MERISE: An information system design and development methodology

MERISE is an Information System Design and Development methodology widely used in France. The framework of MERISE has three cycles: abstraction cycle, approval cycle and life cycle. The abstraction cycle uses the three database levels (conceptual, logical and physical). The approval cycle recognizes the necessity of identifying decision points during the development of the information system. The life cycle consists of (long range) planning, initial study, detailed study, implementation, launching and maintenance. A system of tools has been defined to support these cycles.     

Enabling self-service BI: A methodology and a case study for a model management warehouse

The promise of Self-Service Business Intelligence (BI) is its ability to give business users access to selection, analysis, and reporting tools without requiring intervention from IT. This is essential if BI is to maximize its contribution by radically transforming how people make decisions. However, while some progress has been made through tools such as SAS Enterprise Miner, IBM SPSS Modeler, and RapidMiner, analytical modeling remains firmly in the domain of IT departments and data scientists. The development of tools that mitigate the need for modeling expertise remains the “missing link” in self-service BI, but prior attempts at developing modeling languages for non-technical audiences have not been widely implemented. By introducing a structured methodology for model formulation specifically designed for practitioners, this paper fills the unmet need to bring model-building to a mainstream business audience. The paper also shows how to build a dimensional Model Management Warehouse that supports the proposed methodology, and demonstrates the viability of this approach by applying it to a problem faced by the Division of Fiscal and Actuarial Services of the US Department of Labor. The paper concludes by outlining several areas for future research.     

A hybrid methodology for restructuring decision of a manufacturing system: A case study

The output performance of a manufacturing system depends on its operational continuity. However, a manufacturing system can stop for a short time or it can be out of service for a long time due to crucial problems such as poor performance of old machines, unexpected breakdowns or faulty plant and system design. Many firms try to restructure their manufacturing systems to avoid these crucial problems and to be more efficient, profitable and better organized. However, the decision to begin the process of restructuring is a difficult and critical choice for most decision makers due to the fact that the decision threshold of the process is ambiguous. This paper proposes a hybrid methodology for the decision makers who will decide on restructuring a manufacturing system. The proposed methodology provides crucial information for the decision makers combining fuzzy logic approach and reliability analysis.     

What is on your mind? Using the perceptual cycle model and critical decision method to understand the decision-making process in the cockpit

Aeronautical decision-making is complex as there is not always a clear coupling between the decision made and decision outcome. As such, there is a call for process-orientated decision research in order to understand why a decision made sense at the time it was made. Schema theory explains how we interact with the world using stored mental representations and forms an integral part of the perceptual cycle model (PCM); proposed here as a way to understand the decision-making process. This paper qualitatively analyses data from the critical decision method (CDM) based on the principles of the PCM. It is demonstrated that the approach can be used to understand a decision-making process and highlights how influential schemata can be at informing decision-making. The reliability of this approach is established, the general applicability is discussed and directions for future work are considered.

Practitioner Summary: This paper introduces the PCM, and the associated schema theory, as a framework to structure and explain data collected from the CDM. The reliability of both the method and coding scheme is addressed.     

Group decision making to better respond customer needs in software development

Quality Function Deployment (QFD) is a well-known planning methodology for translating customer needs into relevant design and production requirements. The intent of applying QFD is to incorporate the voice of the customer into the various phases of the product development cycle for a new product, or a new version of an existing product. The traditional QFD structure requires individuals to express their preferences in a restricted scale without exceptions. In practice, people contributing to the process tend generally to give information about their personal preferences in many different ways, numerically or linguistically, depending on their background. Moreover, collaborative decision-making is not an emphasized issue in QFD even though it requires several people's involvement. In this study, we extend the QFD methodology by introducing a new group decision making approach that takes into account multiple preference formats and fusing different expressions into one uniform group decision by means of fuzzy set theory. An application on software development is supplied to illustrate the approach.     

领域大数据应用开发与运行平台技术研究

随着大数据技术在不同领域的快速应用,构建大数据应用系统的开发与运行一体化平台,降低大数据技术在各行各业应用普及的门槛,为面向领域的大数据应用系统的快捷开发和高效运行提供方法、工具和平台支撑,成为大数据产业发展的迫切需求。由于大数据固有的复杂性、动态性、多样性及其价值独创性,目前尚未形成系统化的大数据软件开发方法,难以满足不同领域对大数据全生命周期处理的多样化需求。大数据时代的软件工程面临的挑战,体现在互为依赖的两方面：面向大数据全生命周期的集成设计开发环境和基于软件生命周期的系统运行分析工具。本文结合特定领域的实际需求,研究面向领域的大数据应用系统开发与运行一体化平台技术,覆盖大数据生命周期（获取、清洗、集成、分析、呈现）以及软件生命周期（设计、开发、运行、优化）,形成自管理、自适应、自优化的平台化解决方案。在此基础上,开展面向装备物联网及气象民生服务的大数据示范应用,以验证平台的有效性。     

An effective fuzzy collaborative forecasting approach for predicting the job cycle time in wafer fabrication

Predicting the cycle time of each job in a factory is an important task to the factory. However, it is not easy to deal with the uncertainty in the job cycle time. To cope with this problem and to effectively predict the job cycle time, an effective fuzzy collaborative forecasting approach is proposed in this study. The main difference between the proposed methodology and the existing methods is that the proposed methodology generates a fuzzy cycle time forecast in an effective way. In addition, the proposed method utilizes each round of fuzzy artificial neural network training to generate the upper and lower bounds of the job cycle time. The upper and lower bounds then serve as the basis for the subsequent collaboration. We collected the data of 120 jobs from a wafer fabrication factory to assess the effectiveness of the proposed method. The analysis results showed that the proposed fuzzy collaborative forecasting approach was indeed more efficient and accurate than some existing methods.     

Dynamic tolerant skyline operation for decision making

Skyline operation is typical multicriteria decision making well documented in data engineering. The assumption of skyline operation is settled human preference, which may be subject to huge challenges in practical decision-making applications because it simplifies preference scenarios that are usually dynamic. This study establishes the mathematical formulation of dynamic preference in real settings. A decision approach called tolerant skyline operation (T-skyline) is completely developed, including its conceptual modeling, computation methods, and a skyline maintenance mechanism on a database. The method is established and its computation mechanism is designed, and both are evaluated through an empirical study of personnel selection and evaluation. We also analyze computation efficiency and system stability. The decision targets are fully achieved, the computation results are satisfactory, and the computation efficiency is rational. The effectiveness and advantages of the approach are significant, as illustrated in different real-world settings. Experiments facilitated the examination of the design and development of T-skyline operation by adopting real and public datasets to evaluate players in the National Basketball Association in the United States. The experiment results validate the practical viability of our decision model, which can inspire discussions in sport industries. The methodology used in this study is valuable for further academic research, particularly for the interdisciplinary investigation of decision making and data engineering.     

An integrated decision making approach for ERP system selection

Enterprise resource planning (ERP) systems have gained major prominence by enabling companies to streamline their operations, leverage and integrate business data process. In order to implement an ERP project successfully, it is necessary to select an ERP system which can be aligned with the needs of the company. Thus, a robust decision making approach for ERP software selection requires both company needs and characteristics of the ERP system and their interactions to be taken into account. This paper develops a novel decision framework for ERP software selection based on quality function deployment (QFD), fuzzy linear regression and zero–one goal programming. The proposed framework enables both company demands and ERP system characteristics to be considered, and provides the means for incorporating not only the relationships between company demands and ERP system characteristics but also the interactions between ERP system characteristics through adopting the QFD principles. The presented methodology appears as a sound investment decision making tool for ERP systems as well as other information systems. The potential use of the proposed decision framework is illustrated through an application.     

DRSA中基于支配矩阵的类合集近似的新方法

摘 要：DRSA(Dominance-based Rough Set Approach)是经典粗糙集理论在支配关系下的扩展，而类合集近似是DRSA中非常重要的概念与方法。传统的方法是利用原始的定义来求取类合集的上下近似，针对其计算复杂的缺点，该文提出了一种基于支配矩阵的类合集近似新方法，不仅将计算复杂度降到约1/k倍（k为决策类的个数），而且简捷直观。最后的实例证明了新方法的有效性及可行性。     

Pattern classification by concurrently determined piecewise linear and convex discriminant functions

This paper develops a new methodology for pattern classification by concurrently determined k piecewise linear and convex discriminant functions. Toward the end, we design a new l₁-norm distance metric for measuring misclassification errors and use it to develop a mixed 0–1 integer and linear program (MILP) for the k piecewise linear and convex separation of data. The proposed model is meritorious in that it considers the synergy as well as the individual role of the k hyperplanes in constructing a decision surface and exploits the advances in theory and algorithms and the advent of powerful softwares for MILP for its solution. With artificially created data, we illustrate pros and cons of pattern classification by the proposed methodology. With six benchmark classification datasets, we demonstrate that the proposed approach is effective and competitive with well-established learning methods. In summary, the classifiers constructed by the proposed approach obtain the best prediction rates on three of the six datasets and the second best records for two of the remaining three datasets.     

To disassemble or not: a computational methodology for decision making

Environmentally conscious manufacturing is an important paradigm in today’s industrial practices and disassembly is a crucial factor in implementing this paradigm. Disassembly allows the reuse and recycling of parts and products that reach their “death” after their life cycle ends. There are many questions that must be answered before a disassembly decision can be reached. The most important question is economical. The cost of disassembly versus the cost of scrapping a product is always considered. This paper develops a computational methodology that allows decision-makers to calculate the disassembly cost of a product before it is built. The methodology makes it simple to perform “what if” scenarios fairly quickly. A Java based application has been developed to implement this methodology and it uses computational algorithms and a graphical user interface to enable designers to simulate product designs. The front end user interface is a Java based application while the back-end is the combination of a data parser and disassembly engine, which also makes use of Java and XML technologies. The disassembly engine performs calculations based on data represented in an XML data store and runs as the back-end component of the computational tools. The user interface has the ability to display a dynamically configurable disassembly tree. The disassembly knowledge is represented in XML format to allow robust and scalable parsing and processing of the various disassembly alternatives that correspond to the configurable disassembly tree. Examples are presented to demonstrate the implementation and capabilities of the computational design methodology presented in this paper. Received: March 2005 / Accepted: January 2006     

Missile defense and interceptor allocation by neuro-dynamicprogramming

This paper proposes a solution methodology for a missile defense problem involving the sequential allocation of defensive resources over a series of engagements. The problem is cast as a dynamic programming/Markovian decision problem, which is computationally intractable by exact methods because of its large number of states and its complex modeling issues. We employed a neuro-dynamic programming framework, whereby the cost-to-go function is approximated using neural network architectures that are trained on simulated data. We report on the performance obtained using several different training methods, and we compare this performance with the optimal approach     

Maximum appreciative cross-efficiency in DEA: A new ranking method

Ranking decision making units (DMUs) is one of the most important applications of data envelopment analysis (DEA). In this paper, we exploit the power of individual appreciativeness in developing a methodology that combines cross-evaluation, preference voting and ordered weighted averaging (OWA). We show that each stage of the proposed methodology enhances discrimination among DMUs while offering more flexibility to the decision process. Our approach is illustrated through an example involving 15 baseball players.     

A strategic approach to customer satisfaction in the telecommunication service market

As is the case in most other service industries, customer satisfaction is of paramount importance in the telecommunications industry. owever, customer satisfaction management in the telecommunication industry is difficult because of the diversity of services and customer segments that exist. This diversity makes it implausible to have a uniform customer satisfaction questionnaire that can be administered to all the different service/customer segment combination. Therefore, we first carried out a segmentation study to identify key customer segments for Korea Telecom, and then focused on one service/customer group in developing the questionnaire. The questionnaire was developed using the SERVPERF approach to measuring service satisfaction. We then developed a decision support system to manage and analyze the customer satisfaction data. The system consists of data input, tracking ability, and statistical modeling capabilities. The computer software was designed as an “open” decision support system that was intended to be used by a wide and general audience within Korea Telecom to review and make active use of customer satisfaction data.     

Automatic data collection for enterprise architecture models

Enterprise Architecture (EA) is an approach used to provide decision support based on organization-wide models. The creation of such models is, however, cumbersome as multiple aspects of an organization need to be considered, making manual efforts time-consuming, and error prone. Thus, the EA approach would be significantly more promising if the data used when creating the models could be collected automatically—a topic not yet properly addressed by either academia or industry. This paper proposes network scanning for automatic data collection and uses an existing software tool for generating EA models (ArchiMate is employed as an example) based on the IT infrastructure of enterprises. While some manual effort is required to make the models fully useful to many practical scenarios (e.g., to detail the actual services provided by IT components), empirical results show that the methodology is accurate and (in its default state) require little effort to carry out.     

Fuzzy LINMAP method for multiattribute decision making under fuzzy environments

The Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) developed by Srinivasan and Shocker [V. Srinivasan, A.D. Shocker, Linear programming techniques for multidimensional analysis of preference, Psychometrika 38 (1973) 337–342] is one of the existing well-known methods for multiattribute decision making (MADM) problems. However, the LINMAP only can deal with MADM problems in crisp environments. Fuzziness is inherent in decision data and decision making processes, and linguistic variables are well suited to assessing an alternative on qualitative attributes using fuzzy ratings. The aim of this paper is further extending the LINMAP method to develop a new methodology for solving MADM problems under fuzzy environments. In this methodology, linguistic variables are used to capture fuzziness in decision information and decision making processes by means of a fuzzy decision matrix. A new vertex method is proposed to calculate the distance between trapezium fuzzy number scores. Consistency and inconsistency indices are defined on the basis of preferences between alternatives given by the decision maker. Each alternative is assessed on the basis of its distance to a fuzzy positive ideal solution (FPIS) which is unknown. The FPIS and the weights of attributes are then estimated using a new linear programming model based upon the consistency and inconsistency indices defined. Finally, the distance of each alternative to the FPIS can be calculated to determine the ranking order of all alternatives. A numerical example is examined to demonstrate the implementation process of this methodology. Also it has been proved that the methodology proposed in this paper can deal with MADM problems under not only fuzzy environments but also crisp environments.     

Conceptual process planning – an improvement approach using QFD,FMEA, and ABC methods

Conceptual process planning (CPP) is an important technique for assessing the manufacturability and estimating the cost of conceptual design in the early product design stage. This paper presents an approach to develop a quality/cost-based conceptual process planning (QCCPP). This approach aims to determine key process resources with estimation of manufacturing cost, taking into account the risk cost associated to the process plan. It can serve as a useful methodology to support the decision making during the initial planning stage of the product development cycle. Quality function deployment (QFD) method is used to select the process alternatives by incorporating a capability function for process elements called a composite process capability index (CCP). The quality characteristics and the process elements in QFD method have been taken as input to complete process failure mode and effects analysis (FMEA) table. To estimate manufacturing cost, the proposed approach deploys activity-based costing (ABC) method. Then, an extended technique of classical FMEA method is employed to estimate the cost of risks associated to the studied process plan, this technique is called cost-based FMEA. For each resource combination, the output data is gathered in a selection table that helps for detailed process planning in order to improve product quality/cost ratio. A case study is presented to illustrate this approach.     

Microcomputer-based software to perform lease versus buy analysis

The primary purpose of this paper is to present a computerized lease-versus-buy program utilized by the Defense Contract Audit Agency to monitor defense contractor data processing hardware procurement. The main points of the paper shall pertain to the financial elements incorporated into the software, reports generated, and the resulting lease-versus-buy decision. Discussion of the lease-versus-buy process shall relate primarily to the procurement of data processing equipment. The approach taken in the lease-versus-buy decision process for procuring data processing equipment is basically no different from the methodology used in acquiring other types of capital equipment. Hence, the proposed microcomputer-based program used to performed lease-versus-buy analysis for data processing equipment is suitable for industrial equipment procurement, as well.     

Systematic modeling and model handling for manpower planning systems

The primary purpose of this paper is to show an efficient way of handling models and model data in a decision support system, in which it is usual to consider several variants of a model. The model data primarily consist of model-defining data, but the same approach may be used for the generated results as well. By efficient handling is meant the handling by the computer as well as by the user. For the user it is particularly important that new models can be conveniently defined as variants of existing models. The approach is introduced within the context of a decision support system for manpower planning based on Markov models. In the mean time the same approach has been used for the implementation of other decision support systems and has been found to be more generally applicable.