Wei-Chih Wang

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

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 ac- curate cost estimate due to the great amount of uncer- tain cost items. This study proposes a new model for estimating semiconductor hookup construction project costs. The developed model, called FALCON-COST, in- tegrates the component ratios method, fuzzy adaptive learning control network (FALCON), fast messy ge- netic algorithm (fmGA), and three-point cost estimation method to systematically deal with a cost-estimating en- vironment involving limited and uncertain data. In ad- dition, the proposed model improves the current FAL- ∗ To whom correspondence should be addressed. E-mail: weichih@ mail.nctu.edu.tw. CON 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 approx- imately 22.74%, 23.08%, and 21.95% more accurate than the conventional average cost method, component ratios method, and modified FALCON-COST method, respec- tively. Providing project managers with reliable cost esti- mates is essential for effectively controlling project costs.

Integration of Simulation-Based Cost Model and Multi-Criteria Evaluation Model for Bid Price Decisions

Several criteria affect bidding decisions. Cur- rent bidding models determine a markup based on a fixed project construction cost. This work presents a novel bid price determination procedure that is built by integrating a simulation-based cost model and a multi-criteria eval- uation model. The cost model is used to consider cost uncertainties and generate a bid price cumulative distri- bution, whereas the multi-criteria evaluation model ap- plies pairwise comparisons and fuzzy integrals to reflect bidder preferences regarding decision criteria. The rela- tionship between the two models is based on a practi- cal phenomenon in that a bidder has a high probability of winning when criteria evaluations favor his bid, and, consequently, the bidder would bid a low price, and vice versa. The merits of the proposed procedure are demon- strated by its application to two construction projects in Taiwan.

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 ...

Supporting project cost threshold decisions via a mathematical cost model

Abstract A project cost threshold must be determined in a relatively short time by a project owner as a reference for evaluating competitive bids. In practice, such a decision is mainly based on subjective experience. This work presents a novel systematic procedure for assessing a reasonable project cost threshold. The proposed procedure involves a utility-based multi-criteria evaluation model and a cost model. The multi-criteria evaluation model is applied to accurately reflect an owners preferences in relation to the decision criteria, as the cost model is derived to generate a cumulative cost distribution to set boundaries on the cost threshold. The proposed cost model potentially saves the computer time and coding effort that required by a simulation-based model.

Simulation‐Facilitated Model for Assessing Cost Correlations

Project cost becomes increasingly variable if many cost items for a construction project are correlated, and this can increase the uncertainty of completing a project within a target budget. This workpresents a factor- based computer simulation model (COSTCOR) for evalu- ating project costs given correlations among cost items. Uncertainty in the total cost distribution of an item (grand- parent) is transferred to several factor cost distributions (parents) according to qualitative estimates of the sensitiv- ity of each cost item to each factor. Each cost distribution is then decomposed further into a family of distributions (chil- dren; costs given factor conditions), with each child corre- sponding to a factor condition. Correlations are retrieved by sampling from the child distributions with the same con- dition for a given iteration of the simulation. COSTCOR integrates the uncertainty effects caused by each factor at the project cost level, thus making it easier for manage- ment to determine what parts of the project need to be controlled.

Applying cluster identification algorithm and simulation to generate probabilistic network schedules for design projects

Scheduling of a design project is complex because design activities often have information dependencies between each other. This study proposes a network‐based model to schedule design projects and generate probabilistic project durations. The proposed model applies a modified cluster identification algorithm to evaluate information dependencies between design activities to facilitate the establishment of a schedule network (and regroup activities to support the assignment of design work); it also uses a simulation approach to incorporate the effect on duration of the uncertain number of design iterations. The model is implemented in four stages, which are breaking down the design work; evaluating the dependencies; identifying concurrent activities; and estimating the durations of activities and simulating the duration of design project. The advantages of the proposed model are demonstrated through its application to an example project, which was reviewed by industrial practitioners. Practitioners felt that the generated detailed scheduling data could help them to control their design work more precisely than a bar chart. Additionally, the simulated probabilistic project duration provided them with an awareness of the risk involved in meeting the contractual deadline.

Application of importance-satisfaction analysis and influence-relations map to evaluate design delay factors

AbstractDesign delays can negatively influence the total completion time of a facility construction project. Knowing the factors to which design delays are most sensitive supports the time management of designs. However, factors that cause design delays are several and interrelated. This study proposes a new model to identify key factors that drive design delays. The core of the model integrates importance-satisfaction analysis (ISA) and an influence-relations map (IRM). The ISA evaluates the performance of each delay factor, while the IRM captures the causal relationships among factors. Additionally, the IRM is generated using a decision making trial and evaluation laboratory technique (DEMATEL). The model is applied to a real-world high-tech facility construction project to indicate the strengths of the model. In this investigation, four first-level delay factors and 17 second-level delay sub-factors are derived. The factor of “organizations decision making and budget constraints” is identified as the ...

A factor-based probabilistic cost model to support bid-price estimation

An appropriate bid price is essential for winning a construction project contract. However, making an accurate cost estimate is both time-consuming and expensive. Thus, a method that does not take much time and can approximate a proper bid price can help a contractor in making bid-price decisions when the available bid-estimation time is insufficient. Such a method can also generate a target cost and provide a cross-check for their bid prices that were estimated using a detailed process. This study proposes a novel model for quickly making a bid-price estimation that integrates a probabilistic cost sub-model and a multi-factor evaluation sub-model. The cost sub-model, which is simulation-based, focuses on the cost divisions to save estimation time. At the same time, the multi-factor evaluation sub-model captures the specific factors affecting the cost of each cost division. The advantages of the proposed model are demonstrated by its application to three residential housing projects located in northern Taiwan. The steps for applying this model to other contractors are also provided.

Using data envelopment analysis to support best-value contractor selection

AbstractSelecting an appropriate contractor or supplier is essential to the successful implementation of a public procurement project. The Taiwan government frequently applies the best-value (BV) tendering method, a multi-criteria evaluation method, to procure projects. However, the selection process of the winner for a BV-based procurement project is generally subjective and thus is easily accused of corruptions. To develop a systematic method to support contractor selection, this study proposes using the Data Envelopment Analysis (DEA) to facilitate the criteria evaluations for each bidder during the short-listing stage. The evaluation results of using the DEA are a list of potential BV winners who are then suggested to enter into the final selection stage. Based on three case studies related to service procurement projects, this research finds that the DEA is suitable of assessing the relative efficiencies among bidders when the BV approach is applied. Lessons learned here should be helpful in applying...

Conceptual cost estimations using neuro-fuzzy and multi-factor evaluation methods for building projects

AbstractDuring the conceptual phase of a construction project, numerous uncertainties make accurate cost estimation challenging. This work develops a new model to calculate conceptual costs of building projects for effective cost control. The proposed model integrates four mathematical techniques (sub-models), namely, (1) the component ratios sub-model, fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm (fmGA) based sub-model, (2) regression sub-model, and (4) multi-factor evaluation sub-model. While the FALCON- and fmGA-based sub-model trains the historical cost data, three other sub-models assess the inputs systematically to estimate the cost of a new project. This study also closely examines the behavior of the proposed model by evaluating two modified models without considering fmGA and undertaking sensitivity analysis. Evaluation results indicate that, with the ability to more thoroughly respond to the project characteristics, the proposed model has a high probability o...