Recruitment and selection processes through an effective GDSS

[[abstract]]This study proposes a group decision support system (GDSS), with multiple criteria to assist in recruitment and selection (R&S) processes of human resources. A two-phase decision-making procedure is first suggested; various techniques involving multiple criteria and group participation are then defined corresponding to each step in the procedure. A wide scope of personnel characteristics is evaluated, and the concept of consensus is enhanced. The procedure recommended herein is expected to be more effective than traditional approaches. In addition, the procedure is implemented on a network-based PC system with web interfaces to support the R&S activities. In the final stage, key personnel at a human resources department of a chemical company in southern Taiwan authenticated the feasibility of the illustrated example.[[notice]]補正完畢[[journaltype]]國內[[incitationindex]]SCI[[incitationindex]]E

A business strategy selection of green supply chain management via an analytic network process

This study designates green supply chain management (GSCM) strategies to effectively direct business functions and activities in the electronics industry. Enterprises conduct environmental scanning to understand the external environment and internal functions; a successful strategy identifies unique firm-owned resources and transforms them into capabilities. This study proposes a network to clarify managerial levels and firm-related content. It derives four business functions from product lifecycle management: design, purchasing, manufacturing, and marketing and service—and associates their related activities with “greenness”. These functions and activities are a network’s clusters and elements in an analytic network process (ANP) model with dependent relations. A detailed procedure solves complex GSCM strategy-selection problems and evaluates the most important activity in each business function. A case study takes a leading Taiwanese electronics company to identify the proposed procedure’s stability.[[abstract]]This study designates green supply chain management (GSCM) strategies to effectively direct business functions and activities in the electronics industry. Enterprises conduct environmental scanning to understand the external environment and internal functions; a successful strategy identifies unique firm-owned resources and transforms them into capabilities. This study proposes a network to clarify managerial levels and firm-related content. It derives four business functions from product lifecycle management: design, purchasing, manufacturing, and marketing and service—and associates their related activities with “greenness”. These functions and activities are a network’s clusters and elements in an analytic network process (ANP) model with dependent relations. A detailed procedure solves complex GSCM strategy-selection problems and evaluates the most important activity in each business function. A case study takes a leading Taiwanese electronics company to identify the proposed procedure’s stability.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]電子

Using extended hazard regression model to assess the probability of aviation event

[[abstract]]Flight safety has always been the major attention subject in civil aviation in view of the rapid and continuous growth in air transportation traffic volume. The Airline Safety Assessment System, which is currently under development by the Taiwan Civil Aeronautics Administration (CAA), will contain indicators of airline safety performance that can identify potential problem areas for inspectors. The objective of this research is to develop an analytic method that uses data on both accident and performance measures to analyze potential aviation event. The extended hazard regression (EHR) model was conducted to assess the probability of aviation event and analyze how the probability may be affected by airline safety performance. We investigated and demonstrated its applicability in a practical case study.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[booktype]]紙本[[countrycodes]]US

A data mining approach to discovering reliable sequential patterns

[[abstract]]Sequential pattern mining is a data mining method for obtaining frequent sequential patterns in a sequential database. Conventional sequence data mining methods could be divided into two categories: Apriori-like methods and pattern growth methods. In a sequential pattern, probability of time between two adjacent events could provide valuable information for decision-makers. As far as we know, there has been no methodology developed to extract this probability in the sequential pattern mining process. We extend the PrefixSpan algorithm and propose a new sequential pattern mining approach: P-PrefixSpan. Besides minimum support-count constraint, this approach imposes minimum time-probability constraint, so that fewer but more reliable patterns will be obtained. P-PrefixSpan is compared with PrefixSpan in terms of number of patterns obtained and execution efficiency. Our experimental results show that P-PrefixSpan is an efficient and scalable method for sequential pattern mining.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]US

ERP System Selection Using Analytic Network Process Model

[[abstract]]This paper illustrates a four-step semi-structured process for ERP system evaluation. To improve the evaluation process, we suggest using Analytic Network Process (ANP) for ERP systems' qualitative review involving multiple criteria and interdependency property. The ANP method is based on the feedback system framework of the well-known Analytic Hierarchy Process. A case study indicates that the evaluated aspects of the method are feasible and improving the quality of ERP system selection compared with traditional approaches.[[sponsorship]]中華企業資源規劃學會; 世新大學; 中央大學「電子商務環境與科技發展應用」卓越計劃辦公室[[conferencetype]]國內[[conferencedate]]20030111~20030111[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]臺北市, 臺

Using Deep Learning Approach in Flight Exceedance Event Analysis

[[abstract]]Causal analysis of flight exceedance events, e.g. hard-landing, is a key task for mod-ern airlines performing Flight Operation Quality Assurance (FOQA) programs. The main objective of the program is to learn from experience: detect early signs of major problems and correct them before accidents occur. It has been found that flare operation would greatly influence the landing performance. According to the finding, we proposed a deep learning approach to assist airlines performing causal analysis for hard landing events. Experimental results confirm that compared with the other state-of-the-art techniques, the proposed approach provides a more reliable results. The technique can be the basis of de-veloping advanced models for further revealing the relationships between pilot operations and flight exceedance events.[[notice]]補正完

Designing a multi-issues negotiation support system based on prospect theory

[[abstract]]Considering a prospect-theory type marginal utility function, the S-shape value function is utilized to encode the preferences of negotiators. This article presents the framework of a purchase negotiation support system. Based on the proposed framework, a prototype system was developed to carry on multi-issue decision problems during the purchase negotiation process. To examine the feasibility of using the value function to represent players’ preferences in the negotiation system, we examined the final agreements from 25 simulated negotiation scenarios using two different preference settings.[[notice]]補正完

COSTS evaluation using modified TOPSIS and ANP

[[abstract]]This paper models the COTS evaluation problem as an MCDM problem and proposes a five-phase COTS selection model, combining the technique of ANP (analytic network process) and modified TOPSIS (technique for order performance by similarity to idea solution). This article discusses using the ANP to determine the relative weights of multiple evaluation criteria. The modified TOPSIS approach is used to rank competing products in terms of their overall performance. To illustrate how the approach is used for the COTS evaluation problem, an empirical study of a real case is conducted. The case study demonstrates the effectiveness and feasibility of the proposed evaluation procedure.[[notice]]補正完畢[[journaltype]]åå¤[[incitationindex]]SCI[[incitationindex]]EI[[ispeerreviewed]]

Discovering time-interval sequential patterns by a pattern growth approach with confidence constraints

[[abstract]]Sequential pattern mining is to discover frequent sequential patterns in a sequence database. The technique is applied to fields such as web click-stream mining, failure forecast, and traf- fic analysis. Conventional sequential pattern-mining approaches generally focus only the orders of items; however, the time interval between two consecutive events can be a valuable information when the time of the occurrence of an event is concerned. This study extends the concept of the well-known pattern growth approach, PrefixSpan algorithm, to propose a novel sequential pattern mining approach for sequential patterns with time intervals. Unlike the other time-interval sequential pattern-mining algorithms, the approach concerns the time for the next event to occur more than the timing information with its precedent events. To obtain a more reliable sequential pattern, a new measure of the confidence of a sequential pattern is defined. Experiments are conducted to evaluate the performance of the proposed approach.[[notice]]補正完

[[alternative]]The 2D C-String Knowledge Representation Based on Decomposed Objects

[[abstract]]以二維C-字串(2D C-string)為基礎之圖形資料結構已被用來表示符號圖(Symbolic picture)。它提供了以非常自然的方式來建立圖示索引(Iconic indexes),而且物件間之空間關係能根據這些字串利用空間推理(Spatial reasoning)的方式而獲得。但這些字串產生之方法皆是根據各物件之最小邊界矩形(Minimum bounding rectangles,MBR)而產生,因此,推論出來之空間關係與真實物件之空間關係存有相當差異。在本論文中,我們將提出一個減少此差異之方法,將較大之物件分解成較小物件再取其MBR而產生二維C-字串,而據此推論出差異較小,較接近真實物件之空間關係。產生此種二維C-字串以及其空間推理之方法將被提出。[[abstract]]The pictorial data structures based on 2D C-strings have been used to represent symbolic pictures. It provides a natural way to construct the iconic indexes for pictures. The spatial relationships between any two objects can be obtained by using spatial reasoning based on these strings. However, these strings are produced according to the minimum bounding rectangles (MBR) of objects. Hence, there is some difference between the inferred spatial relationships and that between the real objects. In this paper, we propose an approach that can reduce the difference. Bigger objects are decomposed into smaller objects first, and then MBRs are generated for the smaller objects. Finally, 2D C-strings are generated and the spatial relationships with less difference can be obtained by spatial reasoning. The methods of producing the 2D C- strings based on decomposed objects and the spatial reasoning according to these strings are proposed.[[sponsorship]]教育部; 元智工學院[[conferencetype]]國內[[conferencedate]]19951221~19951222[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]桃園縣, 臺