软件开发项目多技能人力资源均衡调度模型

为了在软件开发项目中同时解决项目活动调度问题和多技能人员指派问题,实现资源均衡分配,本文建立了一个多技能人力资源均衡调度的混合整数规划模型,并以一个软件开发项目为背景开展了算例研究,采用Lingo11软件环境下改良后的分支定界法进行优化求解。优化之后,项目资源强度方差和员工工作量方差大幅度下降。研究结果表明,本文创建的资源均衡调度模型可以显著提高项目周期内员工需求量和员工工作量分配的均衡水平。     

项目员工力[I班调度多目标优化模型与算法

为最小化加班对项目工期、人力资源成本和总加班时间的影响,建立了项目员工加班调度多目标优化模型。首先,采用初始化模块生成各活动的可行加班方案;然后,使用调度模块计算确定每一种调度方案的项目工期、人力资源成本和总加班时间;最后,采用并列选择遗传算法得到多目标Pareto解集。研究结果表明,使用该模型能够有效的确定出工期短、人力资源成本低和总加班时间少的员工加班调度方案集合,从而为项目管理者提供有力的加班调度决策支持。     

研发型企业多项目人力资源调度研究——基于蚁群优化的超启发式算法

现阶段,研发型企业的项目处于多项目环境下,为了解决多项目并行时人力资源争夺问题,本文针对该类企业多项目管理中人力资源调度进行优化研究,以考虑项目延期惩罚成本的最小总成本为目标函数,将现实问题抽象建模。基于国内外的研究提出了一种超启发式算法进行求解,该算法将人力资源调度问题分为项目活动分配和人员选择项目活动两个部分,采用蚁群优化作为高层启发式策略搜索低层启发式规则,再进一步根据规则解构造出可行解。最后本研究设计多组仿真实验与启发式规则进行对比,结果表明该算法有较好的搜索性能,为人力资源的调度问题提供了新的解决方案。     

基于E-CARGO模型的志愿型偏好平衡性指派

个人偏好与团队执行力的平衡性指派方法,是国内外人力资源管理、决策支持领域重点关注的问题。为寻找兼顾团队与个人利益的指派平衡点,针对员工志愿型偏好平衡性指派问题,结合“基于角色的协作”（role-based collaboration,RBC）工程理论与方法及其E-CARGO模型,提出了一种兼顾个人志愿型偏好与团队执行力的指派方法,并通过实验仿真论证了方法的有效性与可靠性。该方法能使团队效能在考虑个人偏好的情况下尽可能最大化,优化人员生产与管理,支撑行业决策。     

基于人员-任务-资源匹配度的产品设计项目静态调度

设计人员和技术资源的特点使得产品设计项目优化调度建模及求解的复杂性大大增加。首先对产品设计项目中的三大主要要素的属性进行了深入地分析,并提出了任务-人员-资源匹配度的评价指标和计算方法;其次,借助多模式资源受限问题的思想,将设计人员和技术资源分别考虑,并将设计人员、技术资源和设计任务三者的匹配度以及技术资源强度引入到调度模型中,建立了针对产品设计项目的多模式资源受限调度数学模型;最后,设计了一种双层单亲遗传算法,实现了对模型的求解,得到了工期最短目标下最优的任务、设计人员、技术资源调度方案。案例分析结果表明,基于人员-任务-资源匹配度的多模式优化调度模型充分体现了设计项目的特点,有利于人力资源和技术资源的优化配置。     

复杂时空网络冲突消解群组角色指派研究

为了解决因孤立时空约束而导致的多项任务指派的协同失效和全局优化性能急剧下降问题,使用角色协同理论(role-based collaboration)及其通用模型E-CARGO的子模型群组角色指派(group role assignment),以机场登机口调度为例,对问题进行指派时空约束形式化建模;分析不同代理承担不同角色、不同代理承担同个角色的协作情况,从而建立量化评估矩阵与协作矩阵;继而对时空约束进行解耦与消解,采用整数规划在追求协作空间利用率最大化的同时,考虑平衡旅客偏好,对问题进行多目标求解。大规模仿真实验论证了模型与方法的一般性、有效性和可靠性。此外,与传统GRA模型相比,主体利益指标提升6.21%,客体偏好指标提升9.72%,实现秒级求解,满足了复杂时空网络下的任务分配快速指派响应要求。     

研发项目群人力资源调度研究

关于人力资源受限项目调度这一备受调度领域学者们高度关注的重要问题,将可更新资源延展至胜任力有别的人力资源之中,基于此建立一个含有胜任力水平的模型,此模型与项目群人力资源调度现实情境比较贴合。首先选取了多个评估人员胜任力的指标,并且提出了胜任力的计算公式,再将最小化项目群总工期和最小化项目群总成本作为双重目标,由此创建了考虑人员胜任力差异的数学优化模型。基于模型假设可绘制出相应的多项目初始网络图;通过枚举算法列举出符合假设条件的可行解集;之后提出以动态规划方法为基础的算法展开分阶段寻优,以双目标重要程度的高低进行降序排列,逐一确定总工期最优解和总成本最优解。根据实验结果发现,将胜任力差异纳入到数学优化模型的构建在求解方面优势明显,具有较好的应用价值。     

基于混合智能算法的多资源约束项目优化调度

 基于关键链项目管理(critical chain project management,CCPM),利用一种混合智能优化算法求解多资源约束项目调度问题,解决传统调度优化算法的不足,提高这类问题的求解质量.首先利用一类启发式算法识别项目关键链,并设置缓冲区尺寸,以项目总工期和在制品库存为优化目标建立数学模型,设计一种混合智能优化算法求解,并对项目问题库中的j30hrs.sm问题和某生产型项目进行实验分析,得到优化的结果.实验表明,采用混合智能算法求解这类问题能得到明显优于一般调度优化算法的结果.     

基于行为效应的MRO服务中心多技能工调度优化

人力资源是MRO服务中心调度的重要因素。将多技能工调度与员工行为效应即学习和遗忘效应相结合,以最小化任务总完工时间为目标建立模型,并用分段染色体遗传算法进行求解。在模型中考虑多技能工掌握的技能和任务的技能需求相匹配,并考虑员工在一个任务点可以使用其掌握的多项技能。通过算例对模型进行分析,发现任务数量和员工数量对任务总完工时间和作业时间有影响,且考虑行为效应的指派能优化员工安排,缩短任务总完工时间。说明人的行为效应对企业实际利益有重要影响。     

启发式算法在软件项目人力资源均衡的问题应用

多项目多资源均衡是项目调度是强NP问题,因此不存在多项式的解法,首先分析国内某中型软件企业人力资源的利用现状,据此借用人力资源均衡使用数学模型,接着利用多项目多资源均衡的启发式算法,结合软件开发企业人力资源利用的实际情况,对其人力资源在项目计划进行验证,实践证明,所采用的数学模型和启发式算法是可行的。     

Interference-limited resource allocation for cognitive radio in orthogonal frequency-division multiplexing networks

Efficient and fair resource allocation strategies are being extensively studied in current research in order to address the requirements of future wireless applications. A novel resource allocation scheme is developed for orthogonal frequency-division multiplexing (OFDM) networks designed to maximise performance while limiting the received interference at each user. This received interference is in essence used as a fairness metric; moreover, by defining different interference tolerances for different sets of users, the proposed allocation scheme can be exploited in various cognitive radio scenarios. As applied to the scheme, the authors investigate a scenario where two cellular OFDM-based networks operate as primary and secondary systems in the same band, and the secondary system benefits by accessing the unused resources of the primary system if additional capacity is required. The primary system benefits either by charging the secondary system for the use of its resources or by some form of reciprocal arrangement allowing it to use the secondary system's licenced bands in a similar manner, when needed. Numerical results show our interference-limited scheduling approach to achieve excellent levels of efficiency and fairness by allocating resources more intelligently than proportional fair scheduling. A further important contribution is the application of sequential quadratic programming to solve the non-convex optimisation problems which arise in such scenarios.     

A desired load distribution model for scheduling of unrelated parallel machines

Scheduling problems concern the allocation of limited resources over time among both parallel and sequential activities. Load balancing has been adopted as an optimization criterion for several scheduling problems. However, in many practical situations, a load-balanced solution may not be feasible or attainable. To deal with this limitation, this paper presents a generic mathematical model of load distribution for resource allocation, called desired load distribution (DLD). The objective is to develop a DLD model for scheduling of unrelated parallel machines that can be used both in centralized resource management settings and in agent-based distributed scheduling systems. The paper describes the proposed DLD model in details, presents a dynamic programming based optimization algorithm for the proposed model, and then discusses its application to agent-based distributed scheduling.     

A New Task Scheduling Scheme Based on Genetic Algorithm for Edge Computing

With the continuous evolution of smart grid and global energy interconnection technology, amount of intelligent terminals have been connected to power grid, which can be used for providing resource services as edge nodes. Traditional cloud computing can be used to provide storage services and task computing services in the power grid, but it faces challenges such as resource bottlenecks, time delays, and limited network bandwidth resources. Edge computing is an effective supplement for cloud computing, because it can provide users with local computing services with lower latency. However, because the resources in a single edge node are limited, resource-intensive tasks need to be divided into many subtasks and then assigned to different edge nodes by resource cooperation. Making task scheduling more efficient is an important issue. In this paper, a two-layer resource management scheme is proposed based on the concept of edge computing. In addition, a new task scheduling algorithm named GA-EC(Genetic Algorithm for Edge Computing) is put forth, based on a genetic algorithm, that can dynamically schedule tasks according to different scheduling goals. The simulation shows that the proposed algorithm has a beneficial effect on energy consumption and load balancing, and reduces time delay.     

A Joint Algorithm for Resource Allocation in D2D 5G Wireless Networks

With the rapid development of Internet technology, users have an increasing demand for data. The continuous popularization of traffic-intensive applications such as high-definition video, 3D visualization, and cloud computing has promoted the rapid evolution of the communications industry. In order to cope with the huge traffic demand of today’s users, 5G networks must be fast, flexible, reliable and sustainable. Based on these research backgrounds, the academic community has proposed D2D communication. The main feature of D2D communication is that it enables direct communication between devices, thereby effectively improve resource utilization and reduce the dependence on base stations, so it can effectively improve the throughput of multimedia data. One of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular user. To solve this problem, this paper proposes a joint algorithm time scheduling and power control. The main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service requirements. The constraint problem is decomposed into time scheduling and power control subproblems. The power control subproblem has the characteristics of mixed-integer linear programming of NP-hard. Therefore, we proposed a gradual power control method. The time scheduling subproblem belongs to the NP-hard problem having convex-cordinality, therefore, we proposed a heuristic scheme to optimize resource allocation. Simulation results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms.     

和声搜索算法优化快速消费品生产配送协调调度

研究具有生产准备环节的快速消费品生产配送问题,考虑工厂和配送中心的库存限制,工厂产能限制和劳动力限制,建立一个多周期、多工厂、多产品、多配送中心、多客户的混合整数线性规划模型,旨在最小化准备成本、生产成本、库存成本和配送成本。通过设计一种遗传和声搜索算法对模型进行求解。最后给出一个算例说明所提模型和算法的可行性和有效性。     

基于Project 2003的关键链启发式算法

介绍了关键链项目管理的基本方法;针对资源受限项目调度问题,提出了资源冲突时的资源分配原则;以项目工期最小化为目标,以Proiect 2003为辅助工具,提出了一个确定关键链的启发式算法,即在找出关键路径的基础上,从后往前遍历,对发生资源冲突的活动,依资源分配原则修改紧前关系,然后再确定关键链,进而得到了基于关键链项目管理方法的项目调度算法.对算法进行了验证,结果表明算法是有效的.     

Power efficient uplink scheduling in SC-FDMA: benchmarking by column generation

We study resource allocation in cellular systems and consider the problem of finding a power efficient scheduling in an uplink single carrier frequency division multiple access system. Due to the discrete nature of this problem and its computational difficulty, particularly in a real-time setting, the use of suboptimal algorithms is common practice. We aim at an effective way of gauging the performance of suboptimal algorithms by finding tight bounds on the global optimum. Toward this end, we first provide a basic integer linear programming formulation. Then we propose a significantly stronger column-oriented formulation and a corresponding column generation method, as well as an enhanced column generation scheme. The latter extends the first scheme through the inclusion of a stabilization technique, an approximate column generation principle, and a tailored heuristic that is embedded in the column generation scheme to find high-quality though not necessarily global optimal solutions. The computational evaluation demonstrates that compared with a poor performance by the integer linear programming formulation, the column generation method can produce near-optimal schedules that enable a sharp bounding interval. The enhanced column generation method significantly sharpens the bounding interval. Hence the column generation approach serves well for the purpose of benchmarking results for large-scale instances.     

Resource-constrained scheduling with hard due windows and rejection penalties

This work studies a scheduling problem where each job must be either accepted and scheduled to complete within its specified due window, or rejected altogether. Each job has a certain processing time and contributes a certain profit if accepted or penalty cost if rejected. There is a set of renewable resources, and no resource limit can be exceeded at any time. Each job requires a certain amount of each resource when processed, and the objective is to maximize total profit. A mixed-integer programming formulation and three approximation algorithms are presented: a priority rule heuristic, an algorithm based on the metaheuristic for randomized priority search and an evolutionary algorithm. Computational experiments comparing these four solution methods were performed on a set of generated benchmark problems covering a wide range of problem characteristics. The evolutionary algorithm outperformed the other methods in most cases, often significantly, and never significantly underperformed any method.