多资源约束下的工序排序的一个准则及算法

在ＰＥＲＴ和ＣＰＭ中，对于资源约束优化的工序排序准则大多数明要用量小时差法。本文提出的多资源约束下新的工序排序准，使工程计划安排满足资源约束，且使总工期尽可能不拖延或拖延最少。     

员工配置资源行为的优化研究:自适应组织的视角

组织目标必须通过约束体系规范员工行为得以实现.进行组织约束下员工行为的定量研究,这对保证组织健康稳定发展非常必要.员工自身适应组织目标,服从组织的约束体系,配置好自有的资源,使得自身行为得以优化的研究较少.本文简要回顾了经济学理论、组织行为学、人力资源管理等方面对员工配置资源行为优化的研究及应用.界定了组织资源,并将其分为三种类型.在约束体系给出的基础上,将组织对员工行为的控制、要求或激励转为员工在之上或之下的运行约束;认为约束体系具有离散、悬河效应的特点;得出了评价组织约束体系的两种算法.讨论了员工资源及行为.尝试构造了基于员工适应组织约束下,员工的多目标、非线性,既有连续又有离散约束条件的配置资源行为优化的定量模型,并在基于员工利益最大化的前提下,对模型求解最优解的几种情况进行了研究.希望能够填补员工自身适应组织目标,行为优化定量模型这方面研究的空白.     

资源约束下的投资优化模型

资源约束下的投资问题在决策中很常见.本文提出运用边际净现值比较的思想来解决资源约束下多项目多地区投资决策问题的观点,并且尝试用"区域影响力系数"来衡量区域经济规模对投资项目的影响.在此基础上建立了资源约束下的投资优化模型,并给出解决此类问题的方法.     

解线性约束优化问题的新锥模型信赖域3法

本文提出了一个解线性等式约束优化问题的新锥模型信赖域方法．论文采用零空间技术消除了新锥模型子问题中的线性等式约束，用折线法求解转换后的子问题，并给出了解线性等式约束优化问题的信赖域方法．论文提出并证明了该方法的全局收敛性，并给出了该方法解线性等式约束优化问题的数值实验．理论和数值实验结果表明新锥模型信赖域方法是有效的，这给出了用新锥模型进一步研究非线性优化的基础．     

资源约束项目调度中重心启发式方法研究

本文目的在于研究一种新的资源约束项目的启发式方法。研究中通过两个平行工序转化为顺序工序的优化问题，提出了工序的重心的概念，并将其引入到资源约束项目调度问题的启发式方法中，提出了一个新的启发式方法——基于重心的启发式方法。在此基础上，本文还对该启发式方法的进行了评价。首先将该方法程序化；然后选取PSPLIB中的问题实例进行计算；最后，在分析计算结果的基础上，对该方法的求解效果和执行效率进行评价。评价结果说明这种新方法求解效果符合实际需要并且计算效率很高。     

安全约束条件下的经济调度优化模型研究

发电侧放开竞争的电力系统需要更加有效、准确的决策工具对有限的资源进行调度规划。短期经济调度优化问题是一个混合整数非线性规划问题,很难得到有效最优解,尤其是对于大规模电力系统。为了提高求解效率,本文提出了一个考虑安全约束的经济调度优化模型(Security-Constrained Economics Dispatch,SCED),主要采用线性化思想处理经济调度优化问题的模型以及各种约束,采用基于校正的交替求解方法,使得调度优化结果在运行成本最小化的前提下满足系统的安全稳定约束。同时,将本文方法运用到IEEE 30节点系统进行测试,从而验证本文方法有效性。     

资源利用与能耗约束下铁矿采选品位的智能优化

集成非线性目标规划模型与差分进化(DE)及神经网络(ANN)等智能算法,提出了资源利用和节能降耗约束下铁矿采选生产品位的动态优化方法。首先建立以截止品位和入选品位为决策变量,精矿产量、资源利用率、总用电量以及经济效益为目标约束的非线性目标规划模型,模型中包括损失率、选矿金属回收率和采选成本三个非线性函数;然后将所构建的非线性目标规划模型转化成无约束优化问题,将DE的高效寻优能力和ANN的建模功能相结合,构成DE-ANN算法来搜索最优采选品位组合;最后以D铁矿为例进行研究,结果表明了所提出方法的有效性。该方法综合考虑了资源利用、节能降耗等因素,为新时期铁矿的采选品位优化提供了科学可行的思路。     

面向应急对地观测任务的多平台资源部署优化研究

提出了一种基于遗传算法的面向应急对地观测任务的多平台资源部署优化方法。该方法通过把观测区域离散化为网格点的集合,将多平台资源部署问题形式化为一个组合优化问题,其目标是在一定响应时间约束下最大化观测区域覆盖率。设计的求解算法采用整数编码表示各平台资源的部署位置,使用精英保留策略加快算法收敛速度。仿真结果表明,该方法能够快速获得满意的卫星、飞艇、无人机多平台资源部署方案。     

不等式约束优化问题的精确罚函数法

1引言 在约束最优化的研究中，罚函数法有很高的理论及应用价值，为求约束优化问题的最优解x，很多方法是通过求解一系列优化问题来实现，人们称之为SUMT方法~[1].     

基于资源互补的项目伙伴选择方法

当不同企业承担的工程项目在同一地区同时开展时,彼此间经常会存在工程资源互补的可能,如果加以合理利用,便能够使项目的实施进一步得到优化。因此,企业如何选择有资源互补潜力的最佳合作伙伴有必要予以研究。文中从合作博弈的角度阐述了资源互补的形成机理,定性分析了伙伴选择问题的决策特征;运用双层规划构建了基于资源互补的项目伙伴选择模型,提出了基于期望收益约束选择的求解方法。最后以某临港产业园区填海项目与其他项目的资源互补为例,采用文中提出的模型及求解方法优化施工方案,得到降低建设投资、缩减施工工期的优化效果。     

Heuristic Performance and Network/Resource Characteristics in Resource-constrained Project Scheduling

The problem considered in this study is that of non-pre-emptive scheduling of the activities in a project network to minimize project duration under limited resource availabilities. Various heuristic rules and optimization techniques have been applied to this problem, and comparisons of their effectiveness have been made in the literature. However, no thorough investigation of the types of network and resource characteristics which play an underlying role in determining heuristic performance and which account for the variability of results has been made previously. In this study, a new heuristic rule which compares favourably with the widely-used heuristic rules is developed, and the influence of network/resource characteristics on the performance of different heuristic rules is investigated.     

A single-resource revenue management problem with random resource consumptions

We study a single-resource multi-class revenue management problem where the resource consumption for each class is random and only revealed at departure. The model is motivated by cargo revenue management problems in the airline and other shipping industries. We study how random resource consumption distribution affects the optimal expected profit and identify a preference acceptance order on classes. For a special case where the resource consumption for each class follows the same distribution, we fully characterize the optimal control policy. We then propose two easily computable heuristics: (i) a class-independent heuristic through parameter scaling, and (ii) a decomposition heuristic that decomposes the dynamic programming formulation into a collection of one-dimensional problems. We conduct extensive numerical experiments to investigate the performance of the two heuristics and compared them with several widely studied heuristic policies. Our results show that both heuristics work very well, with class-independent heuristic slightly better between the two. In particular, they consistently outperform heuristics that ignore demand and/or resource consumption uncertainty. Our results demonstrate the importance of considering random resource consumption as another problem dimension in revenue management applications.     

An investigation into the relationship of heuristic performance with network-resource characteristics

Constrained resource project management heuristics are analyzed and their performance is assessed related to well defined project types. Such results have shown that using the overall performance measure of a given heuristic can, in some situations, be misleading.An efficient heuristic, which combines resource and criticality factors is also proposed. Computational experiments on a set of 6120 networks varying in size from 45–666 activities, 3 resources, and under different network parameters are reported. The proposed heuristic outperforms the best existing dispatching rules in certain project classes and also on problem sets appearing in the literature.     

A Comparison of Heuristics for the Discrete Cost Multicommodity Network Optimization Problem

In this paper, approximate solutions algorithms for discrete cost multicommodity network optimization problems are presented and compared. Firstly, extensions of classical greedy heuristics, based on link-rerouting and flow-rerouting heuristics, are presented in details. Secondly, a new approximate solution algorithm, which basically consists of a heuristic implementation of the exact Benders-type cutting plane generation method, is proposed. All these algorithms are extensively compared on randomly generated graphs up to 50 nodes and 90 links. It clearly appears that this new Benders-type approach is very promising since it produces the best heuristic solutions.     

A new heuristic for the multi-mode resource investment problem

This paper considers the problem of minimizing resource investment required to execute the tasks in a project network by a given project due date. A project consists of non-pre-emptive tasks executed in a known and required precedence order. Each task is completed in one of its feasible modes, which may differ not only in task duration but also in consumption of renewable resources. A priority rule heuristic with polynomial computational complexity is presented for this computationally intractable problem. This heuristic simultaneously considers due date constraints and resource usage to select and schedule tasks with one decision rule. This differs from prior multi-mode priority rule scheduling heuristics that apply two consecutive decision rules to schedule tasks. Extensive computational testing indicates promising results.     

Heuristics for multi-period capacity expansion in local telecommunications networks

In this paper, different heuristics are devised to solve a multi-period capacity expansion problem for a local access telecommunications network with a tree topology. This expansion is done by installing concentrators at the nodes and cables on the links of the network. The goal is to find a least cost capacity expansion strategy over a number of periods to satisfy the demand. A local search heuristic is first proposed to improve previously reported results on problem instances based on different cost and demand structures. This heuristic is then integrated into a genetic algorithm to obtain further improvements.     

一种新的交通网络设计优化算法

交通网络设计问题是研究如何用定量的方法在已有交通网络上添加或扩容某些路段的问题.文章在回顾交通网络设计问题文献的基础上,提出了基于图论网络优化思想的解决该类问题的一种新思路,给出了启发式算法,并进行了算法复杂性分析,最后通过算例验证了其有效性.     

A new bidirectional search algorithm with shortened postprocessing

For finding a shortest path in a network bidirectional A^∗ is a widely known algorithm. This algorithm distinguishes between the main phase and the postprocessing phase. The version of bidirectional A^∗ that is considered the most appropriate in literature hitherto, uses a so-called balanced heuristic estimate. This type of heuristic is chosen, as it accounts for a short postprocessing phase. In this paper, we do not restrict ourselves any longer to balanced heuristics. First, we introduce an algorithm containing a new method for the postprocessing phase, reducing this phase considerably for non-balanced heuristics. For a balanced heuristic the new algorithm is nearly equivalent to the existing versions of bidirectional A^∗. An obvious choice for a non-balanced heuristic turns out to be superior in terms of storage space and computation time. Second, we show that the main phase on its own, when using this non-balanced heuristic estimate, is a useful algorithm, which provides us quickly with a feasible approximation.     

Priority rule-based heuristic for multi-mode resource-constrained project scheduling problems with resource vacations and activity splitting

This paper presents a priority rule-based heuristic for the multi-mode resource-constrained project scheduling problem with the splitting of activities around unavailable resources allowed. All resources considered are renewable and each resource unit may not be available at all times due to resource vacations, which are known in advance. A new concept called moving resource strength is developed to help identify project situations where activity splitting is likely to be beneficial during scheduling. The moving resource strength concept is implemented in priority rule-based heuristics to control activity splitting when scheduling. Multiple comparisons of the performance of combination of activity–mode priority rules used in the heuristics are provided. Computational experiments demonstrate the effectiveness of the heuristic in reducing project makespan, and minimizing activity splitting.     

Resource levelling for projects with schedule-dependent time windows

The paper presents polynomial heuristic procedures for different types of resource levelling problems for projects with minimum and maximum time lags between project activities. Both problems without and with explicit resource constraints are treated. Thus far, only pseudopolynomial heuristics for special resource levelling problems without maximum time lags and resource constraints have been proposed. An experimental performance analysis shows that the new heuristics approximately solve problem instances with up to 500 activities and five resources within reasonable computing time.