related with fleet and decision-making flexibility, and that render this problem relevant for academic research and practical applications. Three main contributions are presented: an in-depth literature review and discussion on car rental fleet and revenue management issues, a novel integrating conceptual framework for this problem, and the identification of research directions for the future development of the field.     

Competitive analysis of online machine rental and online parallel machine scheduling problems with workload fence

In this paper, we introduce the concept of “workload fence" into online machine rental and machine scheduling problems. With the knowledge of workload fence, online algorithms acquire the information of a finite number of first released jobs in advance. The concept originates from the frozen time fence in the domain of master scheduling in materials management. The total processing time of the jobs foreseen, corresponding to a finite number of jobs, is called workload fence, which is irrelevant to the job sequence. The remaining jobs in the sequence, however, can only become known on their arrival. This work aims to reveal whether the knowledge of workload fence helps to boost the competitive performance of deterministic online algorithms. For the online machine rental problem, we prove that the competitiveness of online algorithms can be improved with a sufficiently large workload fence. We further propose a best online algorithm for the corresponding scenario. For online parallel machine scheduling with workload fence, we give a positive answer to the above question for the case where the workload fence is equal to the length of the longest job. We also show that the competitiveness of online algorithms may not be improved even with a workload fence strictly larger than the largest length of a job. The results help one manager to make a better decision regarding the tradeoff between the performance improvement of online algorithms and the cost caused to acquire the knowledge of workload fence.

     

价格下界随时间递增的占线采购问题

本文研究的是价格不确定且其下界随时间递增的原材料采购问题。在实际的原材料采购问题中,原材料的价格随时间的变动往往是不可预测的。之前的学者在研究价格不确定的占线采购问题时,假设价格在一个统一的常数上下界内,这没有考虑到经过时间的变化,价格的上下界可能也是变化的。本文提出并研究价格下界随时间递增的原材料占线采购问题。构建了相应数学模型,给出了相应的竞争采购策略并证明了竞争比,同时通过证明问题的匹配竞争比下界,说明给出的竞争采购策略是最优的,最后利用数值分析进一步说明竞争策略具有较好的竞争性能。     

Data mining driven DMAIC framework for improving foundry quality – a case study

Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) methodology has been widely used across industries as the best systematic and data driven problem solving approach for quality improvement. Statistical Design of Experiment (DOE) is used in the ‘Improve’ stage for obtaining optimal process settings for significant variables contributing towards quality improvement. But, DOE is an offline activity requiring time and other resources for conducting experiments and analyses. Further, there are many small and medium scale enterprises that cannot afford to conduct DOE. Under such practical constraints, it is desirable to apply DMAIC using online process data under day-to-day production situations or with little changes in process settings without compromising production. In this article, we propose a DMAIC framework, driven by data mining techniques for defect diagnosis and quality improvement where historical and online process data can be effectively utilised. We have used two decision tree algorithms namely, Classification and Regression Tree and Chi-squared Automatic Interaction Detection in developing the proposed framework. The proposed approach is applied in an Indian grey iron foundry where conducting DOE is not a feasible option for the management. The result demonstrates a significant reduction in casting defect and validates the practical viability of this approach.     

Optimal algorithms for online time series search and one-way trading with interrelated prices

The basic models of online time series search and one-way trading are introduced by El-Yaniv et al. in Algorithmica 30(1), 101–139 (2001) where it is assumed that the prices are bounded within interval [m,M] (0<m<M). In this paper, we consider another case where every two consecutive prices are interrelated, that is, the variation range of each price depends on its preceding price. We present optimal deterministic online algorithms for the two problems, respectively. According to one conclusion in Algorithmica 30(1), 101–139 (2001), we further point out that for the case we considered, an optimal deterministic algorithm for the one-way trading problem can be regarded as an optimal randomized one for the time series search problem, and randomization is useless for the one-way trading problem.     

Competitive analysis for make-to-order scheduling with reliable lead time quotation

This paper makes extended studies on the discrete problem of online scheduling and reliable lead time quotation (discrete Q-SLTQ) introduced by Keskinocak et al. (Manag. Sci. 47(2):264–279, 2001). We first relax the assumption on revenue function from a linear decreasing function to any decreasing function. We present an online deterministic strategy which is optimal in competitiveness for concave revenue functions. The above results are further extended to the continuous Q-SLTQ model where orders are released at arbitrary time points. For the discrete Q-SLTQ problem, if orders are with nonuniform lengths, we prove the nonexistence of online strategies with bounded competitive ratios; otherwise if orders are with unit length but various weights, we present an optimal online strategy.     

具有概率分布在线租赁问题策略研究

在经济系统中,决策越来越呈现出在线性特征,传统优化方法在解决这类在线问题时,通常假设未来输入是一随机变量从而寻求概率意义上的最优决策。近年来,在优化领域兴起了一种新的研究方法——在线算法与竞争分析,为解决这类在线问题提供了新的视角,但传统的竞争分析方法有意规避概率分布假设。对于在线租赁决策问题,由于其输入结构简单且具有良好的统计性质,似乎忽略这些有用的信息而只运用标准的竞争比方法分析显然具有不足之处。在本文中,我们将其输入结构的概率分布引入纯竞争分析方法中,从而建立了具有概率情形的最优在线租赁模型,并得到了最优竞争策略及其竞争比。     

不确定终止时间的多阶段最优投资组合

研究了当终止时间不确定时的多阶段最优投资组合问题.假定终止时间是个服从某分布的随机变量,将不确定终止时间的问题转化为确定时间的问题,应用动态规划求解模型,得到最优投资策略以及有效边界的解析形式.实例证明所得的结论是对确定终止时间情形的推广,最优投资策略受终止时间分布的影响.