基于约束满足问题的中继卫星调度问题研究

中继卫星任务规划与调度是中继卫星系统应用中的重要问题。根据航天器的空间轨道参数，得到中继卫星与用户航天器之间的可见时间窗口。在此基础上，通过分析中继卫星系统中各种资源之间的约束关系、任务优先级与调度准则，建立中继卫星系统的任务调度模型。仿真结果表明，基于约束规划理论建立中继卫星调度模型是解决中继卫星调度问题的有效方法。     

“货到人”拣选系统机器人任务分配的鲁棒双层规划模型

在电商“货到人”拣选系统中,如何调度系统中的机器人并对任务进行合理地分配决定着整个系统的运行效率与成本。分析“货到人”拣选系统作业流程,建立机器人数量配置、机器人调度与机器人任务分配的双层规划模型。上层模型以批量订单完成总成本最小为目标函数,以机器人调度为决策变量,构建整数规划模型;下层模型以机器人完成所有任务的平均空闲率最小为目标函数,以任务分配为决策变量,考虑机器人在完成任务过程中由于调度、避障、路径规划等导致的行走距离不确定因素,构建鲁棒优化模型。上层的调度结果制约了下层的最小平均空闲率,下层的任务分配结果影响上层的最小成本,上下层结果共同决定机器人配置决策。利用遗传算法求解模型,通过实例仿真验证了模型的有效性。     

基于博弈论的铁路集装箱中心站装卸作业研究

考虑铁路集装箱中心站不同装卸任务之间的竞争性因素,采用博弈论的方法,研究中心站在客户竞争驱动下的装卸资源调度及作业顺序问题。建立博弈模型并设计遗传算法,通过求解该问题的典型算例,将得到的均衡解与静态最优解进行对比,分析考虑竞争因素的作业方案与最优作业方案的差异。结果表明该方法在对最优方案影响较小的情况下,充分考虑了各装卸任务间的竞争公平性,为中心站确定更加合理的装卸作业顺序和资源调度提供科学的依据。     

网格环境下制造资源在线优化调度的区间规划模型

针对网格环境下影响制造资源优化调度的关键参数具有区间性的特点,本文基于区间数建立了在线处理模式下的资源优化调度模型,分别以任务完工的总成本最低与资源的利益最大为目标,将任务的成本限制及资源的价格转换为区间数,充分考虑了任务与资源工作时间限制,给出区间规划模型及其解法。最后通过算例分析得出该模型在反映市场需求以及快速应对市场变化基础上,可得出合理的优化调度方案,验证该方法的可行性与有效性。     

兼顾公平与效率的云计算并行机资源调度研究

研究在云计算中服务资源优化管理背景下,基于时间窗口的非等同并行机服务资源调度问题.为达到最大任务处理数,选取任务延误时间作为目标函数建立数学模型,并利用蚁群算法为模型求解.设计了算法的各项参数,而且进一步探讨了如何将资源分配的公平性引入到算法中来.还通过仿真算例对比了考虑公平性要素前后的调度结果.从结果来看,提出的模型和算法能够较好的用于解决云计算中的并行机资源调度问题,并以较快的收敛速度找到满足约束条件的较优解.     

基于多Agent的车间任务招投标粗模糊集建模研究

首先通过相识集、招标集、投标集和任务集的概念 ,描述制造执行系统中的调度 Agent与资源 A-gent间任务招投标过程模型 ;基于任务的属性和资源 Agent完成任务的成本、质量、负荷和时间等属性 ,定义论域上的模糊集 ,将模糊集中的隶属度函数作为粗集的属性 ,在模糊集上作截集 ,从而获得系统的分类知识 ;收集样本数据 ,构造并分析决策表 ,进而获得调度 Agent调度决策知识 ;应用调度知识进行推理 ,从争取获得招标任务的若干个资源 Agent中 ,选出最适合招标任务的中标者 .     

带有资源柔性约束的max-NPV分布式多项目调度问题

现有的分布式资源约束多项目调度问题研究中,假定全局资源限量在多项目工期内不可突破且多以工期为优化目标。针对此问题,考虑全局资源可从外部获取,以净现值为目标,构建带有全局资源柔性约束的分布式多项目调度问题的整数规划模型并设计有效的求解算法。首先,界定问题并确定项目现金流的计算方法;然后,针对求解问题的NP-hard属性,设计了遗传-模拟退火混合算法(GA_SA)求解此模型。最后,通过多组数值实验,设计不同算法与GA_SA算法进行比较,并分析了关键参数对多项目净现值的影响。结果表明,GA_SA算法具有较好的求解效果;与传统的全局资源刚性约束条件相比,全局资源柔性使用状态可以显著改善分布式多项目的收益绩效。     

基于活动延期风险加权时差的资源受限项目调度鲁棒性度量

在项目调度鲁棒性研究中,当活动出现延期风险时,由于各活动性质不同,其延期风险权重也不同,权重越大的活动越有可能影响项目的完工时间。针对资源受限项目调度问题,提出一个基于活动延期风险加权时差的鲁棒性度量新指标。在出现不确定因素干扰时,该指标不仅考虑了活动延期风险权重的影响,同时为实现时差在多个任务之间的共享,还考虑了紧前任务数量的影响。建立一个以加权时差最大化为目标的资源受限项目调度鲁棒优化模型,并针对模型特点,设计了基于禁忌搜索的模拟退火算法。最后,通过算例验证了该度量方式和算法的合理性和有效性,对比分析结果表明所提出的指标优于现有的度量指标,较好地满足了项目调度质量鲁棒性的要求。     

工期-费用权衡问题的两个不确定机会约束规划模型

项目调度问题是指管理者通过资源分配,延长或缩短某些工序的持续时间,达到项目费用和工期之间的平衡。在实际工程项目中,环境的不确定性往往是项目管理者需要考虑的因素之一。本文应用不确定理论描述不确定环境中的项目调度问题,将每道工序的持续时间分为正常持续时间和可变动持续时间。其中,假设工序的正常持续时间为不确定变量,可变动持续时间为决策变量,分别构建在项目费用和项目工期约束下的两个不确定机会约束规划模型。依据不确定变量运算法则,将所建立的不确定规划模型转化为等价的确定模型。最后,通过一个工程实例验证模型的合理性。     

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。     

Uncertainty Modelling in Software Development Projects (With Case Study)

A project scheduling model tailored specifically for software development projects is proposed in this study. The model incorporates uncertainties related to activity durations and network topology. The first type of uncertainty exists due to error-prone coding which might result in elongated task durations caused by validation and debugging sessions. Furthermore, in practice, macro-activities represent groups of sub-tasks in order to simplify the planning and monitoring of the project. Due to the aggregation, it is more difficult to be precise on the duration of a macro-activity.The uncertainty related to the network topology is due to common database design issues or program modules shared among parallel tasks in the project network. These tasks become associated with each other through uncertain Start-to-Start (SS) precedence relationships. On the other hand, SS lags may also be the outcome of technological precedence relationships among pairs of activities. However, the imprecision underlying the work content of a predecessor activity leads to uncertain SS lags.Software development projects are human-intensive projects and hence, the duration of a task depends on the skill of the person assigned to the job as well as his/her learning rate. Thus, a task may be realized by alternative staff members which results in different expected task durations. Hence, a realistic model proposed for software development projects should incorporate staff assignment features under the uncertainties discussed above. In this study, we develop a mathematical model for software development projects and propose heuristic solution methods to be used by the project co-ordinator in preparing the project plan. The heuristic algorithms developed here are tested on real data provided by a consulting firm undertaking software development projects from manufacturing companies in Turkey.     

Dynamic Capacity Acquisition and Assignment under Uncertainty

Given a set of m resources and n tasks, the dynamic capacity acquisition and assignment problem seeks a minimum cost schedule of capacity acquisitions for the resources and the assignment of resources to tasks, over a given planning horizon of T periods. This problem arises, for example, in the integrated planning of locations and capacities of distribution centers (DCs), and the assignment of customers to the DCs, in supply chain applications. We consider the dynamic capacity acquisition and assignment problem in an environment where the assignment costs and the processing requirements for the tasks are uncertain. Using a scenario based approach, we develop a stochastic integer programming model for this problem. The highly non-convex nature of this model prevents the application of standard stochastic programming decomposition algorithms. We use a recently developed decomposition based branch-and-bound strategy for the problem. Encouraging preliminary computational results are provided.     

A heuristic methodology for sizing a large-scale system of constrained, reusable resources

This paper proposes a methodology for sizing certain large-scale systems of reusable, capacity-constrained resources engaged in tasks of varying duration. A heuristic program schedules resources throughout a finite planning horizon using two decision variables: varying resource capacity for meeting demand and varying task duration. A model of the problem and heuristic scheduling program are presented. A sequential, iterative sizing procedure determines the number of system resources to meet demand at each stage of the problem. Results compare the methodology with heuristics used in practice to schedule resources and size a real-world, large-scale training system.     

Integrating restoration and scheduling decisions for disrupted interdependent infrastructure systems

We consider the problem faced by managers of critical civil interdependent infrastructure systems of restoring essential public services after a non-routine event causes disruptions to these services. In order to restore the services, we must determine the set of components (or tasks) that will be temporarily installed or repaired, assign these tasks to work groups, and then determine the schedule of each work group to complete the tasks assigned to it. These restoration planning and scheduling decisions are often undertaken in an independent, sequential manner. We provide mathematical models and optimization algorithms that integrate the restoration and planning decisions and specifically account for the interdependencies between the infrastructure systems. The objective function of this problem provides a measure of how well the services are being restored over the horizon of the restoration plan, rather than just focusing on the performance of the systems after all restoration efforts are complete. We test our methods on realistic data representing infrastructure systems in New York City. Our computational results demonstrate that we can provide integrated restoration and scheduling plans of high quality with limited computational resources. We also discuss the benefits of integrating the restoration and scheduling decisions.     

不确定箱量下内河集装箱班轮航线动态配载决策

内河集装箱班轮运输中海关抽检可导致外贸箱箱量不断发生变化,班轮航线配载需要动态决策。基于滚动调度策略,将当前港口的配载决策按随机事件划分为多个阶段,以最小化班轮堆栈占用数量和相邻阶段间配载计划偏差为目标,构建单港口单阶段的配载决策模型,进而滚动实现班轮航线动态配载决策。基于大邻域搜索思想设计一种包含整数规划、破坏器与修复器的精确启发式算法,实现港口多阶段滚动配载。基于真实场景的算例研究表明,在优化堆栈占用数量方面,模型与算法之间差异不大,但在考虑相邻阶段间配载计划偏差时,算法的求解结果要优于模型。因此,模型与算法可用来辅助实现不确定箱量下内河集装箱班轮航线动态配载决策,且算法表现更优,可实现配载计划对不确定箱量的鲁棒吸收。     

A mixed-integer programming approach to a class timetabling problem: A case study with gender policies and traffic considerations

This paper considers the class scheduling and timetabling problem faced at Kuwait University (KU). The principal focus is to design efficient class offering patterns while taking into consideration newly imposed gender policies. We formulate a mathematical programming model that assigns offered classes to time-slots and addresses gender issues by defining appropriate surrogate constraints along with objective penalty terms. The model aims to enhance existing manual scheduling and timetabling approaches that are often accompanied with arduous combinatorial tasks such as resolving class conflicts, dealing with parking and traffic congestion, and ensuring an efficient utilization of facility and human resources. This modeling approach emphasizes the generation of flexible class timetables for students, and the efficient utilization of available facility resources. Computational results based on a number of case studies related to Kuwait University reveal that this approach yields improved schedules in terms of offering patterns and class conflicts.     

A robust optimization model for multi-product two-stage capacitated production planning under uncertainty

Production planning (PP) is one of the most important issues carried out in manufacturing environments which seeks efficient planning, scheduling and coordination of all production activities that optimizes the company’s objectives. In this paper, we studied a two-stage real world capacitated production system with lead time and setup decisions in which some parameters such as production costs and customer demand are uncertain. A robust optimization model is developed to formulate the problem in which minimization of the total costs including the setup costs, production costs, labor costs, inventory costs, and workforce changing costs is considered as performance measure. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all the possible future scenarios. A mixed-integer programming (MIP) model is developed to formulate the related robust production planning problem. In fact the robust proposed model is presented to generate an initial robust schedule. The performance of this schedule could be improved against of any possible occurrences of uncertain parameters. A case from an Iran refrigerator factory is studied and the characteristics of factory and its products are discussed. The computational results display the robustness and effectiveness of the model and highlight the importance of using robust optimization approach in generating more robust production plans in the uncertain environments. The tradeoff between solution robustness and model robustness is also analyzed.     

数学规划与约束规划整合下的多目标分组排序问题研究

分组排序问题属于NP-难题, 单纯的数学规划模型或约束规划模型都无法在有效时间内解决相当规模的此类问题. 控制成本、缩短工期和减少任务延迟是排序问题的三个基本目标, 在实际工作中决策者通常需要兼顾三者, 并在 三者之间进行权衡. 多目标分组排序问题 的研究增强了排序问题的实际应用价值, 有利于帮助决策者处理复杂的多目标环境. 然而, 多目标的引入也增加了问题求解难度, 针对数学规划擅长寻找最优, 约束规划擅长排序的特点, 将两类方法整合起来, 提出一个基于Benders分解算法, 极大提高了此类问题的求解 效率.     

Tabu search for a class of scheduling problems

Scheduling problems are often modeled as resourceconstrained problems in which critical resource assignments to tasks are known and the best assignment of resource time must be made subject to these constraints. Generalization toresource scheduling, where resource assignments are chosen concurrently with times results is a problem which is much more difficult. A simplified model of the general resource scheduling model is possible, however, in which tasks must be assigned a singleprimary resource, subject to constraints resulting from preassignment ofsecondary, or auxiliary, resources. This paper describes extensions and enhancements of tabu search for the special case of the resource scheduling problem described above. The class of problems is further restricted to those where it is reasonable to enumerate both feasible time and primary resource assignments. Potential applications include shift oriented production and manpower scheduling problems as well as course scheduling where classrooms (instructors) are primary and instructors (rooms) and students are secondary resources. The underlying model is a type of quadratic multiple choice problem which we call multiple choice quadratic vertex packing (MCQVP). Results for strategic oscillation and biased candidate sampling strategies are shown for reasonably sized real and randomly generated, synthetic, problem instances. The strategies are compared with other variations using consistent measures of solution time and quality developed for this study.