Project Scheduling Using Fuzzy Set Concepts

Probabilistic methods are being used increasingly in construction engineering. However, when a parameter is expressed in linguistic rather than mathematical terms, classical probability theory fails to incorporate the information. The linguistic variables can be translated into mathematical measures using fuzzy set and system theory. A construction management problem, i.e., estimation of the duration of an activity, is solved using this theory. In order to implement the proposed technique, various membership functions need to be estimated using judgment or with the assistance of experts. The proposed technique is not sensitive to small variations in the membership values. This is a very desirable property. However, the method is sensitive to the choice of the fuzzy relations. The uncertainty in the fuzzy relations can be modeled along with other sources of uncertainty. The mean and variance of the parameters involved in the problem under consideration are estimated here using a new method. The method maximizes the product of the sum of the membership associations for a certain frequency of occurrence and the corresponding frequency of occurrence. One of the main advantages of the proposed technique is that it can be easily implemented in existing computer programs for project scheduling.     

Fuzzy Repetitive Scheduling Method for Projects with Repeating Activities

In projects with repeating activities (such as multistory buildings, highways, or pipelines consisting of reiterating identical or similar units) and in which the activity unit production rates are characterized by uncertainty or imprecision, fuzzy set theory and the well-established repetitive scheduling method (RSM) can be combined to ensure uninterrupted usage of resources between similar activities in different units. The reason for this approach is that in practice the application of RSM may be hindered by several considerations, for example, repetitive units may be slightly different from each other, the performance of construction crews may vary, and there may be complex resource matching and sharing between activities and work sites. The proposed methodology is termed fuzzy repetitive scheduling method (F-RSM), and it requires a generalization of RSM in which schedules are represented by two- or three-dimensional graphs and whereby the concepts of a control segment and the controlling sequence area are introduced. The resulting methodology addressing the original RSM scheduling problem is presented in this paper.     

Stochastic Time-Cost-Resource Utilization Optimization Using Nondominated Sorting Genetic Algorithm and Discrete Fuzzy Sets

In a construction project, the cost and duration of activities could change due to different uncertain variables such as weather, resource availability, etc. Resource leveling and allocation strategies also influence total time and costs of projects. In this paper, two concepts of time-cost trade-off and resource leveling and allocation have been embedded in a stochastic multiobjective optimization model which minimizes the total project time, cost, and resource moments. In the proposed time-cost-resource utilization optimization (TCRO) model, time and cost variables are considered to be fuzzy, to increase the flexibility for decision makers when using the model outputs. Application of fuzzy set theory in this study helps managers/planners to take these uncertainties into account and provide an optimal balance of time, cost, and resource utilization during the project execution. The fuzzy variables are discretized to represent different options for each activity. Nondominated sorting genetic algorithm (NSGA-II) has been used to solve the optimization problem. Results of the TCRO model for two different case studies of construction projects are presented in the paper. Total time and costs of the two case studies in the Pareto front solutions of the TCRO model cover more than 85% of the ranges of total time and costs of solutions of the biobjective time-cost optimization (TCO) model. The results show that adding the resource leveling capability to the previously developed TCO models provides more practical solutions in terms of resource allocation and utilization, which makes this research relevant to both industry practitioners and researchers.     

Evaluation of the Resource-Constrained Critical Path Method Algorithms

This study evaluates the resource-constrained critical path method (RCPM), which the writers have recently proposed. RCPM establishes a critical path method (CPM)-like, resource-constrained schedule by resource-dependent activity relationships (or resource links) that the five-step RCPM technique identifies. With its CPM-like feature, RCPM provides the critical path and float data that are not available in traditional resource-constrained scheduling techniques. In addition, RCPM provides more flexibility to the schedule through identified alternative schedules, which allow certain activities to be executed beyond their late finish times without delaying the project completion. This paper evaluates the RCPM’s performance by comparing it with five related previous studies. A brief review of each study is also included in this paper. This comparison shows that RCPM performs well in identifying resource links and alternative schedules, compared to other methods. This study is of interest to academics because it highlights the advantages and disadvantages of different algorithms that have attempted to overcome present problems in traditional resource-constrained scheduling techniques.     

Methodology for Integrated Risk Management and Proactive Scheduling of Construction Projects

An integrated methodology is developed for planning construction projects under uncertainty. The methodology relies on a computer supported risk management system that allows for the identification, analysis, and quantification of the major risk factors and the derivation of their probability of occurrence and their impact on the duration of the project activities. Using project management estimates of the marginal cost of activity starting time disruptions, a heuristic procedure is used to develop a stable proactive baseline schedule that is sufficiently protected against the anticipated disruptions that may occur during project execution and that exhibits acceptable makespan performance. We illustrate the application of the methodology on a real life construction project and demonstrate that our proactive scheduler generates baseline schedules that outperform the schedules generated by commercial software packages in terms of robustness and timely project completion probability.     

Risk Management in the Construction Industry Using Combined Fuzzy FMEA and Fuzzy AHP

Failure mode and effect analysis (FMEA) is recognized as one of the most beneficial techniques in reliability programs. FMEA is a structured technique that can help in identifying all failure modes within a system, assessing their impact, and planning for corrective actions. Although this technique has been widely used in many industries, it has some limitations. The purpose of this paper is to extend the application of FMEA to risk management in the construction industry. Fuzzy logic and fuzzy analytical hierarchy process (AHP) are used to address the limitations of traditional FMEA. In essence, this method explores the concept of fuzzy expert systems to map the relationship between impact (I), probability of occurrence (P), and detection/control (D) and the level of criticality of risk events. A case study is presented to validate the concept. The results obtained confirm the capability of fuzzy FMEA and fuzzy AHP to address several drawbacks of the traditional FMEA application. The use of this approach can support the project management team to establish corrective actions in a timely manner.     

Construction Project Risk Assessment Using Existing Database and Project-Specific Information

This paper develops a risk assessment methodology for construction projects by combining existing large quantities of data and project-specific information through updating approaches. Earlier studies have indicated that risk assessment is still difficult for practicing engineers to use due to the requirement of data on too many input variables. However, the availability of existing large quantities of data and project-specific information makes it possible to simplify the risk assessment procedure. Two main ideas are pursued in this paper to facilitate practical implementation: identify and evaluate the critical risk events, and develop a systematic updating methodology. Both epistemic and aleatory types of uncertainties in the data are considered, and corresponding updating procedures are developed. The proposed methodology is illustrated for the construction risk assessment of a cable-stayed bridge.     

Optimal Planning and Scheduling for Repetitive Construction Projects

This paper presents a multiobjective optimization model for the planning and scheduling of repetitive construction projects. The model enables construction planners to generate and evaluate optimal construction plans that minimize project duration and maximize crew work continuity, simultaneously. The computations in the present model are organized in three major modules: scheduling, optimization, and ranking modules. First, the scheduling module uses a resource-driven scheduling algorithm to develop practical schedules for repetitive construction projects. Second, the optimization module utilizes multiobjective genetic algorithms to search for and identify feasible construction plans that establish optimal tradeoffs between project duration and crew work continuity. Third, the ranking module uses multiattribute utility theory to rank the generated plans in order to facilitate the selection and execution of the best overall plan for the project being considered. An application example is analyzed to illustrate the use of the model demonstrate its new capabilities in optimizing the planning and scheduling of repetitive construction projects.     

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.     

Optimization Research: Enhancing the Robustness of Large-Scale Multiobjective Optimization in Construction

Many construction planning problems require optimizing multiple and conflicting project objectives such as minimizing construction time and cost while maximizing safety, quality, and sustainability. To enable the optimization of these construction problems, a number of research studies focused on developing multiobjective optimization algorithms (MOAs). The robustness of these algorithms needs further research to ensure an efficient and effective optimization of large-scale real-life construction problems. This paper presents a review of current research efforts in the field of construction multiobjective optimization and two case studies that illustrate methods for enhancing the robustness of MOAs. The first case study utilizes a multiobjective genetic algorithm (MOGA) and an analytical optimization algorithm to optimize the planning of postdisaster temporary housing projects. The second case study utilizes a MOGA and parallel computing to optimize the planning of construction resource utilization in large-scale infrastructure projects. The paper also presents practical recommendations based on the main findings of the analyzed case studies to enhance the robustness of multiobjective optimization in construction engineering and management.     

Fuzzy Similarity Consensus Model for Early Alignment of Construction Project Teams on the Extent of Their Roles and Responsibilities

A fuzzy similarity consensus (FSC) model is presented for alignment of construction project owner and contractor project teams to their roles and responsibilities, identifying and reducing fundamental problems of conflicts, duplication, and gaps in roles and responsibilities as early as the project initiation stage. The model achieves its objective by incorporating consensus and quality of construction project teams in aggregating their opinions to decide on the party responsible for every standard task of a construction project. The roles and responsibilities of the owner and contractors are described to different extents using seven linguistic terms defined by triangular membership functions and constructed using a three-step Delphi approach, which allows experts to develop common understanding of the meaning of the terms by determining their overlap on a fuzzy linguistic scale. A modified similarity aggregation method (SAM) aggregates experts’ opinions in a linguistic framework using a consensus weight factor for each expert that is based on the similarity of his or her opinion relative to the other experts to ensure that the experts’ final decision is a result of common agreement. A fuzzy expert system (FES) determines an importance weight factor, representing expert quality for each expert; opinions are aggregated using this factor and the consensus weight factor. The FSC model contributes to the construction industry by solving a fundamental problem for project owners who want to identify and reduce potential conflicts between their project teams on the extent of their roles and responsibilities prior to the construction stage. Also, the FSC model provides an improvement over previous consensus-based approaches, which rely on a subjective assessment of experts’ important weights in aggregating their opinions, and it modifies the SAM to adapt it to a linguistic environment.     

Cost and Land Functions for Wastewater Treatment Projects: Typical Simple Linear Regression versus Fuzzy Linear Regression

This paper analyzes cost data pertinent to the municipal wastewater treatment plants (MWTP) in Greece. First, data have been collected with onsite visits and contain the land size necessary for a MWTP, the construction cost, and the operation and maintenance cost of existing wastewater treatment facilities. Second, they come from analytical budgeting of natural wastewater treatment system units. Twelve equations of the form ln?Yi = A0i+A1i?ln?Xi are estimated both with ordinary least squares (OLS) and fuzzy linear regression (FLR). The root mean square error and the mean absolute error are used as fitting measures for the comparison of the OLS with the fuzzy estimations. It is shown that the FLR did produce very similar estimates but slightly inferior to those of OLS in most of the cases for these particular datasets.