分布式资源受限多项目调度问题研究综述

传统的资源受限多项目调度集中式决策方法已无法满足信息不对称环境下的多项目管理需求，分布式资源受限多项目调度问题研究的是多个自主决策者分别进行全局资源的协调分配决策与局部项目的调度优化决策。鉴于较强的实际应用背景，这类问题逐渐成为项目调度领域新的研究热点。首先，在系统介绍分布式资源受限多项目调度问题特点与模型构建的基础上，分类梳理并评述了问题求解的代表性方法；其次，总结了分布式多项目调度的相关扩展问题以及在不同领域的典型应用；最后，讨论了目前研究中存在的主要问题与未来可能的拓展研究方向。     

基于规则和仿真的多机并行作业车间生产调度研究

资源分派和能力分派是作业车间生产调度中的重要问题,路径选择规则和分派规则是解决上述问题的有效途径。采用基于规则的仿真研究多机并行作业车间资源分派和能力分派问题,分析工件加工时间、到达率以及机器加工速率对调度结果的影响,以平均完工时间、平均延迟交货率以及平均资源利用率为评价指标,通过对4种路径选择规则和6种分派规则的仿真试验,确定不同性能指标下最佳的调度规则。仿真研究表明:调度规则的选用取决于车间资源配置和调度目标,应避免仅凭借经验或偏好选择规则的调度方法。     

关键链上可被部分替代的受限资源动态调度

关键链上受限资源的优化调度,能有效提高系统运作效率,是关键链项目管理的重要研究方向之一.但现有研究主要针对资源能否更新,较少考虑资源的可替代性.针对此,研究了关键链上可被部分替代的受限资源调度问题,以达到提高资源利用率、降低成本等目的.采用α/β/γ三元组方法,将研究的问题描述为以最小化滞后时间和为目标,具有机器适用限制的并行异速机调度问题,并建立对应的数学模型;运用混合重调度策略和改进粒子群算法进行了算法设计;设计了仿真算例,并通过算法比较表明该方法在求解结果和问题求解规模上显著优于传统PSO、GA算法.     

合理利用测控资源的动态调度模型

通过研究小卫星系统测控需求，地面测控资源以及两者之间的关系，建立了一套测控资源动态调度数学模型，以反映小卫星测控要求和测控资源的函数关系，旨在寻找一种资源分配随时间动态改变的最优调度方法，本文提出的测控资源动态调度模型，可用于小卫星系统概念设计阶段运行费用的估算以及提高测控中心的自动化程度，从而保证小卫星大效益。     

基于蚁群算法的试验流程优化研究

水中兵器的海上试验涉及许多人员、兵力、被试产品、测量设备等，试验周期长、消耗大，因此如何缩短试验周期是亟待研究解决的问题．文中首先将试验流程优化问题转化为车间调度问题，建立了相应的数学模型，再应用蚁群算法转移规则得到中间结果并进行排队以对各种资源约束进行处理．最后将结果利用局部搜索算法优化后作为蚁群算法信息素更新的基础．实例计算结果表明，该方法优化效果良好．     

考虑资源成本的Petri网在FMS调度中的应用

本文针对目前基于petri网的调度方法中调度目标单一的缺点，通过在petri网的结构中引入资源成本元素的方法，使得在调度计划产生的过程中可以同时考虑时间和资源成本优化，为企业作出更为科学合理的生产决策提供依据．最后通过一个实例来验证了提出的算法。     

具有任务资源需求分布与资源时变约束的RCPSP分枝定界算法

研究资源受限项目调度问题，考虑了项目中每个任务对可更新（再生）资源需求的任意分布、可更新（再生）资源的最大供给量随时间而变化的情形。作为对前人研究结果的进一步推广，利用了分枝定界技术，以及事件驱动的时间增量方式，成功地解决了这种一般项目的调度问题     

一类多资源调度问题的优化算法

若调度系统中的作业或任务需要一个或多个资源共同完成， 则这一类调度问题被称为多资源（或多处理机）调度问题。本文针对Jobshop中的这一调度问题，提出了基于遗传的优化调度算法，并用实例证实了该算法的有效性。     

板式家具企业的订单组批问题研究

为了减少板式家具企业封边工序过长的准备时间以及提高原材料的开料利用率，满足企业快速组批的需求，本文研究板式家具的订单组批问题，并提出一个组批优化的方法。在对组批问题进行定义和数学描述的基础上，建立了一个双目标问题的数学模型，通过序贯优化分析如何求解双目标问题，设计出一种组合调度规则的启发式算法。为了验证算法和评估调度规则的性能，设计了该类问题的算例生成方法，并将本文的方法与另外构造的10种规则算法应用到生成的算例上，进行仿真调度，最后与精确解法作比较。仿真调度的结果显示基于板块颜色与板块厚度的调度规则可以有效地降低封边工序的准备时间，提高原料利用率。     

飞机移动生产线作业调度问题的启发式算法

飞机移动生产线作业调度问题是一种具有特殊约束的项目调度问题。综合分析了飞机移动生产线的作业顺序关序、资源约束、空间限制的多重特点,建立了以最小装配完工时间为目标的模型。将问题分为确定作业的优先顺序和选择作业人数的两个阶段进行决策,设计了由不同规则组合成27种启发式方法。通过比较启发式方法在250个测试问题集的表现,确定最有效的解决此问题的方法。     

Scheduling research in multiple resource constrained job shops: a review and critique

Over the past several years, a number of survey, classification, and review articles have focused on scheduling research in machine [only] constrained job shops. Barring the work of Treleven (1989), there is no reported research that presents a detailed review of the issues related to scheduling and sequencing in job shops with multiple resource constraints. In his article, Treleven reviewed the research in job shops constrained by machines and labour. Job shops are not only constrained by machines and labour, but by auxiliary resources (in the form of tooling, etc.) as well. This paper extends the work of Treleven by reviewing the literature on scheduling in job shops constrained by more than one resource and comparing the scheduling research in auxiliary resource-constrained job shops with that of labour-constrained job shops. In addition, this article raises some issues for future scheduling research in multiple resource-constrained job shops.     

Optimal power system generation scheduling by multi-objective genetic algorithms with preferences

Power system generation scheduling is an important issue both from the economical and environmental safety viewpoints. The scheduling involves decisions with regards to the units start-up and shut-down times and to the assignment of the load demands to the committed generating units for minimizing the system operation costs and the emission of atmospheric pollutants.As many other real-world engineering problems, power system generation scheduling involves multiple, conflicting optimization criteria for which there exists no single best solution with respect to all criteria considered. Multi-objective optimization algorithms, based on the principle of Pareto optimality, can then be designed to search for the set of nondominated scheduling solutions from which the decision-maker (DM) must a posteriori choose the preferred alternative. On the other hand, often, information is available a priori regarding the preference values of the DM with respect to the objectives. When possible, it is important to exploit this information during the search so as to focus it on the region of preference of the Pareto-optimal set.In this paper, ways are explored to use this preference information for driving a multi-objective genetic algorithm towards the preferential region of the Pareto-optimal front. Two methods are considered: the first one extends the concept of Pareto dominance by biasing the chromosome replacement step of the algorithm by means of numerical weights that express the DM’ s preferences; the second one drives the search algorithm by changing the shape of the dominance region according to linear trade-off functions specified by the DM.The effectiveness of the proposed approaches is first compared on a case study of literature. Then, a nonlinear, constrained, two-objective power generation scheduling problem is effectively tackled.     

An Algorithm for a Single Machine Scheduling Problem with Sequence Dependent Setup Times and Scheduling Windows

An enumeration algorithm is presented for solving a scheduling problem similar to the single machine job shop problem with sequence dependent setup times. The scheduling problem differs from the job shop problem in two ways. First, its objective is to select an optimum subset of the available tasks to be performed during a fixed period of time. Secondly, each task scheduled is constrained to occur within its particular scheduling window. The algorithm is currently being used to develop typical observational timelines for a telescope that will be operated in earth orbit. Computational times associated with timeline development are presented.     

An Optimization Based Heuristic for Scheduling Parallel Project Networks with Constrained Renewable Resources

In this paper we address the scheduling problem for the simultaneous management of multiple resource constrained project networks in parallel. This situation is common in the construction of a housing development where the building contractor is concerned with the simultaneous completion of a number of individual, identical construction projects. If only one project were managed, a resource constrained critical path method could be utilized. In this paper we present a model for the simultaneous planning of many individual project networks. We formulate the problem as an integer program that is similar to models found in the inventory/production scheduling literature. A Dantzig-Wolfe decomposition is used to obtain solutions to the linear programming relaxation of the problem. The algorithm selects a subset of these solutions to form a final schedule. Computational results are presented. Extensions of the algorithm, including non-identical projects, alternative objective functions and implementation on a rolling horizon are also discussed.     

Simulated annealing-based optimal wind-thermal coordination scheduling

The rise of environmental protection and the progressive exhaustion of traditional fossil energy sources have increased the interests in integrating wind energy sources into existing power systems. Development of better wind-thermal coordination algorithms is necessary to determine the optimal proportion of wind generator capacity that can be integrated into the system for operating an isolated hybrid power system reliably and efficiently. A stochastic searching technique, which utilises a simulated annealing (SA) approach combined with an efficient constrained dynamic economic dispatch (CDED) method, is developed to coordinate the wind and thermal generation scheduling in isolated power systems with large integration of wind capacity. The SA algorithm is used for the scheduling of the generating units, whereas a direct search method routine carries out the ramp rate CDED when considering wind power generation. Several technique constraints are applied to determine the maximum proportion of wind generator capacity that can be integrated into the system. A constraint satisfaction technique for generating feasible neighbouring solution is also developed to improve the SA solution process. Numerical experiments are included to understand the wind generator capacity in the operating cost analysis and to provide valuable information for both the operational and planning problems     

智能排班问题的概率约束规划模型与有效求解

由于其广泛的应用领域与难解性,对不同形式智能排班问题的研究现已成为运筹学界的一个研究热点。为克服现有模型无法恰当反映人力需求约束实际特征与求解算法效率低等不足,本文对综合排班问题构建了两种不同形式的概率约束随机规划模型。利用样本平均近似方法和"大M方法",我们将概率约束问题转化为大规模整数线性规划问题,并综合运用了Lagrange松弛法、分解算法与信赖域算法等方法的思想,设计出了一有效的嵌套分解算法。最后,通过数值算例说明了本文模型的合理性与所设计算法的有效性。     

基于优先规则的模糊资源受限多项目调度

在分析国内外相关研究现状的基础上,结合资源受限多项目调度问题的特点,针对多项目中各个工序资源参数的不确定性,通过对工程项目工序资源需求量的模糊表示,建立了模糊资源受限多项目调度模型,并运用基于优先规则的启发式算法对所建立的模型进行调度计算,最后通过算例验证模型的有效性和可行性,以实现多项目资源的优化配置。     

Managing and modelling general resource transfers in (multi-)project scheduling

Most approaches to multi-project scheduling are based on the assumption that resources can be transferred between projects without any expense in time and cost. As this assumption often is not realistic, we generalise the multi-project scheduling problem (RCMPSP) by additionally including transfer times and cost. To integrate this aspect, in a first step, we develop a framework for considering resource transfers in single- and multi-project environments. It includes managerial approaches to handle resource transfers, a classification of resource transfer types and new roles that resources can take in these transfers. Afterwards, we define the multi-project scheduling problem with transfer times (RCMPPTT) and formulate it in a basic and an extended version as integer linear programmes. Eventually, it is supplemented for the first time by cost considerations and introduced as resource constrained multi-project scheduling problem with transfer times and cost (RCMPSPTTC). Computational experiments compare the presented managerial approaches and prove the necessity of explicitly considering transfer times in project scheduling already during the planning phase. Moreover, the experiments evaluate the presented MIP models and show that specialised solution procedures are vital.     

资源约束项目排序问题的一种修正蚁群算法

根据蚁群算法的性质与资源约束项目排序问题(CPSP:Resource-Constrained Project Schedul- ing Problem)的特征,本文给出了蚁群算法中信息素的表示及更新方案、启发信息的计算方法等,由此提出了一种求解RCPSP的修正蚁群算法。最后,通过对项目排序问题库中的标准问题集进行计算,结果表明本文提出的修正蚁群算法是可行优良的。     

An integrated model for multi-resource constrained scheduling problem considering multi-product and resource-sharing

This paper studies a multi-resource constrained scheduling problem considering multi-product and resource-sharing in the manufacturing supply chain, in which many independent production units coordinate with a truck resource manager. A mixed integer programming model is formulated to minimise the total system cost and some analytical properties are proposed to tighten the model. A Lagrangian relaxation-based heuristic with several enhancements, e.g. warm startup, approximating solve and parallel computation of subproblems, is proposed to solve the model. Finally, computational experiments are conducted to verify that (i) the proposed method has a better performance in both objective and CPU time than CPLEX, (ii) all three enhancements can help reduce the total computation time and (iii) a certain degree of resource-sharing can help reduce the total cost of the system.