基于遗传算法的建筑工程三大目标综合优化

工期、成本和质量是建筑工程的三大主要目标,他们能否顺利实现将直接影响项目总体目标的好坏,而以往提出的模型大都是侧重于对工期一成本两目标的综合优化,很少涉及质量目标,这显然是满足不了现实要求的。因此在这里提出了一个对三大目标综合优化的模型,它是在工期一成本、工期一质量为线性关系的基础上建立的,并且以综合目标函数作为适应度函数建立的依据,运用遗传算法进行求解。最后用一个实例验证了此模型的可行性,并且运算之后可以得到多组解,决策者可以根据自己的偏好选择自己满意的一组解。     

Multi-mode resource-constrained discrete time–cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm

Minimizing both project time and cost is an important matter in today's competitive environment. Therefore trade-off between project time and cost is necessary. In projects, each activity can be started at different time points, depending on its precedence relationship and resource availability. Also cost and duration of the activities could be changed depending on the allocated resources. In addition, another strategy that affects the project total time and cost is resource leveling, which is applied to reduce excessive fluctuations in the resource usage. In this paper multi-mode resource-constrained project scheduling problem (MRCPSP), discrete time–cost trade-off problem (DTCTP) and also resource allocation and resource leveling problem (RLP) are considered simultaneously. This paper presents the multi-mode resource-constrained discrete time–cost-resource optimization (MRC-DTCRO) model in order to select starting the time and the execution mode of each activity satisfying all the project constraints. To solve these problems, non-domination based genetic algorithm (NSGA-II) is employed to search for the non-dominated solutions considering total project time, cost, and resources moment deviation as three objectives. The results of MRC-DTCRO model presented in this paper show that adding the resource leveling capability to the previously developed multi-mode resource-constrained discrete time–cost trade-off problem (MRC-DTCTP) models provides more practical solutions in terms of resource allocation and leveling, which makes this research applicable to both construction industry and researchers.     

A two-phase GA model for resource-constrained project scheduling

In construction scheduling, problems can arise when each activity could start at different time points and the resources needed by the activities are limited. Moreover, activities have required conditions to be met, such as precedence relationships, resource requirements, etc. To resolve these problems, a two-phase GA (genetic algorithm) model is proposed in this paper, in which both the effects of time-cost trade-off and resource scheduling are taken into account. A GA-based time-cost trade-off analysis is adopted to select the execution mode of each activity through the balance of time and cost, followed by utilization of a GA-based resource scheduling method to generate a feasible schedule which may satisfy all the project constraints. Finally, the model is demonstrated using an example project and a real project.     

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.     

工程项目工期--成本综合模糊优化

针对工程项目中工期的不确定性,本文提出了一种综合模糊集合论与遗传算法的新方法来解决工期—— 成本优化问题。该算法能较好的寻找项目在不同风险水平下的工期——成本优化的最优解,同时能深入分析工期 和成本的理想平衡点,为决策者提供相关信息,用于指导实践。     

Fuzzy-based MOGA approach to stochastic time–cost trade-off problem

In construction projects, time and cost are manageable objectives with significant interdependencies for which sets of trade-offs may exist. This study presents a new approach for the solution of time–cost trade off problems in an uncertain environment. Fuzzy numbers are used to address the uncertainties in the activities execution times and costs. Fuzzy sets theory is then explicitly embedded into the optimization procedure. A multi-objective genetic algorithm is specially tailored to solve the discontinuous and multi-objective fuzzy time- cost model with relatively large search space. The proposed approach identifies the best set of implementation options defined by the sets of non-dominated solutions Accepted risk level and optimism of the decision maker are addressed using α-cut approach and optimism index (β) respectively. To illustrate the application and performance of the model, two case examples are presented, for which separate Pareto fronts are developed. The fuzzy presentation of the non-dominated solution helps the project manager to apply his own level of risk acceptance and degree of optimism in decision making process. Different risk acceptance level and/or optimism leads to different scheduling and sets of Pareto solutions from which the project manager may select his preferred options.     

Dynamic modeling of the quantitative risk allocation in construction projects

Allocation of construction risks between owners and their contractors has a significant impact on the total construction costs. This research presents an integrated fuzzy-system dynamics approach for quantitative risk allocation. All the factors affecting the risk allocation process are modeled using system dynamics approach. Fuzzy logic is integrated into system dynamics modeling structure to account for the existing uncertainties. The values of different factors that have an uncertain nature are determined by fuzzy numbers. The application of Zadeh's extension principle and interval arithmetic is proposed for the system dynamics to enable the system outcomes to be presented considering uncertainties in the input variables. Using the proposed integrated fuzzy-SD model, the project cost is simulated at different percentages of risk allocation. The optimum percentage of risk allocation is determined as a point in which the project cost is minimized. The performance of the proposed method is assessed by employing the method in a tunneling project.     

A TIME-COST TRADE-OFF MODEL WITH RESOURCE CONSIDERATION USING GENETIC ALGORITHM

A genetic algorithm (GA)-based model to deal with time-cost trade-off problems is presented. The traditional algorithms assume the unlimited availability of resources. Instead, the proposed model allows for resource constraints. Accordingly, the trade-off is considered in terms of the level of resources to be deployed for each activity. At the same time the model schedules the starts of the activities in order to optimize the objective function. The activity starts is a significant factor in the case of non-uniform available profile. The GA searches both spaces of resource utilization and activity starts to determine the optimal schedule that conforms to the resource available profile. Although, in principle, this has the potential of a combinatorial explosion, earlier work suggests that the GA-based model can be applied to larger networks without appearing to suffer from this problem. Allowing the project manager to think in terms of resource utilization makes this approach consistent with resource allocation problems, and more pragmatic and appealing. The model can also be used to solve conventional time-cost trade-off problems by a simple modification of the objective function. Numerical examples are used to illustrate the working of the model. The examples include a case of varying resource available profile.     

Optimization of time and cost

The most significant decisions relating to the planning and control of projects concern the trade-off between the time and cost parameters. Existing exact models have proved difficult or virtually impossible to implement owing to the large number of variables and constraints involved and to the difficulty of the mathematical analysis. The paper presents an integer linear programming model based on points of breakthrough on the time-cost curve. This approach results in a reduction in the number of variables since the number of breakthrough points is less than the number of points where there is a discontinuity in the time-cost slope. The model is implemented using a program called Trans, which schedules the problem in such a way that it is suitable for the application of a standard integer linear programming package. Trans eliminates the necessity for any mathematical analysis in the formulation of the problem.     

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.     

Discrete time–cost–environment trade-off problem for large-scale construction systems with multiple modes under fuzzy uncertainty and its application to Jinping-II Hydroelectric Project

This paper presents a discrete time–cost–environment trade-off problem for large-scale construction systems with multiple modes under fuzzy uncertainty. A multi-objective decision making model is established in which the total project duration is regarded as a fuzzy variable. To deal with the uncertainty, the fuzzy numbers in the model are defuzzified by using an expected value operator with an optimistic–pessimistic index. The objective functions are to minimize the total project cost, project duration, crashing cost, and environmental impact. Furthermore, a fuzzy-based adaptive-hybrid genetic algorithm is developed to find feasible solutions. The one-point crossover and repairing strategy for mutations are designed to avoid infeasible solutions. Finally, the Jinping-II Hydroelectric Project is used as a practical example to demonstrate the practicality and efficiency of the model. Results and a sensitivity analysis are presented to highlight the performance of the optimization method, which proves to be very effective and efficient compared to other algorithms.     

工程项目时间/成本模糊集优化

从分析工程项目的时间／成本影响因素入手，综合考虑定性和定量因素，包括管理水平、管理复杂程度、资源分配情况等，提出了模糊集理论的时间／成本决策模型，从而促进工程项目时间／成本模糊集优化的研究。     

基于BIM 和网络计划技术的建设工程施工进度—成本研究

为了提高建设工程的施工水平,应当寻求在缩短施工进度的同时降低施工成本的方法。建立了基于BIM 与网络计划技术的施工进度—成本架构体系和优化模型。运用BIM 能够快速准确地提取与处理庞大工程量的优势,并结合传统的网络计划技术、工作分解结构（WBS）方法协同处理进度—成本优化问题,给企业提供了一种新的优化施工进度—成本的方法。该方法有助于充分发挥现代信息技术与传统经典方法理论的优势,进一步提高工程建设的信息化水平,更好地实现项目的管理目标。     

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.     

System reliability theory based multiple-objective optimization model for construction projects

Although the system reliability theory has a high capability in quality quantification, while system reliability optimization (SRO) has been well developed in manufacturing engineering, seldom can their applications be found in the construction industry. This study aims to develop a system reliability theory based multiple-objective optimization model to conduct SRO, and then identify the cost–quality trade-off solution for construction projects. First, the whole construction project is treated as a system composed of different work packages. Second, the reliability function is employed to quantify the quality performance and the nonlinear cost-reliability function is set up. Moreover, according to the physical arrangements among each work package, the system reliability structural function is determined. Third, the total construction cost minimization and system reliability maximization are defined as multiple-objectives. The particle swarm optimization algorithm is employed to search for the Pareto-optimal solutions, from which the final cost–quality trade-off solution can be selected. A real construction case is used to evaluate the workability of the proposed model and the results have fully proven its validity and practicality.     

基于模糊规划的工程项目工期—成本—质量均衡优化研究

在工程项目的实施过程中,由于存在技术、天气、人员等诸多不确定因素的影响,导致工期、成本、质量这三大目标均具有模糊性。由于工期、成本、质量是相互关联的矛盾统一体,对这3 个不确定目标约束进行均衡优化,也就成为工程项目管理的重要内容之一。根据可信性理论,建立了多模式工期—成本—质量均衡优化的模糊期望值模型;设计了模糊模拟、神经网络和多目标粒子群算法相结合的混合智能算法来求解模型;并通过一个工程实例验证了所构建的多模式工期-成本-质量均衡优化模型的合理性和混合智能算法对该类问题求解的有效性。     

Uncertainty Processing and Risk Monitoring in Construction Projects Using Hierarchical Probabilistic Relational Models

Construction projects do not often reach their expected results regarding time, cost, and quality, due to the internal and external environment variations. Despite a substantial literature about risk management, no generic approach is proposed to represent construction project considering together technical and human dimensions or sustainability with their uncertainties. Modeling complex dynamical systems from heterogeneous pieces of knowledge varying in precision and reliability is a challenging task. This article proposes an innovative generic and versatile approach, based on the formalism of hierarchical probabilistic relational models to analyze and to propagate uncertainty in construction project regarding different levels of knowledge. The aim is to obtain a flexible, portable, and versatile model able to simulate the behavior of complex system's entities involved in any construction project at different levels of detail while taking uncertainty into account. To illustrate and highlight this approach, an academic example and a real case are proposed.     

建设项目多目标综合优化方法研究

对于工程的质量、工期、成本、资源的均衡等多个目标进行控制市建筑工程项目管理的重要内容。在具体分析各个目标之间相互作用关系的基础上,建立了工期-费用关系模型、质量工期关系模型和资源均衡-工期关系模型。进一步在工程费用-费用优化的基础上,引入工程质量、资源均衡目标,综合单目标模型,建立了进度计划的多目标模型,利用多目标模糊决策理论,对实施方案进行优选,最后用算例验证了方法的可行性和实用性。     

基于全寿命环境成本的工程结构维护方案优化

工程结构全寿命过程中存在有害气体、污水和固体废弃物排放等环境问题,但是由其造成的经济损失却很少在结构设计或工程决策过程中予以考虑。为此,将三类污染物排放的防治成本计入工程活动的环境成本中,得到了多种建筑材料、能源消耗、运输方式和建筑机械使用的环境成本。通过将环境成本与经典全寿命成本模型相结合,建立了包含环境成本的全寿命总成本模型。根据所提出的环境成本模型和全寿命总成本模型,以某一沿海高速公路桥梁结构为例,通过设置预防性维护措施和必要性维护措施,对其进行维护方案的多目标优化,分析环境成本对工程决策的影响。并对直接成本和环境成本的不确定性进行了分析,研究了环境成本折现率对环境成本影响。结果表明,在结构优化过程中考虑环境成本能够得到经济效益和环境综合性能更佳的工程方案。     

应对不确定性的水利工程项目责任成本测算方法

成本控制对提高项目管理水平、扩大项目利润空间具有重要意义。责任成本测算作为工程项目成本控制的有效方法被许多企业认可,但是工程项目的许多不确定性限制了这种方法的应用。以水利工程项目群为基础,从概率论的角度分析了人为、环境等不确定性因素对责任成本测算的影响,并提出应对不确定性的具体措施。针对水利工程项目特点对责任成本测算方法进行研究,提出了“小循环”成本测算方法,将不确定性因素产生的总影响考虑在责任成本测算额之内,为项目成本测算的合理性与准确性提供了思路,并为责任成本测算在水利工程项目中的应用提供了方法。