I-Tung Yang

Impact of budget uncertainty on project time-cost tradeoff

Any project has to be supported financially. The budget allocated to the project, however, is subject to uncertainty due to various financial, market, and political risks. The present paper incorporates budget uncertainty into project time-cost tradeoff. The proposed model formulates financial feasibility as a stochastic constraint, transforms it into a deterministic equivalent in the case of normal, beta, or triangular distribution, and solves the equivalent accordingly. The direct result is a minimum time-cost curve, which relates the shortest project duration to different levels of budget. The present study shows that a higher degree of budget uncertainty represents a tighter financial constraint and, thus, needs extra contingency duration. Moreover, if the financial constraint has to be met at a higher probability level, extra contingency costs are necessary to ensure an on-time completion. An actual remodeling project is used to demonstrate the application.

Utility-based decision support system for schedule optimization

The present study quantifies the impact of individual preferences of decision makers on schedule optimization and proposes a decision support system (DSS) to account for the diversity in the time-cost tradeoff analysis. The proposed DSS defines the multiattribute utility function based on subjective assessment of one-dimensional utility functions and scaling factors of time and cost. The multiattribute utility function is subsequently optimized by aid of a new particle swarm optimization algorithm. The application of the proposed DSS is demonstrated through case studies. It has been verified, both statistically and subjectively, that the proposed DSS is effective, efficient, and robust. It has also been shown that the proposed DSS outperforms genetic algorithms. The formulation of the proposed DSS is of practical value because it considers, in addition to direct and indirect costs, the amount of liquidated damages and bonus for early completion. Moreover, the formulation has no restriction on the forms of activity time-cost functions and therefore provides the most flexibility.

Applying the AHP to support the best-value contractor selection – lessons learned from two case studies in Taiwan

Abstract Lately the Best-Value (BV) method for contractor selection has been receiving considerable attention in the public sector in many countries. However, the operations used in performing the BV method often differ due to the various government procurement requirements. Consequently, some of the methods popular in the academic community are not easily incorporated in the BV method in some countries. To enhance the procurement process, this study aims to gain experience by applying the well-known analytical hierarchy process (AHP) to weight the decision criteria for selecting BV contractors of two construction projects in Taiwan. Through these two case studies, this work confirms that the AHP provides a significant benefit for considering the individual preferences of all decision-makers when weighting the criteria. However, this study finds two major potential obstacles, the legal requirements associated with using the AHP and the time it takes to implement the AHP. To overcome these obstacles, this ...

Multiobjective optimization for manpower assignment in consulting engineering firms

In this article, a new multiobjective optimization model, MUST, is proposed to facilitate the staff-to-job assignment in consulting engineering firms. In addition to the typical objective of maximizing profits, other human resource related objectives are also incorporated to balance workloads, avoid excessive overtime, and eliminate demoralizing idleness while giving preference to projects with specified priorities. The present optimization problem is of significant complexity (nonlinear, non-smooth, and combinatorial) and has been proved NP- and #P-complete. To handle all the difficulties, MUST incorporates a particle swarm optimization algorithm to approximate the tradeoff surface consisting of non-dominated solutions. The application of MUST is demonstrated through a numerical case of assigning six engineering teams to fifteen incoming projects. It has been shown that non-dominated solutions generated by MUST help decision makers choose the compromised assignment plan which is otherwise hard and time-consuming to obtain. The comparisons with SPEA2 and LINGO verify the effectiveness and efficiency of MUST.

Scheduling system with focus on practical concerns in repetitive projects

The line of balance (LOB) method has long been used to model construction projects with repetitive units. Critics, however, indicate two major shortcomings of applying LOB in the construction industry: (1) it has not yet been adapted to numerical computation as readily as network methods; and (2) it relies on restrictive assumptions and therefore cannot treat the practical concerns concluded in this paper. To treat all the practical concerns and provide necessary calculation power, a new scheduling system is proposed: the Repetitive Scheduling Method (RSM) and its computerized implementation, Repetitive Project Planner (RP2). RSM includes necessary modelling elements (i.e. activity and relationship types) and a set of computational algorithm to calculate the start time of every activity as well as the minimum project duration. RP2 automatically calculates and generates RSM diagrams that are particularly useful in serving as a test‐bed for project managers to perform what‐if analyses for different crew utilization strategies. A real‐life pipeline project is used to demonstrate the application of RP2 and to compare that with the critical path method (CPM) and traditional LOB models.

Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization

Reliability-based design optimization (RBDO) is concerned with designing an engineering system to minimize a cost function subject to the reliability requirement that failure probability should not exceed a threshold. Conventional RBDO methods are less than satisfactory in dealing with discrete design parameters and complex limit state functions (nonlinear and non-differentiable). Methods that are flexible enough to address the concerns above, however, come at a high computational cost. To enhance computational efficiency without sacrificing model flexibility, we propose a new RBDO framework: PS2, which combines Particle Swarm Optimization (PSO), Support Vector Machine (SVM), and Subset Simulation (SS). SS can efficiently estimate small failure probabilities, based on which SVM is adopted to evaluate the reliability of candidate solutions using binary classification. PSO is employed to solve the discrete optimization problem. Primary emphasis is placed upon the cooperation between SVM and PSO. The cooperation is mutually beneficial since the SVM classifier helps PSO evaluate the feasibility of solutions with high efficiency while the optimal solutions obtained by PSO assist in retraining the SVM classifier to attain better accuracy. The PS2 framework is implemented to find the optimal design of a ten-bar truss, whose component sizes are selected from a commercial standard. The reliability constraints are non-differentiable with two failure modes: yield stress and buckling stress. The interactive process between PSO and SVM contributes greatly to the success of the PS2 framework. It is shown that in various trials the PS2 framework consistently outperforms both the double-loop and single-loop approaches in terms of computational efficiency, solution quality, and model flexibility.

Using Gaussian copula to simulate repetitive projects

An important requirement for simulating repetitive projects is to treat correlations inherent in the repetition of same crews working at various locations. To attain the requirement, this study develops a new Monte Carlo simulation model implementing a Gaussian copula in conjunction with the inverse‐transform method to generate correlated duration samples in repetitive projects that have pre‐specified marginal distributions and pairwise rank correlations. The proposed model is equipped with an automatic approximation procedure to adjust an infeasible correlation matrix, if necessary. The proposed model is statistically verified through a real‐life residential apartment project. The simulation results are compared to two conventional analyses (PERT and simulation without correlation) to show the aggregated impact of correlations. The proposed model contributes to the state‐of‐the‐art in handling non‐linear dependencies among activity durations that may have non‐normal distributions. Moreover, it is flexible in the ways of correlation assessments (qualitative or quantitative), the magnitudes of correlations (weak to strong), and the types of marginal distributions (symmetrical or skewed).

Developing as-built BIM model process management system for general contractors: A case study

AbstractAs the application of building information modeling (BIM) becomes more common, BIM model management becomes necessary and important to enhance the effectiveness of BIM implementation for a general contractor (GC) during the construction phase. Implementation of BIM models in the construction phase, which typically involves participants from different fields, often has numerous BIM-related interfaces and problems. Therefore, feedback from the most recent as-built model tracking and BIM model problems are essential for BIM process management (BIM PM) in the construction field. BIM PM has become the most important aspect of a sound as-built BIM model management strategy. Changes in as-built BIM models should be identified, updated, and tracked for BIM PM, such that version management of as-built BIM models can be improved, while minimizing rework and reducing as-built BIM models development duration. Systematic approaches and information on management platforms for enhancing BIM PM during the constru...

Repetitive Scheduling Method: Requirements, Modeling, and Implementation

AbstractProjects with repeating activities, such as multistory buildings and highways, present unique scheduling challenges. This paper presents the necessary requirements that scheduling systems should address to meet the needs and complexities of repetitive projects in practice. The repetitive scheduling method (RSM) provides a unified scheduling framework that applies to all types of repetitive projects. In RSM, projects are classified as discrete (vertical) or continuous (horizontal or linear) and then as uniform and nonuniform, depending on whether progress is measured using a counting scale or a dominant-attribute scale. RSM modeling elements include three types of activities (line, block, and bar) and 10 types of relationships (links) between activities. RP2, a computer program developed by the authors to validate the proposed scheduling approach, is used to develop the production diagrams and tabular reports of the target schedule for a complex of four-story apartment buildings for a low-income ho...

Use of Support Vector Regression to Improve Computational Efficiency of Stochastic Time-Cost Trade-Off

AbstractStochastic time-cost trade-off has been a popular object of investigation in past decades because there are uncertain factors that can be considered when determining the appropriate trade-off between project completion time and cost. Previous studies, however, have implemented a double loop procedure, which performs optimization in the outer loop and simulation in the inner loop. The double loop procedure is ponderous because it requires an unacceptably long computation time (taking hours or days), even for a small to medium project. The present study proposes an integrated system that converts the double loop to single loops,thereby dramatically reducing computation time. This is done by incorporating a support vector regression model to obtain a decision function, which will be used to replace the time-consuming Monte Carlo simulation to evaluate the objective function values for individual solutions. With the objective function values, a multiobjective particle swarm optimization algorithm is d...