多资源约束下基于关键链的项目群工期-费用优化

现有工期压缩模型未考虑资源约束下多合同项目工期压缩所产生的局部效应与整体效应,针对这一局限性,通过引入子网络,分析了基于关键链法的项目群工期—费用优化机理,据此,对项目群初始网络计划进行工期优化。借助关键链,除去计划工期的冗余时间,动态调整存在资源冲突的合同项目开始时间。在此基础上,分析工期动态优化对关键链和非关键链合同项目压缩费用产生的不同影响以及对子网络自身和项目群的影响。基于此,研究并构建了多资源受限下单一子网络和项目群工期-费用优化模型。最后,结合算例,分析了优化模型的可行性和优势。研究结果表明：模型能够有效地解决资源约束下项目群计划调整和工期-费用优化问题,从而能够为业主科学安排项目群中各合同项目的起始时间和资源计划提供依据。     

子网络视角下项目群合同项目工期延误诊断模型研究

项目群工期延误诊断是项目群进度目标控制的一项重要任务。总时差可用于判断项目群中某项工作延误对项目群总工期的延误程度,但没有解决某项工作延误对其自身合同项目和后续合同项目工期延误程度的判断问题。本文根据多项目管理和项目群管理理论,通过引入项目群子网络,研究并构建了基于子网络的项目群结构。在此基础上,运用关键路径法（CPM）,系统地研究了因子网络中合同项目某工作工期延误对自身子网络以及项目群中后续子网络工期的影响,提出了子网络后主链定理以及前主链总时差定理,从而实现子网络视角下项目群合同项目工期延误的诊断分析。结合算例进行了具体阐述与应用。最后就如何应用人工智能算法实现项目群进度及其影响因素进行实时监控提出研究思路。本文研究成果为子网络承包商的工期延误责任划分以及索赔提供依据。     

基于柔性资源约束的前摄性项目调度优化研究

不确定环境中，项目进度计划鲁棒性的高低直接影响项目能否顺利实施。本文研究了具有随机活动工期的柔性资源约束下的前摄性项目调度优化问题，目标是在柔性资源和项目工期的约束下，借助对活动开始时间合理的进行安排进而得到拥有最大鲁棒性的进度计划。首先对研究问题进行界定；随后构建优化模型，并根据问题NP-hard属性和模型特点设计了双层嵌套禁忌搜索启发式算法，通过内外两层交互搜索寻找满意解；最后通过一个实际案例对本文研究进行说明，并分析关键参数对进度计划鲁棒性的影响，得到如下结论：相对于资源无柔性情况下的项目进度计划而言，资源具备柔性后得到的项目进度计划的鲁棒性更高，具有更强的抗干扰能力，能够保证项目稳定执行；同时，项目进度计划鲁棒性分别随着项目工期的延长、资源可用量的增加或资源柔性的提高而上升。     

资源约束下的工程项目模糊工期-费用综合优化

工期和费用是工程项目最典型的目标,有效地实现工期-费用综合优化是工程项目管理成功的关键。针对项目活动持续时间的不确定性,基于模糊理论建立了考虑了工期不确定性、资源约束以及间接费用等因素的两类工期-费用综合优化模型,为更好的进行工程项目管理提供借鉴。     

复杂不确定环境下活动可拆分的项目资源鲁棒性调度优化

复杂不确定环境下,制定一个具有较强抗干扰能力的基准进度计划非常必要。本文研究了活动工期不确定环境下考虑活动可拆分的项目资源鲁棒性调度优化问题,旨在考虑活动可拆分,探究在活动优先关系约束、项目截止日期约束、活动拆分约束、资源流约束等条件下如何进行活动拆分决策并合理地安排各个项目活动/活动分段间的资源调配方案和时间缓冲添加策略,以制定鲁棒性最大化的基准进度计划。本文创新点如下：1)在项目资源鲁棒性调度优化问题中考虑活动可拆分,定义了资源流网络下活动自由时差的计算方法,提出了一种新的活动可拆分情形下进度计划鲁棒性的衡量方式,进而构建得到了考虑活动可拆分的项目资源鲁棒性调度优化模型;2)分析证明了问题模型的强NP-hard属性以及非线性属性,并在此基础上开发了一种内嵌资源调度安排的遗传算法进行问题求解;3)选取一个典型的实际案例对研究问题进行说明,直观展示了活动拆分执行对进度计划鲁棒性提升的重要价值,揭示了鲁棒性调度计划中资源调度方案的重要性,得到了活动拆分执行会增加项目内部资源转移次数的结论。     

基于奖惩机制的建筑供应链工期协调优化研究

在研究由业主和建筑商组成的两级建筑供应链的工期协调优化问题时,首先建立了基于网络图的具有资源约束的传统工期费用交换问题的优化模型。针对传统工期优化模型的缺陷,建立了具有固定资源约束的考虑资金时间价值的工期优化模型。然后从供应链角度,考虑供应链成员决策的相互影响,建立了具有柔性资源约束的基于工期奖惩机制的工期协调优化模型。给出了模型的求解算法。最后结合算例对比了不同工期优化下的结果。结果显示,传统工期优化模型的优化结果很难在考虑资金时间价值的模型中实现,但通过奖惩激励可以实现甚至更进一步改进工期优化结果,同时实现项目工期控制目标协调优化和供应链的帕累托改善。     

基于着色时间Petri网的项目进度制定与优化

着色时间Petri网具有网络图、时间、着色令牌的三重特性,能够充分表达项目进度计划中的工序流程、工序工期以及资源的约束,可为资源约束下进度计划的制定与优化提供更为便捷、有效的方法工具。本文从着色时间Petri网的定义出发,给出了基于着色时间Petri网的项目进度计划描述方法;引入了资源调控层,给出了项目进度执行过程中资源的注入、分配和释放规则,实现了进度计划的仿真优化。     

项目管理中工期-费用-质量的综合仿真和满意决策

研究了工期服从三角概率分布,费用受工期影响,质量受到费用和工期双重影响的网络计划的Monte Carlo仿真。根据仿真工期和项目工期费用曲线,得出计算费用,并以计算费用作为三角概率分布的最可能值对费用进行Monte Carlo仿真。根据仿真工期和仿真费用计算出计算质量,并以计算质量作为三角概率分布的最可能值对质量进行Monte Carlo仿真。得到各工作的工期费用和质量的仿真数据,并进一步得到项目总工期总费用和总质量的仿真数据,该数据考虑到项目三属性的不确定性和随机性：选择离理想点距离近而离负理想点距离又远的仿真结果,作为决策的满意方案。     

目标成本管理在施工项目中的适用性分析

施工项目的目标成本,就是完成总承包合同约定质量标准的施工产品所发生的一切费用期待值,是成本费用开支的控制标准。施工项目的目标成本管理是根据施工企业施工生产的特点,结合工程标价的构成和成本发生的区域,在满足工程质量、工期等合同要求的前提下,对项目实施过程中所发生的费用,通过计划、组织、控制和协调等活动实现预定的成本目标,尽可能地降低项目成本费用。     

基于软逻辑关系的重复性项目调度工期:费用优化研究北大核心CSCDCSSCI

为了满足重复性项目调度对工作连续性的要求,研究基于软逻辑关系的重复性项目调度时间-费用优化问题,从重复性项目调度工期-费用效用最大化角度,分析软逻辑关系对重复性项目调度方案的影响。构建基于软逻辑关系的重复性项目调度时间-费用优化模型,引入云模型理论,利用云模型云滴的随机性和稳定倾向性的特性,对遗传算法的交叉操作和变异操作进行改进,设计能满足重复性项目调度软逻辑关系约束的云遗传算法。结果表明,引入软逻辑关系,增强了项目调度的灵活性;以工期-费用效用最大化为目标,可有效缩短项目工期并降低项目的总费用。     

GPRs搭接网络分解优化定理在流水作业中的应用

本文发现在GPRs搭接网络传统算法中，针对某些可分解的关键工序，通过工序的分解会产生分解悖论和咖啡时间悖论。通过对这些悖论现象的分析研究，发现其存在帕累托改进。对此，提出了两个分解优化定理及网络的分解优化方法，使网络的总工期和总时差的分布都得到了优化，为项目WBS和资源优化提供了更科学的，更充足的条件。并将该分解优化定理同流水作业原理相结合，用实例证明了该方法的可操作性，为流水作业中施工段的划分提供了科学的优化方法。     

Quality assurance in an IPA setting

The experience at CIGNA Healthplans shows that a quality assurance program can be instituted in an IPA-model HMO at low cost and with the addition of little new staff. Existing resources can be effectively restructured to implement a functional program of a traditional type that is easily understood by employer groups, members, and providers. It can serve as a transitional process until the HMO is large enough to put a total quality management program in place.     

A combined optimization and agent-based approach to supply chain modelling and performance assessment

The main objective of this paper is to give an example of how expert systems techniques for distributed decision-making can be combined with contemporary numerical optimization techniques for the purposes of supply chain optimization and to describe the resulting software implementation. In this paper, multi-agent modelling techniques are applied to simulate and control a simple demand-driven supply chain network system, with the manufacturing component being optimized through mathematical programming. The system measures supply chain performance and the effect of different parameters in the replenishment control system, and can be used to simulate the behaviour of a system that uses optimization for part of its decision-making. The objective of this supply chain network system is to reduce operating cost, while maintaining a high level of customer order fulfilment.     

Credibility-based rescheduling model in a double-track railway network: a fuzzy reliable optimization approach

Using a space–time network to represent the choice of train trajectories, this paper proposes a fuzzy optimization framework to reschedule trains in a double-track railway network when the capacity reduction is caused by a low-probability incident. We explicitly introduce a fuzzy variable-based recovery time to capture the uncertainty of incident duration based on professional judgements or empirical estimates. The problem is then formulated as a credibilistic two-stage fuzzy 0–1 integer optimization model to find a reliable operational plan for emergency response guidance. The first stage of the model aims to generate a traversing order on the incident link, and meanwhile the second stage adaptively generates optimized schedules to evaluate the operational plan. Crisp equivalents of mathematical models are further investigated to simplify solution methodologies. The numerical experiments, which are solved by using the GAMS optimization software, demonstrate the effectiveness and efficiency of the proposed approaches.     

隐藏招标人偏好的政府工程多属性招投标分析

采用Harsanyi引入虚拟局中人的方法,将隐藏招标人偏好的政府工程多属性不完全博弈转换为一个完全但不完美信息博弈,在求出博弈模型的精炼贝叶斯纳什均衡解的基础上,分析招、投标双方的博弈策略。结果发现：投标人是否参与隐藏招标人偏好的政府工程投标,取决于其对招标人偏好的判断,其预期招标人的质量和工期偏好系数的概率值越大,期望收益就越大,投标积极性越高;招标人的招标剩余效用以及投标人的收益与投标质量和工期呈U形关系;招标人可以有意识地释放其偏好信息,以刺激投标人从而增加招标的剩余效用。这些结论的应用有助于改进政府工程招标程序设计,规范政府工程招投标行为。     

A PENSION DECISION MODEL FOR THE EMPLOYER*

Recently, the Financial Accounting Standards Board (FASB) reevaluated accounting for pension plans. The issue is emotional and highly political in nature. The FASB attempted to justify its approach on the basis of measuring economic activity, but it failed to provide much in the way of analytical support. This paper provides a managerial decision model and an economic basis for the existence of pension plans. A pension plan is described as a cost-saving, risk-sharing, incentive contract. The analysis is developed using agency theory. The model presented here meets three suggested objectives of an employer: 1. Maximization of utility through the maximization of profit 2. Ability to conform the risk characteristics of an employment contract to the risk characteristics of the employer 3. Diversification of the risk inherent in the employment contract Profit is maximized by producing cost savings associated with employee tenure and loyalty. Sharing cost savings with employees (i.e., offering a pension plan) meets the above objectives. The employer determines the optimal sharing rate for the expected cost savings. An examination of the employer's underlying decision process reveals implications for pension plan accounting which generally are consistent with and support the FASB's Statement of Financial Accounting Standards No. 87 [5].     

Computerization of a medium-sized hospital without an internal DP department

The conventional approach to computerization in a large hospital is to establish an internal data processing (DP) department and DP professionals then begin to develop computer-based information systems (CBIS). For medium-sized hospitals, however, this approach is doubtful. The limitation of financial resources and the lack of skilled systems professionals force the medium-sized hospital to consider different approaches. In this paper, we present a conceptual framework for the computerization of a medium-sized hospital based upon the viewpoint of top management. Based on the conceptual framework presented, a PC-based application software package has been developed, tested, and implemented at Cheng-Ching Hospital in Taiwan. Our study shows that a medium-sized hospital can initiate and conduct computerization in very short time with low cost and without an internal DP department.     

Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization

Hospital readmissions present an increasingly important challenge for health‐care organizations. Readmissions are expensive and often unnecessary, putting patients at risk and costing $15 billion annually in the United States alone. Currently, 17% of Medicare patients are readmitted to a hospital within 30 days of initial discharge with readmissions typically being more expensive than the original visit to the hospital. Recent legislation penalizes organizations with a high readmission rate. The medical literature conjectures that many readmissions can be avoided or mitigated by post‐discharge monitoring. To develop a good monitoring plan it is critical to anticipate the timing of a potential readmission and to effectively monitor the patient for readmission causing conditions based on that knowledge. This research develops new methods to empirically generate an individualized estimate of the time to readmission density function and then uses this density to optimize a post‐discharge monitoring schedule and staffing plan to support monitoring needs. Our approach integrates classical prediction models with machine learning and transfer learning to develop an empirical density that is personalized to each patient. We then transform an intractable monitoring plan optimization with stochastic discharges and health state evolution based on delay‐time models into a weakly coupled network flow model with tractable subproblems after applying a new pruning method that leverages the problem structure. Using this multi‐methodologic approach on two large inpatient datasets, we show that optimal readmission prediction and monitoring plans can identify and mitigate 40–70% of readmissions before they generate an emergency readmission.