Project Delivery System Selection under Uncertainty: Multicriteria Multilevel Decision Aid Model

Selecting an optimal project delivery system is a critical task that owners should do to ensure project success. This selection is a complex decision-making process. The complexity arises from the uncertain or not well-defined parameters and/or the multiple criteria structure of such decisions. In this study, a decision aid model using the analytical hierarchy process (AHP) coupled with rough approximation concepts is developed to assist the owners. The selection criteria are determined by studying a number of benchmarks. The model ranks the alternative delivery systems by considering both benchmark results and owner’s opinion. In interval AHP, an optimization procedure is performed via obtaining the upper and the lower linear programming models to determine the interval priorities for alternative project delivery systems. In cases having incomparable alternatives, which is the most likely case in uncertain decision making, the model uses rough set-based measures to reduce the number of decision criteria to a subset, which is able to fully rank the alternatives. To illustrate the applicability and usefulness of this methodology, a real world case study will be demonstrated.     

Performance Objectives Selection Model in Public-Private Partnership Projects Based on the Perspective of Stakeholders

Over the years, public-private partnership (PPP) has been acknowledged by many as an innovative approach to the procurement of public projects. The desire for more efficient and effective PPP projects renders the performance management to be increasingly important, in which the influence of the stakeholders must be considered. To implement complete and effective performance management in PPP projects, 15 performance objective attributes are proposed based on the perspectives of different stakeholders. A structured questionnaire survey was conducted to investigate the relative significance of each attribute in four stakeholder groups. According to the survey results, the objective attributes are all important. Integrating all stakeholders’ benefits and selecting the appropriate qualitative level of performance objective in the process of decision making are two particularly important problems because of stakeholders’ different preferences. To resolve these problems, a fuzzy entropy method and a fuzzy TOPSIS method based on projection distance have been developed to calculate the final decision weights in all stakeholder groups and select appropriate performance objective levels for PPP projects, respectively. The final decision weights are obtained using fuzzy entropy to integrate the experiences and knowledge of all stakeholders. An illustrative case study on the Beijing National Stadium project for the 2008 Olympic Games is used to demonstrate the feasibility and practicability of the proposed model.     

Improving Objectivity in Procurement Selection

Selecting a procurement strategy for construction projects is inherently subjective. The decision is often intuitively based on the previous experience and knowledge of the decision maker. An objective-subjective procurement selection method is proposed. The method takes into account the need to have an objective base for strategy comparison through the use of multiattribute utility technology. Allowing the decision maker to assign importance ratings for the selection criteria also accommodates individual preferences and project specificities. The analytical hierarchical process based importance ratings assignment method improves both the objectivity and the reliability of the assessment. Applications in real cases suggest positive potential use of the method.     

Relative Effectiveness of Project Delivery and Contract Strategies

Project delivery systems define the roles and responsibilities of the parties involved in a project. They also establish an execution framework in terms of sequencing of design, procurement, and construction. The decision made in the selection of a project delivery system for a project impacts all phases of execution of the project and greatly impacts the efficiency of project execution. Such decisions should be facilitated by thorough analysis. Structured, quantitative decision analysis processes have been shown to have several benefits over the simplistic, holistic, and informal processes that typically characterize subjective evaluations. However, a dearth of quantitative values of project delivery systems established and validated through research has invariably left project managers with no alternative than to make project delivery selection decisions on the basis of subjective evaluations. Development of the needed quantitative values for application in a decision analysis process would greatly enhance the quality of the decision-making process and provide a defensible rationale for selection of project delivery systems for capital projects. This paper presents research findings that provide the needed quantitative values in this area. Based on the quantitative values defined here, interested parties can develop and implement quantitative evaluation of project delivery alternatives to identify the optimal solution for a given project. Multicriteria decision analysis was found to be the suitable approach for a quantitative, analytical evaluation of project delivery systems. Consequently, the quantitative values presented in this paper were developed in accordance with the requirements of the multicriteria decision analysis technique known as simple multiattribute rating technique with swing weights (SMARTS). Utilizing the quantitative values presented here and applying the analysis technique of SMARTS, a decision support tool has been developed and validated for the Construction Industry Institute. The decision support tool is presently being utilized by member companies of the Construction Industry Institute that were privy to its development. With the presentation of the quantitative values in this paper, other parties interested in developing similar tools would benefit from the research results presented here.     

Project Manager’s Decision Aid for a Radical Project Cycle Reduction

Currently there are no formal decision tools or guidelines to assist owners and project managers in choosing delivery systems and project strategies that would allow significant reductions in the project cycle time. The development of a decision aid that would allow a project manager to prioritize and apply project cycle reduction techniques would be a valuable tool for achieving project cycle time reduction in projects. This paper presents a new high performance project delivery system called “project manager’s game planner” (PMGP) designed to assist a decision maker in identifying and utilizing an optimal set of radical reduction techniques (RRTs) with the greatest potential for success in achieving cycle time reduction. Additionally, the PMGP can assist the user in identifying the top RRTs during any of the five project phases: preproject planning, design, material management, construction, and start-up. Most projects can utilize this PMGP to improve the project performance whether to achieve significant cycle time reduction or to simply achieve effective project execution.     

BOT Viability Model for Large-Scale Infrastructure Projects

The key to a successful implementation of a build-operate-transfer (BOT) infrastructure project is in-depth analysis of all aspects related to economic, environmental, social, political, legal, and financial feasibility of the project. For these reasons, the analysis of the project feasibility decision needs a technique to include the qualitative decision factors that have a strong impact on the project. This paper aims to introduce a decomposed evaluation model developed to assess the most common significant decision factors that strongly affect the feasibility of BOT projects. The paper describes the viability decision factors that were identified and screened with the assistance of a group of industry experts. This analysis yielded 21 significant factors that would have a certain impact on the feasibility of any BOT project. These factors were classified into three relative categories forming the structure of the suggested project viability model. This model presents a new approach, based on the analytical hierarchy process technique, to evaluate the relationships between decision factors related to project feasibility determination. The new approach has been validated by information obtained from three case studies of BOT projects. The proposed approach to project feasibility evaluation aims to increase the decision maker’s ability to determine the factors contributing the most to the viability to the BOT project at hand.     

Site Layout Planning using Nonstructural Fuzzy Decision Support System

Site layout planning can affect productivity and is crucial to project success. However, as construction is heterogeneous in the nature of its organizations, project designs, time constraints, environmental effects, etc., site layout planning for each project becomes unique. Affected by many uncertainties (variables) and variations, site layout planning is a typical multiobjective problem. To facilitate the decision-making process for these problems, a nonstructural fuzzy decision support system (NSFDSS) is proposed. NSFDSS integrates both experts’ judgment and computer decision modeling, making it suitable for the appraisal of complicated construction problems. The system allows assessments based on pairwise comparisons of alternatives using semantic operators that can provide a reliable assessment result even under the condition of insufficient precise information.     

Neurofuzzy Genetic System for Selection of Construction Project Managers

Choosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions.     

Monte Carlo Simulation Approach to Support Alliance Team Selection

The alliance concept is similar to the design build project delivery system. However, it is denoted by a special form of partnership between the owner and the design-build team, where the owner is very involved in the project. This type of delivery systems is gaining popularity as many infrastructure projects require the owner to order materials ahead of time, before engaging the design-build team in the project. As in design-build, the selection of the engineer-procure-construct team depends not only on the price but also on qualitative factors. This paper lays out the framework that facilitates selecting the best alliance team for a project by quantifying the evaluation factors and combining them into a single score. Using a Monte Carlo simulation and varying all the factors relevant to the decision problem can reveal biases present in the evaluation to assist in making the best possible decision. A case study dealing with a large utility project illustrates this methodology.     

Neurofuzzy Decision Support System for Efficient Risk Allocation in Public-Private Partnership Infrastructure Projects

The performance of public-private partnership (PPP) infrastructure projects is largely contingent on whether the adopted risk allocation (RA) strategy is efficient. Theoretical frameworks drawing on the transaction cost economics and the resource-based view of organizational capability are able to explain the underlying mechanism but unable to accurately forecast efficient RA strategies. In this paper, a neurofuzzy decision support system (NFDSS) was developed to assist in the RA decision-making process in PPP projects. By combining fuzzy and neural network techniques, a synthesized fuzzy inference system was established and taken as the core component of the NFDSS. Evaluation results show that the NFDSS can forecast efficient RA strategies for PPP infrastructure projects at a highly accurate and effective level. A real PPP infrastructure project is used to demonstrate the NFDSS and its practical significance.     

Selection of Project Delivery Method in Transit: Drivers and Objectives

This paper describes the results of research on alternative project delivery methods in transit projects in the United States. The research, sponsored by the Transit Cooperative Research Program, aimed to identify those factors that drive the decision in the choice of project delivery method. A rigorous case study analysis based on on-site structured interviews with the directors of several transit projects was used to identify decision drivers and the rationale behind the delivery method selection decision in transit agencies. The nine case studies conducted in this research represent a cross section of delivery methods, including design-bid-build/multiprime, construction manager-at-risk, design/build, and design/build-operate-maintain. The interviewees agreed that the use of alternative delivery methods have resulted in savings in schedule and cost for transit agencies. The research also found that achieving aggressive schedule compression is the most influential factor when selecting alternative delivery methods. Also, implementation of a formal risk analysis as part of the project development process appears to improve the project’s chances of meeting budget and schedule objectives.     

Decision Support System for Monthly Operation of Hydropower Reservoirs: A Case Study

This paper presents a decision support system for multipurpose reservoir operation. The mathematical models in the system are formulated for monthly operation of hydropower reservoirs. The key components of the system are four main modules: database management, inflow modeling and forecasting, operation management, and real-time operation. Flexibility is the key feature of the system, providing the users with different decision tools and different indices for measuring the performance of each tool. A cost function is developed based on the present value of the total capital cost and the cost of operation and maintenance of the system. This cost function, which is developed based on “reasonable” estimates of water and energy prices, is used to measure the performance of reservoir operation policies. A utility function based on multicriterion decision making (MCDM) that uses an analytical hierarchy process is also developed. The MCDM utility function enables decision makers to incorporate the priority of different objectives in developing optimal operating policies and can be effectively used when the priority of objectives is not clear and the decision-making process relies mainly on the decision maker’s preferences. Both economic and MCDM utility functions are implemented and coupled with deterministic and stochastic optimization models. The decision support system (DSS) is applied to the largest surface water resources system in Iran, namely, the Dez and Karoon river-reservoir system. The results of the case study have shown that the DSS has been able to significantly increase the long-term power generation of the system while satisfying water demands for different purposes.     

Dynamic Prediction of Project Success Using Artificial Intelligence

The purpose of construction management is to successfully accomplish projects, which requires a continuous monitoring and control procedure. To dynamically predict project success, this research proposes an evolutionary project success prediction model (EPSPM). The model is developed based on a hybrid approach that fuses genetic algorithms (GAs), fuzzy logic (FL), and neural networks (NNs). In EPSPM, GAs are primarily used for optimization, FL for approximate reasoning, and NNs for input-output mapping. Furthermore, the model integrates the process of continuous assessment of project performance to dynamically select factors that influence project success. The validation results show that the proposed EPSPM, driven by a hybrid artificial intelligence technique, could be used as an intelligent decision support system, for project managers, to control projects in a real time base.     

Fuzzy Preference Relations Consensus Approach to Reduce Conflicts on Shared Responsibilities in the Owner Managing Contractor Delivery System

This paper proposes a fuzzy preference relations consensus (FPRC) approach that helps owners and contractors reach consensus on their responsibilities and reduce conflicts in shared tasks. A fuzzy similarity consensus (FSC) model was developed to aggregate experts’ opinions on roles and responsibilities in the owner managing contractor (OMC) project delivery system. The FSC model categorized 324 generic OMC tasks into three responsibility task lists: owner, contractor, and shared. In a consensus-reaching process, the FPRC approach is applied to shared tasks, where expert opinions on responsibility conflict are expressed, to achieve an aggregated responsibility decision for each task. Experts compare the three responsibility alternatives in pairs by using linguistic preferences, defined on a fuzzy preference scale, to select a preferred responsibility alternative for each of the conflicting tasks. A computed linguistic consensus degree guides the experts on their level of consensus in every round of the process. The quality of experts is defined with a fuzzy expert system–determined importance weight factor for each expert. The FPRC approach is relevant to the construction industry, as it incorporates consistency in decision making by allowing experts to measure and reach an adequate level of consensus linguistically when deciding on responsibilities. The proposed approach provides a method of reducing conflicts in the assignment of task responsibility between the owner and its contractors as early as the project initiation phase; thus, the project teams can concentrate on the work to be done rather than deal with responsibility conflicts during project execution.     

Entropy Application to Improve Construction Finance Decisions

In financial decision-making processes, the adopted weights of the objective functions have significant impacts on the final decision outcome. However, conventional rating and weighting methods exhibit difficulty in deriving appropriate weights for complex decision-making problems with imprecise information. Entropy is a quantitative measure of uncertainty and has been useful in exploring weights of attributes in decision making. A fuzzy and entropy-based mathematical approach is employed to solve the weighting problem of the objective functions in an overall cash-flow model. The multiproject being undertaken by a medium-size construction firm in Hong Kong was used as a real case study to demonstrate the application of entropy. Its application in multiproject cash flow situations is demonstrated. The results indicate that the overall before-tax profit was HK$ 0.11 millions lower after the introduction of appropriate weights. In addition, the best time to invest in new projects arising from positive cash flow was identified to be two working months earlier than the nonweight system.     

Developing a Fuzzy Risk Allocation Model for PPP Projects in China

Equitable allocation of risks between the government and the private sector in concession agreement is essential to the success of public-private partnership (PPP) projects. The decision-making process, based on the established risk allocation principles expressed in linguistic terms, requires qualitative judgment and experiential knowledge of construction experts. However, it is subjective, partial, and implicit in actual application. This paper aims to develop a fuzzy synthetic evaluation model for determining an equitable risk allocation between the government and the private sector. By doing so, it assists the PPP project practitioners to transform the risk allocation principles in linguistic terms into a more usable and systematic quantitative-based analysis using fuzzy set. Twenty-three principles and influencing factors for risk allocation were identified through a comprehensive literature review. Nine critical risk allocation criteria (RACs) that evaluate the risk carrying capability of project participants were further identified, validated, and compiled based on the experts’ knowledge via face-to-face interviews. On the other hand, the weighting for each critical risk allocation criterion was determined through a two-round Delphi questionnaire survey. A set of knowledge-based fuzzy inference rules was then established to set up the membership function for the nine RACs. Based on the research findings, a fuzzy synthetic evaluation model was finally established to determine an equitable risk allocation between the government and the private sector.     

A Fuzzy Decision Framework for Contractor Selection

Contractor selection is the process of selecting the most appropriate contractor to deliver the project as specified so that the achievement of the best value for money is ensured. Construction clients are becoming more aware of the fact that selection of a contractor based on tender price alone is quite risky and may lead to the failure of the project in terms of time delay and poor quality standards. Evaluation of contractors based on multiple criteria is, therefore, becoming more popular. Contractor selection in a multicriteria environment is, in essence, largely dependent on the uncertainty inherent in the nature of construction projects and subjective judgment of decision makers (DMs). This paper presents a systematic procedure based on fuzzy set theory to evaluate the capability of a contractor to deliver the project as per the owner’s requirements. The notion of Shapley value is used to determine the global value or relative importance of each criterion in accomplishing the overall objective of the decision-making process. The research reported upon forms part of a larger study that aims to develop a fuzzy decision model for construction contractor selection involving investigating multiple criteria selection tendencies of construction clients, relationship among decision criteria, and construction clients’ preferences of criteria in the contractor selection process. An illustration with a bid evaluation exercise is presented to demonstrate the data requirements and the application of the method in selecting the most appropriate contractor for the project under uncertainty. The proposed model is not intended to supplant the work of decision-making teams in the contractor selection process, but rather to help them make quality evaluations of the available candidate contractors. One major advantage of the proposed method is that it makes the selection process more systematic and realistic as the use of fuzzy set theory allows the DMs to express their assessment of contractors’ performance on decision criteria in linguistic terms rather than as crisp values.     

Making career decisions: A sequential elimination approach.

Presents a model for career decision making based on the elimination of occupational alternatives, which is an adaptation for career decisions of A. Tversky's (see record 1973-00249-001) elimination-by-aspects theory of choice. Each occupational alternative is viewed as a set of aspects, and at each stage an aspect is selected according to its importance; alternatives lacking the selected aspect are eliminated. This process continues until only a few alternatives, or a single alternative, remain. The expected utility approach is reviewed as a representative compensatory model for career decisions. The advantages and the disadvantages of the sequential elimination and the expected utility approaches are examined. The possible combination of the 2 approaches is explored, and the implications for career counseling, computerized career decision-making systems, and research issues are discussed. (53 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Entropic Risk Analysis by a High Level Decision Support System for Construction SMEs

The method of entropy has been useful in evaluating inconsistency on human judgments. This paper illustrates an entropy-based decision support system called e-FDSS to the solution of multicriterion risk and decision analysis in projects of construction small and medium enterprises (SMEs). It is optimized and solved by fuzzy logic, entropy, and genetic algorithms. A case study demonstrated the use of entropy in e-FDSS on analyzing multiple risk criteria in the predevelopment stage of SME projects. Survey data studying the degree of impact of selected project risk criteria on different projects were input into the system in order to evaluate the preidentified project risks in an impartial environment. Without taking into account the amount of uncertainty embedded in the evaluation process; the results showed that all decision vectors are indeed full of bias and the deviations of decisions are finally quantified providing a more objective decision and risk assessment profile to the stakeholders of projects in order to search and screen the most profitable projects.     

Fuzzy Approach to Prequalifying Construction Contractors

Construction contractor prequalification (CCPQ) is a crucial decision making process to select capable potential bidders and ensure the success of construction projects. The purpose of CCPQ is to guarantee a contractor’s characteristic to meet the construction project’s requirements, which has been established worldwide as a standard practice. However, existing methods, i.e., marking method, subjective judgment method, etc., for contractor prequalification have been inadequate because it is difficult for decision makers to investigate contractor’s capabilities against inexact, vagueness, and qualitative criteria. The objective of this paper is to propose a fuzzy framework-based fuzzy number theory to solve construction contractor prequalification issues, which include decision criteria analysis, weights assessment, and decision model development. Finally, a case study for a tunnel construction project was used to demonstrate the feasibility of fuzzy approaches.