新产品研发项目GERT模型及其模拟求解

本文以新产品研发项目—— V CDM3型真空树脂灌注机的研发过程为背景 ,利用随机网络技术建立了该项目的 GERT模型 ;基于 GERT模型的简化递推算法 ,作者编制了专用软件对该模型进行了模拟求解 ,获得了项目完成时间的各种统计特征值和概率分布直方图 ;根据模拟计算结果 ,作者进一步对项目完成时间进行了统计推断 ,从而为项目进度的控制和决策提供了定量化的参考依据 .     

工期与收益的最优化模型

运用经济学原理及最优决策方案 ,建立了如何选择最优工期和制定奖惩措施的动态优化模型 .利用泛函变分法求出在各种情况下的最优工期 ,并确定出影响最优工期的各种因素 ;然后确定出使双方都有利的激励强度 ;最后利用计算机对模型进行了模拟分析 ,并对结果给出了详细的分析 .     

基于蒙特卡洛方法的网络计划资源优化

蒙特卡洛方法由计算机产生伪随机数而生成试验点,根据约束条件找到优化解,据此编制C语言程序,可以成功求解网络计划的工期固定-资源均衡优化、资源有限-工期最短优化和资源有限-工期最短-资源均衡优化,并给出蒙特卡洛方法得到最优解的概率.算例结果表明:相同条件下,基于蒙特卡洛方法的工期固定-资源均衡优化方案资源方差较粒子群算法小,基于蒙特卡洛方法的资源有限-工期最短优化方案工期较遗传算法短,基于蒙特卡洛方法的资源有限-工期最短-资源均衡优化方案工期较遗传算法短.     

随机活动工期下求解资源约束项目最大期望净现值的动态调度算法

本文研究了随机活动工期下如何调度资源约束项目使得项目的期望净现值最大。首先对问题进行了界定,建立了相应的优化模型,其次针对问题的特点设计了一种动态规划算法。在算法设计的过程中,本文通过对项目网络图结构及不同状态最优值之间关系的分析,优化了动态规划算法状态的生成过程及状态最优值的求解过程,从而加快了算法的求解。使用随机生成的540个不同规模、不同结构的仿真案例对算法的有效性进行了验证,并分析了项目网络特征对算法效率的影响。实验发现:项目的次序强度对算法所需时间有着较大的影响,随着项目次序强度的减小,生成的状态数量会增加,从而计算时间也会增加。本文的研究可以为不确定环境下的项目调度提供决策支持。     

基于收益管理和仿真的第三方仓储资源分配

针对不确定市场需求条件下第三方仓储资源的能力规划与分配问题,构建随机数学规划模型,理论分析证明了最优资源分配量的存在性,并指出最优资源分配量是单位资源成本的递减函数、单位资源收益和单位损失成本的递增函数。鉴于解析求解的复杂性,基于收益管理思想,结合离散事件仿真技术和响应曲面法,提出一种新的分析求解框架:收益管理用于细分顾客、构建资源分配策略,仿真模型刻画系统随机特性并评估系统绩效指标,响应曲面法则优化分配策略并探寻绩效改进方向。案例研究和仿真实验结果显示,根据顾客类别分配仓储能力的策略优于传统的先到先服务策略,收益管理、响应曲面法与仿真的综合集成,能够提高系统收益,从而使本文所提方法体系得到了有效验证。     

带有资源转移时间的RCPSP资源流模型及算法

本文在传统资源受限项目调度问题(resource-constrained project scheduling problem, RCPSP)中引入资源转移时间,为有效获得问题的最优解,采用资源流编码方式表示可行解,建立了带有资源转移时间的RCPSP资源流优化模型,目标为最小化项目工期。根据问题特征设计了改进的资源流重构邻域算子,分别设计了改进的禁忌搜索算法和贪心随机自适应禁忌搜索算法求解模型。数据实验结果表明,相较于现有文献中的方法,所提两种算法均可针对更多的项目实例求得最优解,并且得到最优解的时间更短,求解效率更高。此外,分析了算法在求解具有不同特征的项目实例时的性能,所得结果为项目经理结合项目特征评价算法适用性提供了指导。     

混合动力公交车能量控制策略的优化模型

针对混合动力公交车在循环工况内功率需求的特点,建立了未来功率需求贝叶斯预测模型;利用2-阶段随机动态规划模型将大规模的随机动态规划问题简化为多个小规模的随机动态规划问题和一个确定型动态规划问题;对于随机动态规划模型的求解,给出了稀疏表示的降维方法,将复杂的泛函极值问题转化为常规的随机动态优化问题,并采用分布估计算法和计算资源最优配置算法的计算机仿真优化算法对随机动态优化问题进行求解;给出了基于查表的在线控制策略,为模型的实际应用进行了有益的探索。     

具有CON/SLK交货期指派的一类单机排序问题

研究工件的实际加工时间既具有指数学习效应,又依赖所消耗资源的准时制排序问题.在模型中,探讨了共同交货期(CON)和松弛交货期(SLK)两种情形.管理者的目标是确定最优序、最优资源分配方案和最佳工期(共同交货期或松弛交货期)以便极小化工件的总延误、总提前、总工期和资源消耗费用的总和.对于工件的实际加工时间是资源消耗量的线性函数的排序问题,通过将其转化为指派模型,给出了时间复杂性为O(n~3)的算法,从而证明该类排序问题是多项式时间可求解的.针对工件的实际加工时间是资源消耗量的凸函数的排序问题,也给出了多项式算法.     

多因素指派模型全局优化问题研究

基于多因素资源优化分配问题的不确定性,建立基于区间数型下的不确定多因素指派模型,给出模型建立的理论依据与全局优化算法,拓展区间数型多因素指派模型,解决了不确定条件下多因素资源优化分配问题.考虑多因素影响,基于任务完成效率,以5类任务多因素分配问题为例,获得了指派模型全局优化的解.为不确定条件下资源优化分配问题的研究拓宽了决策途径.     

基于模糊参数的随机网络F-GERT模型研究

针对随机网络研究中活动参数的不确定,提出了模糊随机网络F-GERT模型,利用梯形模糊数表示F-GERT模型中的活动参数,给出了串联结构、并联结构和自环结构F-GERT模型中活动参数的计算方法,最后文中给出了算例,丰富了GERT网络研究内容。     

Structural properties of the optimal resource allocation policy for single-queue systems

This paper studies structural properties of the optimal resource allocation policy for single-queue systems. Jobs arrive at a service facility and are sent one by one to a pool of computing resources for parallel processing. The facility poses a constraint on the maximum expected sojourn time of a job. A central decision maker allocates the servers dynamically to the facility. We consider two models: a limited resource allocation model, where the allocation of resources can only be changed at the start of a new service, and a fully flexible allocation model, where the allocation of resources can also change during a service period. In these two models, the objective is to minimize the average utilization costs whilst satisfying the time constraint. To this end, we cast these optimization problems as Markov decision problems and derive structural properties of the relative value function. We show via dynamic programming that (1) the optimal allocation policy has a work-conservation property, and (2) the optimal number of servers follows a step function with as extreme policy the bang-bang control policy. Moreover, (3) we provide conditions under which the bang-bang control policy takes place. These properties give a full characterization of the optimal policy, which are illustrated by numerical experiments.     

Time complexity of single- and identical parallel-machine scheduling with GERT network precedence constraints

In this paper we deal with the time complexity of single- and identical parallel-machine scheduling problems in which the durations and precedence constraints of the activities are stochastic. The stochastic precedence constraints are given by GERT networks. First, we sketch the basic concepts of GERT networks and machine scheduling with GERT network precedence constraints. Second, we discuss the time complexity of some open single-machine scheduling problems with GERT network precedence constraints. Third, we investigate the time complexity of identical parallel-machine scheduling problems with GERT network precedence constraints. Finally, we present an efficient reduction algorithm for the problem of computing the expected makespan for the latter type of scheduling problem.     

高校人力资源配置优化模型的研究

通过对高校人力定岗、现有人力考核、人才引进考核三个方面进行阐述,给出了构建高校人力资源指标体系的原则、方法和流程.运用数理统计和AHP方法(层次分析法)建立了高校教师定岗模型和现有人员考核模型,对高校人事管理部门对未来的师资建设提供了参考依据。     

Resource allocation problem in a two-sector economic model of special form

In the present paper, we study the resource allocation problem for a two-sector economic model of special form, which is of interest in applications. The optimization problem is considered on a given finite time interval. We show that, under certain conditions on the model parameters, the optimal solution contains a singular mode. We construct optimal solutions in closed form. The theoretical basis for the obtained results is provided by necessary optimality conditions (the Pontryagin maximum principle) and sufficient optimality conditions in terms of constructions of the Pontryagin maximum principle.     

Resource capacity allocation to stochastic dynamic competitors: knapsack problem for perishable items and index-knapsack heuristic

In this paper we propose an approach for solving problems of optimal resource capacity allocation to a collection of stochastic dynamic competitors. In particular, we introduce the knapsack problem for perishable items, which concerns the optimal dynamic allocation of a limited knapsack to a collection of perishable or non-perishable items. We formulate the problem in the framework of Markov decision processes, we relax and decompose it, and we design a novel index-knapsack heuristic which generalizes the index rule and it is optimal in some specific instances. Such a heuristic bridges the gap between static/deterministic optimization and dynamic/stochastic optimization by stressing the connection between the classic knapsack problem and dynamic resource allocation. The performance of the proposed heuristic is evaluated in a systematic computational study, showing an exceptional near-optimality and a significant superiority over the index rule and over the benchmark earlier-deadline-first policy. Finally we extend our results to several related revenue management problems.     

A genetic approach for strategic resource allocation planning

Enterprises often implement a measurement system to monitor their march towards their strategic goals. Although this way it is possible to assess the progress of each goal, there is no structured way to reconsider resource allocation to those goals and to plan an optimal (or near optimal) allocation scheme. In this study we propose a genetic approach to match each goal with an autonomous entity (agent) with a specific resource sharing behavior. The overall performance is evaluated through a set of functions and genetic algorithms are used to eventuate in approximate optimal behavior’s schemes. To outline the strategic goals of the enterprise we used the balanced scorecard method. Letting agents deploy their sharing behavior over simulation time, we measure the scorecard’s performance and detect distinguished behaviors, namely recommendations for resource allocation.     

工程建设企业多项目人力资源配置研究——基于胜任力和两阶段优化视角

主要研究工程建设企业多项目人力资源优化配置问题。以项目经理这一关键性人力资源为对象,构建了基于胜任力模型的工程建设企业项目经理能力定级指标体系,给出了具体的能级评价方法,解决了待分配人员的定级和排序问题;在项目优先级和人员能级界定的基础上,以多项目工期均衡和人工总成本最低为目标建立了两阶段优化模型,给出了基于贪婪算法和线性规划的模型求解算法,设计了MATLAB程序对模型进行求解;最后通过实例验证了优化配置模型和求解算法的有效性。     

Hybrid flowshop scheduling with machine and resource-dependent processing times

Most of research in production scheduling is concerned with the optimization of a single criterion. However the analysis of the performance of a schedule often involves more than one aspect and therefore requires a multi-objective treatment. In this research, with combination of two multiple objective decision-making methods, min–max and weighted techniques, a new solution presentation method and a robust hybrid metaheuristic, we solved sequence-dependent setup time hybrid flowshop scheduling problems. In this paper for reflecting real-world situation adequately, we assume the processing time of each job depends on the speed of machine and amount of resource allocated to each machine at the stage which is processed on it. In formulation of min–max type, the decision-maker can have the flexibility of mixed use of weights and distance parameter in expressing desired improvement on produced Pareto optimal solutions. To minimize makespan and total resource allocation costs, the proposed hybrid approach is robust, fast, and simply structured, and comprises two components: genetic algorithm and a variable neighborhood search. The comparison shows the proposal to be very efficient for different structure instances.