Abstract-Within the scope of construction supply chain, various uncertainties arising in the processing procedure lead to difficulties and impracticality in structural planning. As a result, consistency in individual job function and standardization of components of the production process, which is also known as "industrialized construction," become the major topic for discussion in the last few years. In order to achieve this objective, prefabrication production adopts the production method used by factory, so as to reduce numerous uncertainties arising during the processing procedure. It also attempts to standardize as many components as possible during the design stage. Under such a production environment, the production speed would be surely increased. However, contractors have to face the discrepancies between the site installation priorities and the factory production priorities. Subsequently, the issue of adopting a quality storage and transportation mechanism to act as a buffer within these processes becomes an important and prominent topic of concern. This study considers the behavioral pattern of the storage and transportation of the prefabrication factory, and then constructs an optimized pattern of prefabrication storage and transportation. Firstly, it proposes a concept of storage zoning to undergo the storage and allocation of every component. It would also consider the storage spaces available in the prefabrication factory, storage area outside the factory and jobsite, as well as the transport relationship among these storage areas. Considering the cost issue, usage cost of respective storage sites and the cost of component transportation would be the primary objectives of planning. Under such circumstances, and together with the strategic application of storage classification and allocation, the influence of different storage zoning strategies towards the whole transportation process is assessed, ensuring the best construction planning for the decision maker.

There are many similar attributes in the behaviors of auction and construction bidding. As studied in economics, it has been widely discussed in game theory. However, game theorists may not be familiar with the subtle differences between auction and construction bidding. As an analytical framework, game theory has the merits of reducing the complication of competitive bidding, but it is still difficult to pursue answers with statistics. Since artificial neural network techniques have been frequently applied to solve ill-structured problems in many areas, they may also be applied to predict tender prices on construction bidding. To do such a prediction, this paper utilizes three methods: statistic, Neural network, and neuro-fuzzy, to compare their results. This study will address the advantages in using artificial intelligence methods.

Advances in information technology (IT) are promoting corporations in various industries to improve their business process. Electronic business is also a current trend for the construction industry. Before adopting more new information technologies in a construction firm, the decision maker should know the firm's present conditions of IT applications, evaluate its real needs of IT, and find its driving force. However, so far there is no specific methodology applied to measure the level of computerization for construction firms. The purpose of this paper is to develop a methodology to measure the level of computerization for construction firms and apply the methodology to present findings in Taiwan construction industry. The findings show that construction firms' computerization can be divided into five levels. The evolution of five levels is related to the organizational characteristics. The factors that drive the construction firms to adopt IT in business process are also evaluated in the paper. The results of the paper will offer guidelines for construction firms to push ahead with their works of computerization, provide references for government organizations to formulate related policies, and supply a basis for software producers to adjust their R&D and marketing strategies.

This study shows the nature of weather risk with a sample project with different networks. The total duration will change by different networks, the starting month, and the different sites. To analyse how to evaluate the risk of weather, by using Mote Carlo simulation and Neural Network to predict potential delay, this study will tackle the risk of weather with proper management skills.