• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.338-343
• /
• 2013

Integrated Project Delivery (IPD) as a delivery method fully capitalizes on an integrated project team that takes advantage of the knowledge of all team members to maximize project outcomes. IPD is currently the highest form of collaboration available because all three core project stakeholders, owner, designer and contractor, are aligned to the same purpose. Compared with traditional project delivery approaches such as Design-Bid-Build (DBB), Design-Build (DB), and CM at-Risk, IPD is distinguished in that it eliminates the adversarial nature of the business by encouraging transparency, open communication, honesty and collaboration among all project stakeholders. The team appropriately shares the project risk and reward. Sharing reward is easy, while it is hard to fairly share a failure. So the compensation structure and the contingency in IPD are very different from those in traditional delivery methods and they are expected to encourage motivation, inspiration and creativity of all project stakeholders to achieve project success. This paper investigates the compensation structure in IPD and provides a method to determine the proper level of contingency allocation to reduce the risk of cost overrun. It also proposes a method in which contingency could be used as a functional monetary incentive when established to produce the desired level of collaboration in IPD. Based on the compensation structure scenario discovered, a probabilistic contingency calculation model was created by evaluating the random nature of changes and various risk drivers. The model can be used by the IPD team to forecast the probability of the cost overrun and equip the IPD team with confidence to really enjoy the benefits of collaborative team work.

• Proceedings of the KIEE Conference
• /
• 1999.07c
• /
• pp.1256-1258
• /
• 1999

Electric industry has been the object of major reforms in many countries. These reforms are aimed at attaining efficiency through competition. Thus network companies do not charge transmission cost for line user the same as method at past. This paper presents a transmission cost allocation through reliability differentiated transmission pricing in competitive electric industry. The proposed method considers only the line capacity affecting the reliability of transmission pricing under normal state and contingency state.

• Proceedings of the KIEE Conference
• /
• 1999.11b
• /
• pp.213-215
• /
• 1999

This paper presents a transmission cost allocation through application of a new reliability index in transmission pricing in competitive electric industry. The proposed method allocates a fair use of transmission system charge given separately to individual generator by capacity usage, based on the contribution of individual generator considering N-1 contingency in reliability margin of transmission capacity, and offers more alternatives of pricing in using transmission capacity and transmission margin.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.5
• /
• pp.919-924
• /
• 2009

Geographical location carries information such as demography, local economy, environment, and life styles, which could be the sources of cancer occurrence. Analyzing geographical location associated with cancer occurrence can be instructive to physicians, patients, and health administrators regarding resource allocation, expenditures, prophylaxis and treatments. In this paper, we explored the correspondence relationship between geographical locations and mortality rates of the cancers using correspondence analysis and illustrated the approach with the mortality rates of the top 10 cancers in the 75 counties in Arkansas from 2001 to 2005. Geographical variations with respect to the mortality rates of cancers are evaluated across Arkansas counties. Based on the contingency table, correspondence analysis model is developed and the simple indices which indicate the degree to which the regions and the cancers affect each other are calculated. Quantitative results are visualized and mapped in two-dimensional graphs.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.21 no.3
• /
• pp.11-46
• /
• 1996

The objectives of this research paper are to identify the types of the firm's new product development strategy and their characteristics about business strategy, to examine the effect of each type on new product outcomes, and to explore the contingency variable influencing the relationship between these types and new product outcomes. The result of the research are summarized as follows : First, in terms of both the resource allocation for product innovativeness and technology acquisition method, this study suggests 9 types of the firm's new product development strategies- Type 1 (pursuing low innovative products/relying on external technology), Type 2 (pursuing low innovative products oriented/relying on internal technology), Type 3 (pursuing low innovative products/relying on mixed technology), Type 4 (pursuing high innovative products/relying on internal technology), Type 6(pursuing high innovative products /relying on mixed technology), Type 7 (balancing low and high innovative products/relying on external technology), Type 8 (balancing low and high innovative products/relying on internal technology), Type 9 (balancing low and high innovative products/relying on mixed technology). Second, these 9 types are deeply associated with the firm's business strategic variables such as product differentiation and market differentiation, and exhibit different level of both technical and commercial performance of new products. Finally, the effects of these types on new product outcomes are different according to industrial environment and firms' characteristics with respect to size and technological capability.

• Asia pacific journal of information systems
• /
• v.18 no.2
• /
• pp.95-114
• /
• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.