Permutation-Based Elitist Genetic Algorithm for Optimization of Large-Sized Resource-Constrained Project Scheduling

The resource-constrained project scheduling problem (RCPSP) has received the attention of many researchers because its general model can be used in a wide variety of construction planning and scheduling applications. The exact procedures and priority-rule-based heuristics fail to search for the optimum solution to the RCPSP of large-sized project networks in a reasonable amount of time for successful application in practice. This paper presents a permutation-based elitist genetic algorithm for solving the problem in order to fulfill the lack of an efficient optimal solution algorithm for project networks with 60 activities or more as well as to overcome the drawback of the exact solution approaches for large-sized project networks. The proposed algorithm employs the elitist strategy to preserve the best individual solution for the next generation so the improved solution can be obtained. A random number generator that provides and examines precedence feasible individuals is developed. A serial schedule generation scheme for the permutation-based decoding is applied to generate a feasible solution to the problem. Computational experiments using a set of standard test problems are presented to demonstrate the performance and accuracy of the proposed algorithm.     

Comparing Schedule Generation Schemes in Resource-Constrained Project Scheduling Using Elitist Genetic Algorithm

An issue has arisen with regard to which of the schedule generation schemes will perform better for an arbitrary instance of the resource-constrained project scheduling problem (RCPSP), which is one of the most challenging areas in construction engineering and management. No general answer has been given to this issue due to the different mechanisms between the serial scheme and the parallel scheme. In an effort to address this issue, this paper compares the two schemes using a permutation-based Elitist genetic algorithm for the RCPSP. Computational experiments are presented with multiple standard problems. From the results of a paired difference experiment, the algorithm using the serial scheme provides better solutions than the one using the parallel scheme. The results also show that the algorithm with the parallel scheme takes longer to solve each problem than the one using the serial scheme.     

Particle Swarm Optimization for Preemptive Scheduling under Break and Resource-Constraints

This paper introduces a particle swarm optimization (PSO)-based methodology to implement preemptive scheduling under break and resource-constraints (PSBRC) for construction projects. The PSBRC under study allows the preemptive activities to be interrupted in off-working time and not to resume immediately in the next working period because all the limited resources are to be reallocated during a break. The potential solution to the PSBRC, i.e., a set of priorities deciding the order to start the activities or restart the interrupted activities, is represented by the multidimensional particle position. Hence PSO is applied to search for the optimal schedule for the PSBRC, in which a parallel scheme is adopted to transform the particle-represented priorities to a schedule. Computational analyses are presented to verify the effectiveness of the proposed methodology. This paper provides an attempt to make use of preemption and break for the resource-constrained construction project with the objective of minimizing project duration.     

Particle Swarm Optimization for Construction Site Unequal-Area Layout

Layout of temporary facilities on a construction site is essential to enhancing productivity and safety, and is a complex issue due to the unique nature of construction. This paper proposes a particle swarm optimization (PSO)-based methodology to solve the construction site unequal-area facility layout problem. A priority-based particle representation of the candidate solutions to the layout problem is proposed. The particle-represented solution in terms of priorities should be transformed to the specific layout plan with consideration of nonoverlap and geometric constraints. In addition, a modified solution space boundary handling approach is proposed for controlling particle updating with regard to the priority value range. Computational experiments are carried out to justify the efficiency of the proposed method and investigate its underlying performances. This study aims at providing an alternative and effective means for solving the construction site unequal-area layout problem by utilizing the PSO algorithm.     

Prediction of RNA Secondary Structure Based on Particle Swarm Optimization

A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization(PSO).PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space.We designed an efficient objective function according to the minimum free energy,the number of selected stems and the average length of selected stems.We calculated how many legal stems there were in the sequence,and selected some of them to obtain an optimal result using PSO in the right of the objective function.A method based on the improved particle swarm optimization(IPSO)was proposed to predict RNA secondary structure,which consisted of three stages.The first stage was applied to encoding the source sequences,and to exploring all the legal stems.Then,a set of encoded stems were created in order to prepare input data for the second stage.In the second stage,IPSO was responsible for structure selection.At last,the optimal result was obtained from the secondary structures selected via IPSO.Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method.Compared with other six methods,the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results.     

基于粒子群最大似然估计的焊缝早期隐性损伤磁记忆精确定位模型

针对由于焊接残余应力、磁场噪声等干扰,造成磁记忆检测在焊缝早期隐性损伤位置定量评价上的困难,提出基于粒子群算法优化的最大似然估计磁记忆梯度定量模型.通过对预制未焊透缺陷的Q235焊接试件进行焊缝疲劳拉伸实验,同步对比扫描电镜和X射线检测结果,发现磁记忆信号梯度对早期隐性损伤位置反应比较敏感,并获得了梯度随着与隐性损伤的距离增大而减小的衰减变化规律,构建隐性损伤位置参数与磁记忆梯度的非线性函数,考虑磁场噪声对隐性损伤定位结果的影响,引入最大似然估计建立目标函数,进一步考虑目标函数的非线性容易陷入局部极值而非全局极值的问题,采用具有全局搜索能力的粒子群算法对目标函数进行优化,建立基于粒子群最大似然估计的焊缝隐性损伤位置磁记忆定量模型,验证结果表明定位误差仅为3.48%,为实际工程中利用磁记忆技术及时发现早期隐性损伤并精确定位提供了新的思路.     

Modification of Minimum Moment Approach in Resource Leveling

This paper proposes a modification to the minimum moment approach that is used for resource leveling as presented by Harris and based upon the critical path method. The proposed and the traditional methods were developed with the assumption of no activity splitting and a fixed project duration with unlimited availability of resources. The difference between these methods is in the criteria of selecting the activity that has to be shifted from its original position to a better position. This is judged by the change in the statical moment of the resource histogram before and after such movement. In the proposed method, and for the activities that lie at the same sequence step, the activity that is to be shifted first is selected based upon both the value of its free float (S) and the value of its resource rate (R). In this way, the calculation of the improvement factor is needed only to determine the extent to which an activity is to be shifted. On the other hand, using the traditional method, the activity with the maximum improvement factor found for each possible day of shifting is selected first. The process is then repeated for all remaining activities using the updated histogram resulting from the shifted activity. The proposed method significantly reduces the calculations so that the number of iterations in each sequence step is equal to the number of its noncritical activities (n) as compared to (n!) in the traditional method. In addition, the calculation process using the proposed method is easier—especially for manual computations—than the traditional one. The results were insignificantly different, and in many cases they were identical. In this paper, the traditional and the proposed methods will be presented along with an example problem that was solved using the two methods. It should be noted that neither of the two methods provides the true minimum moment.     

Applying Optimal Control Model to Dynamic Equipment Allocation Problem: Case Study of Concrete-Faced Rockfill Dam Construction Project

This paper presents an optimization method for solving a dynamic equipment allocation problem (DEAP) encountered during the implementation of a concrete-faced rockfill dam construction project. In contrast to prior studies, the uncertainties associated with equipment failures have been explicitly considered in the objective function of our mathematical model that maximizes construction throughput. Specifically, our research assumes that the equipment failure time is characterized by a Weibull distribution, which has been justified by a chi-square goodness-of-fit test. A failure probability–work time equation is also used to characterize the relationship between the equipment failure probability and mean time to work. Furthermore, our model considers multiple types of equipment, and each is capable of doing different jobs. The model is confined by the relationship equations, initial conditions, and constraint conditions. Then, the particle swarm optimization (PSO) technique is applied to search for the optimal solution of the DEAP. Finally, the Shuibuya Hydropower Project was used as a real-world example to demonstrate the practicality and efficiency of the optimization method.     

基于改进粒子群优化算法的机器人路径规划

提出了一种改进粒子群优化算法的移动机器人路径规划方法。该方法首先将粒子群分成两组,对其中一组加入变异算子,能提高种群的多样性和避免粒子群优化算法的早熟。该方法模型简单,算法复杂度低,收敛速度快。仿真实验结果获得了从起点到终点的无碰撞路径,证实了该方法的有效性和可行性。     

Sequence Planning for Electrical Construction

The sequences in which work is completed bear significantly on the performance of electrical contractors in building construction projects. When project work sequences are poorly planned or poorly executed, electrician constructors often must contend with compressed schedules, trade stacking, and out-of-sequence work to ensure timely completion of a project. This paper analytically evaluates the importance of sequence planning to efficient electrical work. It describes changes that were made to crew-level planning procedures for an electrical contractor and the impact these had on crew performance. The analysis shows that sequence planning at both the project level and the crew level are important to the performance of electrical crews. Most notably, a strong correlation (0.73) was detected between crew planning performance one week and crew productivity performance in the following week. Results of the study are provided. Principles of sound sequences and guidelines for sequence planning are also captured from the analysis. These sequence guidelines are designed to avoid, where possible, the often adverse project conditions in which electrical contractors find themselves and to handle those conditions most effectively when they cannot be avoided.     

Dynamic Layout Planning Using a Hybrid Incremental Solution Method

Efficiently using site space to accommodate resources throughout the duration of a construction project is a critical problem. It is termed the “dynamic layout planning” problem. Solving it involves creating a sequence of layouts that span the entire project duration, given resources, the timing of their presence on site, their changing demand for space over time, constraints on their location, and costs for their relocation. A dynamic layout construction procedure is presented here. Construction resources, represented as rectangles, are subjected to two-dimensional geometric constraints on relative locations. The objective is to allow site space to all resources so that no spatial conflicts arise, while keeping distance-based adjacency and relocation costs minimal. The solution is constructed stepwise for consecutive time frames. For each resource, selected heuristically one at a time, constraint satisfaction is used to compute sets of feasible positions. Subsequently, a linear program is solved to find the optimal position for each resource so as to minimize all costs. The resulting sequence of layouts is suboptimal in terms of the stated global objective, but the algorithm helps the layout planner explore better alternative solutions.     

Control of the Molten Steel Level in the Top Side-Pouring Twin-Roll Casting Process Based on Fuzzy Rules Optimized by Particle Swarm Optimization Algorithm

Twin-roll strip casting is a near-net-shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time-varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model-based controller. Top side-pouring twin-roll casting is a new kind of twin-roll strip casting technology. This study introduces the control system of the top side-pouring twin-roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side-pouring twin-roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed.     

Educating Civil Engineering Professionals of Tomorrow

Engineering education is currently facing unprecedented challenges and opportunities. Engineering institutions are being called upon to educate the architectural, engineering, and construction (A/E/C) professionals of tomorrow by complementing their traditional engineering education with the transfusion of information technology and process automation concepts through the necessary reorganization of classes and academic curricula. This paper presents a framework for an interdisciplinary course sequence in civil engineering, project management, and information technology centered on the concepts of fully integrated and automated project processes (FIAPP). The described sequence enables students to benefit pedagogically from working in truly multidisciplinary teams, to enrich their educational experience by bringing real world projects to academic settings, and to teach them fundamental principles in integration and automation of project processes highlighting the value of such integrated project management systems (information management, planning, design, construction management, procurement, operations, and maintenance). Furthermore, the course sequence addresses deficiencies in current one-dimensional educational curricula and needs expressed by educators, students, and industry professionals. The paper presents experiences and knowledge gained from the aforementioned academic sequence on FIAPP and on the utilization of three-dimensional computer models and associated databases in the management of A/E/C processes.     

Parallel allocation of attention prior to the execution of saccade sequences.

In a series of 5 experiments, the allocation of attention prior to the execution of saccade sequences was examined by using a dual-task paradigm. In the primary task, participants were required to execute a sequence of 2 endogenous saccades. The secondary task was a forced-choice letter identification task. During the programming of the saccade sequences, letters were briefly presented at the saccade goals and at no-saccade locations. The results showed that performance was better for letters presented at any of the saccade goals than for letters presented at any of the no-saccade locations. The results support a spatial model that assumes that prior to the execution of a saccade sequence, attention is allocated in parallel to all saccade goals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An Improved Particle Swarm Optimization for Feature Selection

Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO (MSPSO) to solve discrete problems,which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection (IFS) method by integrating MSPSO, Support Vector Machines (SVM) with F-score method. The IFS method aims to achieve higher generalization capability through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm (GA) based and the grid search based methods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features.     

Impact of Change Orders on Labor Efficiency for Electrical Construction

Change orders are a source of many disputes in today's construction industry. The issue at hand is whether or not the execution of change orders work has a negative impact on overall labor efficiency on a construction project. Previous literature demonstrates evidence that change orders affect labor efficiency. Attempts have been made to quantify these impacts by many researchers, with limited success. Using the electrical construction industry, a research study has been conducted to quantify the impacts of change orders on labor efficiency. In this paper, results of hypothesis testing and regression analysis are presented. A linear regression model that estimates the loss of efficiency, based on a number of independent variables, is also presented. The independent variables used in this model are (1) qualitative and quantitative criteria used to determine whether projects are impacted by changes or not; (2) the estimate of change order hours for the project as a percentage of the original estimate of work hours; (3) the estimate of change order hours for the project; and (4) the total number of years that the project manager had worked in the construction industry. Additional projects were used to validate the model, with an average error rate of 5%. The results of this research study are useful for owners, construction managers, general contractors, and electrical specialty contractors, because they provide a means to estimate the impact of a change order under certain project conditions. This research also identifies factors, which, when understood and effectively managed, may be used to mitigate the impact of a change order on project costs and efficiency.     

A negative order-repetition priming effect: Inhibition of order in unattended auditory sequences?

A novel negative priming (NP) effect is reported in which serial recall for a sequence of visually presented digits was poorer if the same sequence was presented as an irrelevant auditory sequence on the previous trial (Experiments 1 and 2). The effect was enhanced when attention was divided between the to-be-repeated auditory sequence and the concurrent to-be-remembered (TBR) sequence (Experiment 3). When the TBR sequences were also presented auditorily, NP arose only when the repeated TBR sequence was in the same voice as the previous irrelevant sequence; a voice mismatch produced positive priming (Experiment 4). The results suggest that the order of auditory events is registered preattentively and that inhibition may be applied to the acoustic transitions between irrelevant events. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Critical Space Analysis

The construction space scheduling problem has received relatively little attention from researchers and practitioners. We now have sophisticated methods of planning and analyzing the sequence of tasks within the work breakdown structure through time, but the problem of planning where on site those tasks are to be executed is not well-supported especially as those spaces are dynamic as the project progresses. We know that congestion on site reduces output and generates hazards, yet construction planners presently have to rely upon experience and intuition. The research reported here presents a decision support tool for construction project planners to help them address the space scheduling problem. After a review of recent developments in construction space scheduling, the concept of critical space analysis is presented. This forms the basis of decision support tools presented for marking up available space, allocating tasks to spaces, and analyzing and optimizing space loading in relation to the critical path—what we call space-time broking. Requirements capture and evaluation reports from construction planners suggest that the tools presented here have immediate practical relevance. The paper will, therefore, be of interest to both practitioners and researchers.     

智能微粒群算法

微粒群算法是继蚁群算法之后提出的又一种新型的进化计算技术，具有典型的群体智能的特性。本文首先介绍微粒群算法，然后在对算法的关键参数进行分析的基础上，从算法的离散二进制表示、参数选择与设计、群体组织与进化以及混合微粒群算法等方面对国内外微粒群算法的研究进展进行综述。     

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.