Concurrent Construction Scheduling Simulation Algorithm

A scheduler can accelerate a construction project by setting up overlapping activities. Activities are said to be overlapping if two activities that are normally executed in sequence are performed in parallel, the successor starting before the end of the predecessor. This article presents a novel method that identifies an optimal overlap rate between critical activities to provide a time‐cost trade‐off analysis, hence reducing completion time and cost without allocating additional resources. This method is then coded into Concurrent Construction Scheduling Simulation System (C²S²) that makes use of schedule data exported from Primavera Project Planner (P6), integrates the activities’ concurrency attributes, calculates the rework probability/amount, and computes the variability in project completion time and cost by adjusting the overlap between activities. This study is of value to practitioners because C²S² allows establishing a compressed schedule that satisfies a limited budget within a specified contract duration. The study is also of relevance to researchers because it identifies the optimal overlaps between activities expeditiously. Two test cases verify the usability and validity of the computational methods implemented in the system.     

Network‐level scheduling of road construction projects considering user and business impacts

Construction projects are often associated with partial or full road closures, which result in user costs and community disruptions in terms of reduced business productivity. A number of studies have addressed the problem of scheduling construction projects based on a variety of stakeholder objectives. Yet still, there seems to exist a few gaps regarding (1) possible tradeoffs between road user cost reduction and business cost reduction associated with optimal scheduling, (2) role of the project type (rehabilitation and capacity expansion) on the solution methodology, and (3) lack of solution algorithm to address the problem complexity by deriving the optimal solution. In addressing these gaps, this article adopts a novel approach for developing an optimal project schedule for multiple road projects within a construction horizon. The goal is to minimize the overall cost of the projects to road users and adjacent businesses over the construction horizon. The project scheduling problem is formulated as a mixed‐integer nonlinear program. We solve the problem using a local decomposition method. The methodology is demonstrated using the Sioux Falls city network with two project types: capacity expansion and rehabilitation. The results of the numerical experiment suggest that (1) the solution algorithm converges to optimal solution in finite iterations and (2) a network‐wide scheduling of urban road projects using explicit optimization can yield a significant reduction in business disruption costs while incurring a relatively smaller increase in system travel time, and overall, is superior to a schedule developed only considering the total system travel time.     

Study on construction material allocation policies: A simulation optimization method

Due to uncertainty in both demand and supply, material shortages are difficult to completely avoid. To reduce the effect on the schedule and cost performance of construction projects, managers should allocate limited material among activities effectively. Motivated by observations of construction practices, this paper investigates the integration of supply logistics and site logistics issues and develops a framework to model inventory replenishment and allocation decisions jointly. On the basis of the activity feature information (e.g., schedule, cost, and demand), we propose five allocation policies to support the integrated inventory management process: schedule-based, cost-based, demand-based, schedule-cost-based, and schedule-demand-based policies. Meanwhile, a genetic algorithm (GA)-based simulation optimization method is utilized to solve the integrated inventory model and find the optimal inventory level under a given allocation policy. Based on a large set of fictitious project networks with different path difference (PD), a computational analysis is conducted to make detailed interpolicy comparisons. It is shown that for a project network with a small (or large) PD value, the schedule-based (or schedule-cost-based) policy is the most appropriate choice.     

Fuzzy-multi-objective particle swarm optimization for time–cost–quality tradeoff in construction

The time–cost–quality tradeoff (TCQT) problem is to decide an optimal combination of construction methods with the objective of minimizing cost and time while maximizing quality. Searching for such an optimal combination of construction methods needs to evaluate the total cost, time and quality of the project. These performances, especially the quality, may be collected and recorded in terms of imprecise or vague data rather than precise numbers. This paper presents a fuzzy-multi-objective particle swarm optimization to solve the fuzzy TCQT problem. The time, cost and quality are described by fuzzy numbers and a fuzzy multi-attribute utility methodology incorporated with constrained fuzzy arithmetic operations is adopted to evaluate the selected construction methods. The particle swarm optimization is applied to search for the TCQT solutions by incorporating the fuzzy multi-attribute utility methodology. The proposed methodology is implemented and justified through computational analyses. The study is expected to provide an alternative methodology for solving the time–cost–quality tradeoff problem.     

Simulation-based methodology for project scheduling

This paper describes a discrete-event simulation that incorporates the critical path method (CPM) in generating comprehensive information for construction project scheduling or planning. An activity-based graphical model able to consider complexities in construction, for example, unbalanced logical dependencies, unbalanced resource involvements and repetitive activity operation is proposed. An activity scanning (AS)-based forward pass algorithm for early-time information and a backward search algorithm for late-time information are developed. The simulation results that include both resource utilization statistics and late-time information (i.e. latest start or finish time and total float (TF)) of every activity for each cycle of operation cannot be easily achieved through traditional simulation or CPM. Illustrations demonstrate the advantages of the activity-based graphical model and the impacts of resource allocation policies on the schedule. The proposed methodology aims at widening the application of construction simulation by providing float time information and enhanced modelling capability.     

Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization

The absence of a valid resource-constrained critical path method (CPM) not only hampers the widespread use of mainstream project scheduling software in construction management practice, but also destabilizes the very foundation of any sophisticated, CPM-based time or cost analysis in construction scheduling research. This has motivated us into developing an innovative, fully-automated solution to resource-constrained CPM called the Simplified Simulation-based Scheduling system (short as S3). S3 takes advantage of the simplified discrete event simulation approach (SDESA) and the evolutionary optimization technique called particle swarm optimizer (PSO) to automate the formulation of a resource-constrained schedule with the shortest total project duration. We clarify basic issues of resource scheduling, elaborate on the formation of a CPM simulation model by SDESA, present PSO algorithms, and discuss the PSO solution formulation and simulation–optimization interaction in relation to the development of S3 software. In order to introduce S3 to construction schedulers, we also reference the relevant functionalities and features of Primavera Project Planner (P3) and Microsoft Project, which are applied alongside S3 in two case studies. The first case is a classic textbook example while the second case is based on a real drainage project in Hong Kong. In both cases, S3 eclipses the current CPM software with respect of (1) shortening the total project duration; (2) optimizing provisions of resources of various types; and (3) producing valid total float values to guide schedule implementation.     

Time–cost optimization: using GA and fuzzy sets theory for uncertainties in cost

Uncertainties should be considered in any time–cost trade‐off problems when minimizing project cost and duration, which leads to the so‐called stochastic time–cost trade‐off problem. A new approach to investigate stochastic time–cost trade‐off problems employing fuzzy logic theory is presented. The proposed approach fully embeds the fuzzy structure of the uncertainties in total direct cost into the model. An appropriate GA is used to develop a solution to the multi‐objective fuzzy time cost model. The accepted risk level of the project manager is defined through α cut approach for which a separate Pareto front with set of non‐dominated solutions has been developed. To compare the alternative set of options for any assumed project duration, associated fuzzy costs for different values of α cut are ranked employing two appropriate approaches for fuzzy costs comparison. The proposed models are applied to solve two benchmark test problems. It is shown that the models facilitate the decision‐making process by selecting specified risk levels and employing the associated Pareto front.     

The integration of CPM and material management in project management

The problem of scheduling a project for which expensive, long-lead inventory items have to be ordered from outside vendors is addressed. A logic aimed at minimizing the cost of such projects is presented along with an illustrative example. The logic is based on adding a material management module to the CPM analysis, evaluating the feasibility of the CPM schedule, and rescheduling the project in case long-lead items make the schedule generated by CPM infeasible. An attempt to integrate CPM and material management in a large-scale construction project is reported and possible extensions of this research are suggested.     

Automated trade‐off between time and cost in planning repetitive construction projects

An automated model is developed to support the optimization of the planning and scheduling of repetitive construction projects. The model provides the capability of optimizing two important objectives commonly sought in scheduling repetitive construction projects: minimizing project duration; and minimizing project cost. The model performs this multi‐objective optimization using a genetic algorithm approach. The output of the model is a set of optimal solutions that represent the trade‐off between time and cost in planning repetitive construction projects. Furthermore, the model can be utilized to find a single scheduling solution that provides the minimum overall project cost by simply adding project indirect cost to the obtained project direct cost for each of the obtained scheduling solutions on the Pareto optimal curve. Other important time‐related costs are also considered in the model including: early completion incentives, late completion penalties and lane rental costs. Providing the planners of repetitive construction projects with an automated set of optimal time–cost trade‐off solutions should contribute to cost‐effective and speedy delivery of this type of construction project. An application example is analysed to illustrate the use of the model and demonstrate its capabilities in generating optimal trade‐off solutions between minimizing the project time and cost for repetitive construction projects.     

Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction

Abstract: Semiconductor hookup construction (i.e., constructing process tool piping systems) is critical to semiconductor fabrication plant completion. During the conceptual project phase, it is difficult to conduct an accurate cost estimate due to the great amount of uncertain cost items. This study proposes a new model for estimating semiconductor hookup construction project costs. The developed model, called FALCON‐COST, integrates the component ratios method, fuzzy adaptive learning control network (FALCON), fast messy genetic algorithm (fmGA), and three‐point cost estimation method to systematically deal with a cost‐estimating environment involving limited and uncertain data. In addition, the proposed model improves the current FALCON by devising a new algorithm to conduct building block selection and random gene deletion so that fmGA operations can be implemented in FALCON. The results of 54 case studies demonstrate that the proposed model has estimation accuracy of 83.82%, meaning it is approximately 22.74%, 23.08%, and 21.95% more accurate than the conventional average cost method, component ratios method, and modified FALCON‐COST method, respectively. Providing project managers with reliable cost estimates is essential for effectively controlling project costs.     

论挣值法在工程项目时间管理中的施工管理技术应用

国内很多工程的时间管理（俗称计划进度控制）经常出现或大或小的问题,如在最后时期采取非常规手段赶工导致影响质量和增加成本。本文以H工程为例,对产生的进度问题进行分析研究,运用数据说明和逻辑推导等方法,否决传统仅仅采用关键路径法和资源投入比较法的工具技术,论证挣值法管理技术确实能够及时规避工期延误风险,并阐明挣值法的使用方法和步骤。     

Contingency estimation using a real options approach

In construction projects, contingency is the budget which is made available to cope with uncertainties that would incur schedule and cost overruns. Contingency estimation requires considering project cost, schedule and technology variability. The effect of dynamic project management of the decision maker should also be taken into account. Accordingly a real options approach for estimating contingency from the owner’s point of view is presented. This approach not only explicitly recognizes the uncertainty in the life cycle of a construction project, but also incorporates a dynamic project management mechanism into contingency estimation. The contingency is valued by minimizing the expected project cost via a multi‐stage stochastic model that accounts for the variability of project cost and schedule and the exercise of dynamic project crashing. The numerical results indicate that the value of the decision maker’s dynamic project management makes up an important portion of the contingency.     

基于DEA 的建筑工程项目综合优化研究

质量、成本、工期是工程项目管理的三大主要控制目标。首先以工期目标为变量,分析工期-成本、工期-质量的非线性关系,分别建立成本、质量目标与工期目标之间的量化非线性关系模型,进而建立三者之间的多目标优化模型。结合工程网络计划优化技术,用关键路径法进行若干次工期压缩,得到不同工期、成本、质量水平的若干施工方案,组成待选方案集,通过数据包络分析的方法分析不同方案的投入、产出效率,进而根据效率值择优选取施工方案。并结合算例证明了该优化方法的有效性,为项目决策提供支持。     

A Spatial‐Context‐Based Approach for Automated Spatial Change Analysis of Piece‐Wise Linear Building Elements

Changes of designs and construction plans often cause propagative design modifications, tedious construction coordination, cascading effects of errors, reworks, and delays in project management. Among various building elements, those having piece‐wise linear geometries (i.e., connected straight line segments), such as connected straight sections of ducts in mechanical, electrical, and plumbing systems, frequently undergo spatial changes in response to the changes of their surroundings. On the other hand, the piece‐wise linear geometries pose challenges to analyzing and controlling changes in construction and facility management. State‐of‐the‐art 3D change detection algorithms often face ambiguities about which points belong to which objects when piece‐wise linear object are spacked in small spaces. This article examines a spatial‐context‐based framework that uses spatial relationships between piece‐wise linear building elements (ducts in this article) to enable fast and reliable association of 3D data with ducts in as‐designed models for supporting reliable change analysis. Three case studies showed that this framework outperformed a conventional change detection method, and could handle large dislocations of piece‐wise linear elements and occlusions.     

Stochastic time–cost tradeoff analysis: A distribution-free approach with focus on correlation and stochastic dominance

The present study explores how correlated Monte Carlo simulation (MCS) coupled with a multiobjective particle swarm optimization algorithm can expand the time–cost tradeoff analysis in the presence of uncertainty. The goal of the proposed framework is to find the optimal set of activity options, whose objectives are evaluated as value-at-risk measures of project duration and total cost. The proposed framework incorporates the Gaussian copula into MCS to treat statistical dependence between uncertain variables, with no restriction on the estimation process and distribution type. This paper elucidates the definition of stochastic dominance relations, based on which a decision rule is established to prescreen dominated solutions so as to alleviate computational burden. A practical project has been used to validate the proposed framework by comparisons with enumeration and NSGA-II (non-dominated sorting genetic algorithm). In addition to nondominated solutions, the proposed framework provides insightful risk assessments.     

A framework of critical resource chain for project schedule analysis

Analysing a schedule is beneficial to help stakeholders understand the scheduled project. Project schedules, which create time plans based on the critical path method (CPM) or on resource‐constrained project scheduling problem (RCPSP) optimization, are targets herein. The Theory of Constraints (TOC) treats a schedule as a system. Schedule elements are suspected constraints and a goal depends on the schedule creation policy. Resource information is further surveyed herein to identify true constraints. A framework is proposed to integrate identified constraints on a schedule, and the critical resource chain concept is introduced. Three scenarios illustrate the proposed framework under different scheduling considerations. Results explain schedule constraints, and several schedule analysis issues are discussed.     

基于线形图的地铁工程4D进度控制系统研究与应用

地铁工程涉及专业广、建设周期长、内容复杂,且属于线形工程,采用CPM方法编制进度计划,会导致进度计划非常的复杂、难以直观反映任务的地理位置及工作面搭接关系和施工过程中的空间冲突问题。提出一种基于线形图的地铁工程进度管理方法,形象地显示地铁工程的每一个任务的地理位置和耗费的时间,以及同一工作面上不同任务的位置搭接关系。并通过构建基于线形图的进度冲突识别算法,实时检测当前进度计划可能存在的空间冲突问题。在此基础上,通过数据库将进度计划与3D/CAD模型建立关联关系,构建4D可视化进度管理系统,直观地反映施工过程中的空间冲突问题。本系统在某地铁工程中成功应用,及时发现了施工计划中的空间冲突问题,实现了对项目实时、精准的控制,提升了地铁工程进度管理的水平,提高了投资效益。     

Time‐Varying Lane‐Based Capacity Reversibility for Traffic Management

Abstract: This article presents a new bi‐level formulation for time‐varying lane‐based capacity reversibility problem for traffic management. The problem is formulated as a bi‐level program where the lower level is the cell‐transmission‐based user‐optimal dynamic traffic assignment (UODTA). Due to its Non‐deterministic Polynomial‐time hard (NP‐hard) complexity, the genetic algorithm (GA) with the simulation‐based UODTA is adopted to solve multiorigin multidestination problems. Four GA variations are proposed. GA1 is a simple GA. GA2, GA3, and GA4 with a jam‐density factor parameter (JDF) employ time‐dependent congestion measures in their decoding procedures. The four algorithms are empirically tested on a grid network and compared based on solution quality, convergence speed, and central processing unit (CPU) time. GA3 with JDF of 0.6 appears best on the three criteria. On the Sioux Falls network, GA3 with JDF of 0.7 performs best. The GA with the appropriate inclusion of problem‐specific knowledge and parameter calibration indeed provides excellent results when compared with the simple GA.     

Model-based dynamic resource management for construction projects

Excess resource idling can result in cost overruns, while low resource coverage or long lead-time in resource acquisition can delay the project schedule. Therefore, systematically managing this tradeoff is critical to ensure project delivery in time and within budget. In addition, to provide practically useful guidelines and tools, the dynamic construction process needs to be realistically represented. As an effort to address these issues, a model-based dynamic approach is proposed for construction resource management. The dynamics of construction progress and the tradeoff with resource coverage are identified. Then, the dynamic resource management model that has been developed using system dynamics is described. By simulating the model with heuristic and industry data, the effect of resource coverage on project performance is quantified and policy implications are obtained for dynamic resource management. Finally, the use of the model as an automated tool is demonstrated.     

Improving workflow and resource usage in construction schedules through location-based management system (LBMS)

Critical Path Method (CPM), a planning and controlling technique, is widely used in the construction industry. However, CPM is criticized for its lack of workflow and inability to schedule continuous resource usage. Location-Based Management System (LBMS) fill these gaps and has been implemented in many construction projects. We propose that LBMS will improve schedules and project performance, addressing CPM’s main shortcomings. This study is composed of three case studies. CPM schedules were analyzed and were improved using LBMS tools. The resulting schedules show improved workflows, crew balancing, resource usage and had fewer interruptions, without affecting project duration. Furthermore, LBMS schedules were optimized with only a few scheduling operations and fewer planning elements. The computational benefit of LBMS increases with the number of locations and tasks in a schedule. Project managers will benefit from a simpler scheduling process and better resource flow.