基于合作的工程项目风险模糊评价新方法

工程项目的目标风险包括工期风险、费用风险、质量风险、安全风险和环境风险,研究已经表明,同一风险因素会同时对不同的目标产生影响.另外,工程项目管理各方在进行目标管理过程中的合作性也会对目标的最终完成产生影响.因此,将同一风险因素对不同目标的影响以不同的权重表示,并将项目管理各方的合作性作为目标风险的影响因素引入到风险评价中,利用模糊推理的方法建立了基于合作的工程项目风险评价模型,提出了工程项目风险分析的一种新方法.     

基于物理规划的工程项目综合均衡优化

质量、工期和成本是工程项目三大主要控制目标,对于工程项目中质量-工期-成本综合均衡优化问题,传统的基于权重的决策方法存在各目标权重难以合理确定的问题.为此引入物理规划方法建立工程项目质量-工期-成本综合均衡优化模型.决策者只需设定各目标的偏好,即可通过该均衡优化模型获得符合决策者偏好的优化方案,使决策过程更加符合工程实际,避免了确定无实际物理意义的各目标权重的问题.通过桥梁工程实例验证了该方法的有效性和实用性.     

基于可靠性理论的退化设备维修优化

设备可靠性、设备完好率等指标是衡量设备运行状态好坏的关键指标.以设备完好率、设备可靠性以及设备预防维修费用为决策目标,通过线性规划的方法对退化设备的预防维修策略进行了优化研究.在定量化模型中引入了设备检修的概念,并在此基础上构建了以可靠性为约束的预防维修费用模型和设备完好率模型,考虑到企业在生产活动的维修决策是靠多因素条件的约束,而构建了一个以维修费用、设备完好率、设备可靠性为决策目标的多目标规划模型,通过线性规划的方法求解了该策略下的最优维修策略.通过数值验证证明了模型的可行性.     

平板折叠桌最优设计的模型研究

现有一种创意平板折叠桌.建立初等函数模型,给出折叠桌的动态变化过程及开槽长度和桌脚边缘线的函数表达;根据"稳固性、加工难易、用材多少"等要求建立多目标函数的优化模型,采用基于多目标函数的"模拟退火"算法,求得一定条件下的最优设计方案.最后给出一种折叠桌设计软件的初等目标函数数学模型,并利用次模型设计出三角形、六边形两种新的创意平板折叠桌.     

车载跟瞄设备低轨卫星预测方法研究

车载跟瞄设备对低轨卫星进行测量时,常采用轨道预报获得卫星理论运行轨迹,光测设备以预报值作为引导值,搜索目标进行跟踪.跟踪过程中,由于云雾遮蔽或地影的影响,会使光测设备丢失目标.这时可采用理论引导的方式,利用预报轨道继续搜索跟踪.但由于轨道预报的误差较大,且目标过境时间有限,因此这种方法存在一定的缺陷.探讨利用修正目标跟踪脱靶量以及光测设备轴系误差后的实时跟踪值作为已知条件,采用改进的拉普拉斯方法对目标位置进行实时预测,解决上述问题.通过实时预测误差与轨道误差的比较,证明该方法的有效性.     

考虑返修的建设项目时间-成本-质量问题

当施工过程中质量不达标需要返修以改进项目质量,但相应地会影响项目工期和成本。本文基于经典多模式资源受限项目调度问题构建一种新的非线性规划模型,目标为项目成本最小和工期最短,其中项目成本考虑返修成本以提高项目质量。首先,使用二元非独立正态分布函数描述活动质量,根据活动间的串联或并联关系定义隐蔽工程质量为活动质量的函数。其次,本文提出一种基于NSGA的混合蛙跳算法,采用串行进度产生方案和调整的活动列表编码,其中蛙跳过程结合了遗传算法中的交叉操作和基于置换的局域搜索。最后,整个模型算法应用于框架铁路立交桥施工项目,验证本文算法性能在支配解数量和质量上都优于标准NSGA。     

基于工期-成本综合效用最大的关键链项目缓冲确定

为提高关键链中工期和成本的综合效用,研究一种基于关键链工期和成本进行双目标优化的缓冲确定方法。首先,该方法考虑项目不同工序间工期和成本之间的关系,并基于工期和成本的风险暴露度确定权重,解决不同工序间工期和成本偏好难以量化的问题;其次,根据权重多效用函数对工期和成本进行归一化处理,并确定综合效用最大情况下不同工序的最佳工期;最后基于尾部集中法确定项目缓冲。通过蒙特卡洛模拟实验,将此方法提取缓冲后的项目实际综合效用与传统方法进行比较。比较结果显示,该方法极大地提高项目中工期和成本的综合效用,并且对项目工期和成本均形成更有效的保护。     

基于随机活动工期的现金流动态均衡前摄性与反应性项目调度优化

承包商的现金流动态均衡对不确定条件下项目的顺利实施有重要影响。作者研究基于随机活动工期的现金流动态均衡前摄性及反应性项目调度问题,目标是在随机活动工期条件下,为承包商生成现金流均衡基准进度,并根据执行过程中的实际情况,动态地对其进行反应性调整。首先,通过建立前摄性调度优化模型生成基准进度,并提出两个反应性调度策略对其进行调整。其次,为以上诸模型的求解设计了模拟退火和禁忌搜索相结合的混合算法tabu-SA。最后,针对前摄性调度模型,在随机生成的算例集合上对算法进行测试,并进行大规模仿真实验。研究结果可以为随机活动工期下承包商保持现金流动态均衡、确保项目顺利实施,提供定量化决策支持。     

常态及故障条件下的多传感器任务管控调度研究

针对多传感器控制中的常态和故障情况问题,建立了描述常态和故障条件下的多传感器控制的多目标规划模型,通过偏离度指数,应用遗传算法求得常态控制问题最优解.其次将传感器故障转化成伪执行器故障运用改进的遗传算法,实现了多传感器故障情形下最优控制求解.仿真结果表明了最优控制方案的有效性.     

多目标资源受限项目鲁棒调度研究

由于资源受限项目调度属于NP-hard问题,传统的RCPSP主要集中于工期最短单一目标的基本问题研究,而忽略了项目调度对鲁棒性等多目标属性特征的要求。本文以经典的串行进度生成机制为基础,引入了衡量项目稳定性的鲁棒性要素,创建了项目鲁棒调度串行生成机制(RSSGS),提出了项目鲁棒性的测度新指标,构建了优化鲁棒结构的工期最短和鲁棒性最大的双目标优化模型,并结合分层优化原理,设计了改进的SA算法。最后,采用算例验证了该模型的可行性和合理性。     

Elective course planning

Efficient planning increasingly becomes an indispensable tool for management of both companies and public organizations. This is also the case for high school management in Denmark, because the growing individual freedom of the students to choose courses makes planning much more complex. Due to reforms, elective courses are today an important part of the curriculum, and elective courses are a good way to make high school education more attractive for the students. In this article, the problem of planning the elective courses is modeled using integer programming and three different solution approaches are suggested, including a Branch-and-Price framework using partial Dantzig-Wolfe decomposition. Explicit Constraint Branching is used to enhance the solution process, both on the original IP model and in the Branch-and-Price algorithm. To the best of our knowledge, no exact algorithm for the Elective Course Planning Problem has been described in the literature before. The proposed algorithms are tested on data sets from 98 of the 150 high schools in Denmark. The tests show that for the majority of the problems, the optimal solution can be obtained within the one hour time bound. Furthermore the suggested algorithms achieve better results than the currently applied meta-heuristic.     

Increasing the operational efficiency of container terminals in Australia

This paper discusses the major factors influencing the transfer efficiency of rail container terminals, as measured by the throughput time of containers. An analytically based simulation model is designed to describe container progress in the system. Cyclic heuristic rules for equipment assignment are applied and a new heuristic rule is developed to dispatch trains to tracks. The simulation model combined with the heuristic rules is used to address a number of specific objectives of the study. Different performance measures are applied and the impact that the train-to-track despatching and the handling equipment assignment can have on the measures is established. Validation and testing of models make use of data from Acacia Ridge Terminal, Brisbane, Australia.     

Accuracy of Gram–Schmidt orthogonalization and Householder transformation for the solution of linear least squares problems

Accuracy of a Gram–Schmidt algorithm for the solution of linear least squares equations is compared with accuracy of least squares subroutines in three highly respected mathematical packages that use Householder transformations. Results from the four programs for 13 test problems were evaluated at 16 digit precision on four different desktop computers using four different compilers. Singular values obtained from the different programs are compared and the effect of pivoting to improve the accuracy is discussed. Solution vectors from the program using the Gram–Schmidt algorithm were generally more accurate or comparable to solution vectors from the programs using the Householder transformations. © 1997 John Wiley & Sons, Ltd.     

Using aspiration levels in an interactive interior multiobjective linear programming algorithm

In this paper, we propose the use of an interior-point linear programming algorithm for multiple objective linear programming (MOLP) problems. At each iteration, a Decision Maker (DM) is asked to specify aspiration levels for the various objectives, and an achievement scalarizing function is applied to project aspiration levels onto the nondominated set. The interior-point algorithm is used to find an interior solution path from a starting solution to a nondominated solution corresponding to the optimum of the achievement scalarizing function. The proposed approach allows the DM to re-specify aspiration levels during the solution process and thus steer the interior solution path toward different areas in objective space. We illustrate the use of the approach with a numerical example.     

Using Approximation Algorithms to Build Evidence Factors and Related Designs for Observational Studies

Abstract

Observational or nonrandomized studies of treatment effects are often constructed with the aid of polynomial-time algorithms that optimally form matched treatment-control pairs or matched sets. Because each observational comparison may potentially be affected by bias, investigators often reinforce a single comparison with an additional comparison that is unlikely to be affected by the same biases, for instance using multiple control groups or evidence factors or control?+?instrument designs. Use of two comparisons affected by different biases may detect bias if the two comparisons disagree, or may show that two comparisons with different weakness concur in their conclusions. Even this simplest addition—a second comparison—creates design problems without polynomial-time solutions. Faced with a problem that no polynomial-time algorithm can solve, a so-called approximation algorithm is a type of compromise: it provides a solution in polynomial time that is provably not much worse than the unattainable optimal solution. Building upon existing techniques for related problems in operations research, we develop an approximation algorithm for minimum distance matching with near-fine balance for three comparison groups. This algorithm is a practical approach to most observational designs that add a second comparison. The method is applied to an observational study of the effects of side airbags on injury severity in the U.S. Fatality Analysis Reporting System. For many car makes and models, side airbags were initially unavailable, then later available as optional equipment for an additional fee, then still later provided as standard equipment. Within sets matched for make and model of car, for safety belt use, for direction of impact, and other covariates, we compare crashes in these three periods, where each comparison has different limitations. The method is implemented in the R package approxmatch, whose example reproduces some of the calculations. Supplementary materials for this article are available online.     

The multicriteria analysis for construction of benchmarkers to support the Clinical Engineering in the Healthcare Technology Management

In developing countries, approximately half of the healthcare equipments are not in full use and the main cause of this is the inadequate management of them. The Clinical Engineering might face this problem in the healthcare environment analyzing the equipment with a health technological process perspective to identify opportunities of improvements. Generally, the inadequate management is a result of lack of systemized and contextualized information about the health technological process. The effort to make an adequate management generates an increasing interest in the use of benchmarker. Currently, the benchmarkers used by the Clinical Engineering are not representative of a health technological process as a whole. Health technological process concept is stated in this article. The multicriteria analysis methodology MCDA (Multicriteria Decision Aid) is used to obtain benchmarkers and to identify opportunities of improvements, thus generating conditions for that the Clinical Engineering consolidates its relevant contribution for the healthcare.     

求群体多目标决策问题的一种可行方向交互算法

对于群体多目标决策问题,决策者可以各自的关于目标之间的权衡比表达其偏爱信息并进行决策.当个体权衡比具有加性性质时可得群体权衡比.本文以此构造一种求解群体非线性规划问题的交互算法.迭代中基于求解决非线性规划的Topkis-Veinott方法构造可行方向.在一定的条件下,算法收敛于所讨论问题的群体满意解.     

多周期公用工程系统运行的模型,优化方法与应用

针对多周期公用工程系统的运行优化问题，考虑了设备的启停费用的情况下。建立了混合整数非线性规划模型并证明了最优解的存在性。针对该运行优化问题本将其分解成若干子问题，然后利用改进的Hooke-Jeeves优化算法求解每个子问题。应用于具体实例，其数值结果与其它方法得到的相比。运行时间短，且更适合多周期公用工程问题的求解。     

A hybrid inexact-stochastic water management model

An inexact-stochastic water management (ISWM) model is proposed and applied to a case study of water quality management within an agricultural system. The model is based on an inexact chance-constrained programming (ICCP) method, which improves upon the existing inexact and stochastic programming approaches by allowing both distribution information in B and uncertainties in A and C to be effectively incorporated within its optimization process. In its solution process, the ICCP model (under a given pi level) is first transformed into two deterministic submodels, which correspond to the upper and lower bounds for the desired objective function value. This transformation process is based on an interactive algorithm, which is different from normal interval analysis or best/worst case analysis. Interval solutions, which are feasible and stable in the given decision space, can then be obtained by solving the two submodels sequentially. Thus, decision alternatives can be generated by adjusting decision variable values within their solution intervals. The obtained ICCP solutions are also useful for decision makers to obtain insight regarding tradeoffs between environmental and economic objectives and between increased certainties and decreased safeties (or increased risks). Results of the case study indicate that useful solutions for the planning of agricultural activities in the water quality management system have been obtained. A number of decision alternatives have been generated and analyzed based on projected applicable conditions. Generally, some alternatives can be considered when water quality objective is given priority, while the others may provide compromises between environmental and economic considerations. The above alternatives represent various options between environmental and economic tradeoffs. Willingness to accept low agricultural income will guarantee meeting the water quality objectives. A strong desire to acquire high agricultural income will run into the risk of violating water quality constraints.     

Joint spare parts inventory and reliability decisions under a service constraint

Reliability and inventory levels of spare parts are major factors that determine the service level for the maintenance of machines provided by original equipment manufacturers (OEMs). In general, decisions on reliability and stock levels are made separately in practice, and academic literature offers little guidance on how to jointly make these two decisions. In order to fill in the gap in the literature and provide guidance to OEMs, we jointly model reliability and inventory problems. We consider three different service measures: aggregate fill rate, average downtime per system per year and expected total number of long downs in a year. Our models minimize the sum of holding and emergency shipment costs subject to a limited reliability improvement budget and a target service level. We develop an algorithm that considers reliability and inventory decisions simultaneously, test our solution approach on real-life and randomly generated data sets and compare the results with an approach that considers reliability and inventory decisions sequentially. Numerical results show substantial benefits of integrating reliability and inventory decisions.