Integrated Framework for Quantifying and Predicting Weather-Related Highway Construction Delays

Constant exposure to the environment makes highway construction highly dependent on weather. However, highway construction contracts are often unclear about the potential influence of weather-related delays on highway construction project schedules. There is a need to discourage litigation arising from weather-related delays by including in contracts a reasonable number of nonwork days as a consequence of adverse weather and providing an equitable criteria for the course of action when the predictions in the contracts turn out to be inaccurate. To address this need, an integrated framework consisting of the following two key components is proposed: (1) identification of attributes of weather that cause construction delays and (2) generation of synthetic weather sequences using a stochastic weather generator to quantify and provide probabilistic forecasts of weather threshold values. The utility of this framework is demonstrated through its application to construction work on a project in Texas. The use of probabilistic forecast of construction delay attributes provided by a semiparametric weather generator in this research is an example of interdisciplinary study to help address this problem. The result of the research is better decision support for agencies who wish to author contracts that more equitably allow for the influence of weather during construction.     

Probabilistic Forecasting of Project Duration Using Kalman Filter and the Earned Value Method

The earned value method (EVM) is recognized as a viable method for evaluating and forecasting project cost performance. However, its application to schedule performance forecasting has been limited due to poor accuracy in predicting project durations. Recently, several EVM-based schedule forecasting methods were introduced. However, these are still deterministic and have large prediction errors early in the project due to small sample size. In this paper, a new forecasting method is developed based on Kalman filter and the earned schedule method. The Kalman filter forecasting method (KFFM) provides probabilistic predictions of project duration at completion and can be used from the beginning of a project without significant loss of accuracy. KFFM has been programmed in an add-in for Microsoft Excel and it can be implemented on all kinds of projects monitored by EVM or any other S-curve approach. Applications on two real projects are presented here to demonstrate the advantages of KFFM in extracting additional information from data about the status, trend, and future project schedule performance and associated risks.     

OCCASION: New Planning Tool for Optimal Climate Change Adaption Strategies in Irrigation

To sustain productive irrigated agriculture with limited water resources requires a high water use efficiency. This can be achieved by the precise scheduling of deficit irrigation systems taking into account the crops’ response to water stress at different stages of plant growth. Particularly in the light of climate change with rising population numbers and increasing water scarcity, an optimal solution for this task is of paramount importance. We solve the corresponding complex multidimensional and nonlinear optimization problem, i.e., finding the ideal schedule for maximum crop yield with a given water volume by a well tailored approach which offers straightforward application facilities. A global optimization technique allows, together with physically based modeling, for the risk assessment in yield reduction considering different sources of uncertainty (e.g., climate, soil conditions, and management). A new stochastic framework for decision support is developed which aims at optimal climate change adaption strategies in irrigation. It consists of: (1) a weather generator for simulating regional impacts of climate change; (2) a tailor-made evolutionary optimization algorithm for optimal irrigation scheduling with limited water supply; and (3) mechanistic models for rigorously simulating water transport and crop growth. The result, namely, stochastic crop-water production functions, allows to assess the impact of climate variability on potential yield and thus provides a valuable tool for estimating minimum water demands for irrigation in water resources planning and management, assisting furthermore in generating maps of yield uncertainty for specific crops and specific agricultural areas. The tool is successfully applied at an experimental site in southern France. The impacts of predicted climate variability on maize are discussed.     

Application of Innovative Critical Chain Method for Project Planning and Control under Resource Constraints and Uncertainty

This research proposes an innovative critical chain method (ICCM) for project planning and control under resource constraints and uncertainty. An improved genetic algorithm is developed to identify the critical chain and to obtain the optimal start time of each activity under the most optimistic duration of each activity and resource constraints. Furthermore, a feeding buffer is added in an insert point in order to deal with uncertainties. The benefits of applying this ICCM are demonstrated in an example project.     

基于Q学习和规划的传感器节点任务调度算法*

为了改善节点的学习策略,提高节点的应用性能,以数据收集为应用建立任务模型,提出基于Q学习和规划的传感器节点任务调度算法,包括定义状态空间、延迟回报、探索和利用策略等基本元素.根据无线传感器网络(WSN)特性,建立基于优先级机制和过期机制的规划过程,使节点可以有效利用经验知识,改善学习策略.实验表明,文中算法具备根据当前WSN环境进行动态任务调度的能力.相比其它任务调度算法,文中算法能量消耗合理且获得较好的应用性能.     

数字微流控生物芯片微液滴优化调度

数字微流控生物芯片出现,使得单片并行多样品、多试剂、多生物检测量的大规模生物检测系统成为现实,需要在有限的芯片资源中优化调度各样品和试剂以减少检测时间.由于优化调度是一个NP完全问题,本文提出了以多样品检测完成时间为适应度函数,以样品和试剂混合操作类型集合为染色体,并将该染色体分别赋以一整数值代表混合操作优先级高低,同时,将染色体基因分为可同时进行混合操作而资源不冲突基因和有限任意项基因两部份,并对有限任意项基因进行移位、交叉等遗传操作,达到优化调度接近最优解.所提出算法编码基因数从(4Sm*Rn)!降低到Sm*Rn,极大改善了算法效率和并行检测所需时间.     

多用户MIMO系统中一种基于一位反馈信息的调度算法

针对多用户MIMO(Multiple-Input Multiple-Output)无线通信系统潜在的多用户分集增益,该文提出了一种基于一位反馈信息的多用户调度算法.理论分析表明,设置合理的门限可以从统计意义上最大化系统平均频谱效率.同时,该文从理论上对最优门限的选择进行了分析.仿真结果表明所提算法可有效利用多用户分集来提高系统的频谱性能.     

Algorithmic aspects for functional partitioning and scheduling in hardware/software co-design

Hardware/software (HW/SW) partitioning and scheduling are the crucial steps during HW/SW co-design. It has been shown that they are classical combinatorial optimization problems. Due to the possible sequential or concurrent execution of the tasks, HW/SW partitioning and scheduling has become more difficult to solve optimally. In this paper more efficient heuristic algorithms are proposed for the HW/SW partitioning and scheduling. The proposed algorithm partitions a task graph by iteratively moving the task with highest benefit-to-area ratio in higher priority. The benefit-to-area ratio is updated in each iteration step to cater for the task concurrence. The proposed algorithm for task scheduling executes the task lying in hardware-only critical path in higher priority to enhance the task forecast. A large body of experimental results conclusively shows that the proposed heuristic algorithm for partitioning is superior to the latest efficient combinatorial algorithm (Tabu search) cited in this paper. Moreover, the Tabu search for partitioning has been further improved by utilizing the proposed heuristic solution as its initial solution. In addition, the proposed scheduling algorithm obtains the improvements over the most widely used approaches by up to 10% without large increase in running time. This work was presented in part at 2006 IEEE International Conference on Field Programmable Technology (ICFPT).     

A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling

This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling (IPPS). Generally, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph fromthe starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and lowconvergence, some improved strategy is incorporated in the standard ant colony optimization algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance.     

蜜蜂交配优化算法在车间作业调度中的应用

为了解决车间作业调度问题,在对其进行分析描述的基础上,提出了采用蜜蜂交配优化算法的求解方法。该方法把由多个作业调度方案组成的集合作为蜂群,以最小化加工时间作为算法的优化目标,通过模拟蜂群交配繁衍培养蜂王的优化过程来获得最优作业调度方案。采用车间作业调度测试案例在Matlab平台上进行实验,实验结果表明,该方法不仅能够有效地求解车间作业调度问题,而且能够取得了比传统优化方法更好的优化结果。