Toward the design of an intelligent DSS for forecasting

The emergence of decision support systems (DSS), artificial intelligence (AI), and microcomputers in the information systems arena provides managers with some new insights to the planning and control of a productive system.

This paper illustrates how an intelligent microcomputer-based decision support system (MDSS) was developed to support demand forecasting.     

Intelligent Fril/SQL interrogator

The intelligent Fril/SQL interrogator is an object‐oriented and knowledge‐based support query system, which is implemented by the set of logic objects linking one another. These logic objects integrate SQL query, support logic programming language—Fril and Fril query together by processing them in sequence in slots of each logic object. This approach therefore takes advantage of both object‐oriented system and a logic programming‐based system. Fuzzy logic data mining and a machine learning tool kit built in the intelligent interrogator can automatically provide a knowledge base or rules to assist a human to analyze huge data sets or create intelligent controllers. Alternatively, users can write or edit the knowledge base or rules according to their requirements, so that the intelligent interrogator is also a support logic programming environment where users can write and run various Fril programs through these logic objects. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 279–302, 2007.     

An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank

Developing decision support system (DSS) can overcome the issues with personnel attributes and specifications. Personnel specifications have greatest impact on total efficiency. They can enhance total efficiency of critical personnel attributes. This study presents an intelligent integrated decision support system (DSS) for forecasting and optimization of complex personnel efficiency. DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. DEA has two roles in this study. It provides data to ANN and finally it selects the best reduct through ANN results. Reduct is described as a minimum subset of features, completely discriminating all objects in a data set. The reduct selection is achieved by RST. ANN has two roles in the integrated algorithm. ANN results are basis for selecting the best reduct and it is used for forecasting total efficiency. Finally, K-Means algorithm is used to develop the DSS. A procedure is proposed to develop the DSS with stated tools and completed rule base. The DSS could help managers to forecast and optimize efficiencies by selected attributes and grouping inferred efficiency. Also, it is an ideal tool for careful forecasting and planning. The proposed DSS is applied to an actual banking system and its superiorities and advantages are discussed.     

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition‐primed decision model

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well‐known human decision‐making model called Recognition‐Primed Decision (RPD). We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 827–845, 2007.     

Agent and data mining based decision support system and its adaptation to a new customer-centric electronic commerce

Recently, as the Internet has become more widely used, Electronic Commerce (EC) has emerged and has developed a high-level business environment. The customer-centric EC model is important for the success of EC and this study presents a new customer-centric EC model in make-to-order (MTO) semiconductor manufacturing environment. In this study we proposed the EC model providing the process transparency of process sampling method that can provide online semiconductor customers with the performance information of available process sampling methods which can be used at all manufacturing process steps for their own products in MTO manufacturing environment, and then the capability to select a desirable one among them based on their purchase situations on EC web site. In the proposed EC model the customer can select a process sampling method that is most suitable to him/her according to the customer's purchase situation. In this model the use of intelligent decision support system called customized sampling decision support system (CSDSS) that can autonomously generate available customized sampling methods and provide the performance information of those methods to EC system is requisite. We implemented an Internet-based prototype of CSDSS which had an architecture based on intelligent agent technology and also the successful integration of data mining process for the generation of optimal sampling method into DSS framework by means of applying that technology.     

基于多Agent的电力营销决策支持系统的研究

针对传统决策支持系统的局限性以及决策支持问题的复杂性,本文把Agent方法引入到决策支持系统的研究中,提出了一个新型的决策支持系统框架,设计了基于多Agent的电力营销决策支持系统。该模型中的多个Agent通过Agent解释服务通信交流,相互合作,共同完成决策支持任务,充分的发挥了Agent的自主性、反应性、协作性,为电力企业的决策问题的解决提供了一个新的途径。     

The wildspace™ decision support system

A system architecture was developed for the wildspace decision support system (DSS) to provide a better understanding of complex wildlife and habitat problems. The system makes use of two key concepts, SPECIES and SPACES, to define the study domain. wildspace DSS’s flexible user interface allows users to select SPECIES through a number of different approaches, including direct selection and selection using information such as avian life history and project metadata. On the SPACES side, the system uses the raison™ object system (ROS) for mapping functions and spatial analysis. The key element in wildspace DSS is its knowledge-based database manager that provides intelligent support to various components of the system. It keeps track of all the legitimate databases, provides intelligence within the SPECIES and SPACES selection process and, more importantly, interfaces with the knowledge templates which are sets of operations implementing pre-defined analysis routines used for integrated analysis. This integrated decision support approach allows users to combine a diverse set of tools within a common framework. wildspace DSS is used to study complex wildlife problems involving multiple projects and data that are temporally and spatially heterogeneous. A case study about a relevant wildlife conservation question is presented using a series of queries and analyses performed within wildspace DSS. The system also serves as the repository for all past, current and future wildlife data collected by the Canadian Wildlife Service—Ontario Region.     

报业广告智能决策系统的研究与应用

针对报业广告决策的特点,运用数据仓库技术和决策支持系统的思想开发出一套适应面广、灵活性高的智能决策支持系统。基于多层次决策应用的需要提出了虚拟集市的概念和一种改进的三层数据集市结构,并利用星型图数据建模技术将其成功应用到实际系统中;采用基于数据集市和联机分析处理的集成化智能决策支持系统模型,详细论述了系统的预测和客户分析功能模块并提出了基于层次分析法的客户忠诚度定量模型。该系统已成功应用到实际中,对报社广告部的决策提供了有力的支持,取得了良好的效果。     

A web DSS approach to building an intelligent internet shopping mall by integrating virtual reality and avatar

This paper is concerned with designing and implementing the Internet shopping mall by using a virtual reality-driven avatar and web decision support system (Web DSS). Traditionally, the Internet shopping mall has been designed based on the combination of several hyperlinks, images, and texts. However, this sort of approach results in a lower performance because possible customers cannot make accurate shopping decisions. To overcome these kinds of pitfalls facing the current Internet shopping malls, we propose using a combination of virtual reality and Web DSS. The main virtues of our proposed approach to designing the Internet shopping mall are as follows: First, the virtual reality technique is emerging as one of the alternatives that guarantee a sense of reality for the customers' part and facilitating the complex process of shopping decision makings. Especially, the avatar, which is an artificially designed man working on the Internet, can make the Internet shopping-related decision making processes easier. Second, the Web DSS approach can provide an effective decision support mechanism for customers. Especially, we design a set of intelligent agents for the proposed Web DSS. Experimental results with an illustrative example showed that our proposed approach can yield a new Internet shopping mall paradigm with which customers can benefit from a high level of decision support functions.     

神经网络与专家系统结合的DSS模型智能构造

模型智能构造是目前IDSS发展的一个趋势.针对DSS模型构造中存在的问题,提出了一种神经网络与专家系统结合的DSS模型智能构造方案及其系统结构.在对文字描述和数据描述分离的基础上,应用专家系统实现模型类型的选择,应用神经网络实现模型结构的构造.以ARMA模型智能构造为例,说明了方法的可行性和有效性.     

An adaptive stock index trading decision support system

Predicting the direction and movement of stock index prices is difficult, often leading to excessive trading, transaction costs, and missed opportunities. Often traders need a systematic method to not only spot trading opportunities, but to also provide a consistent approach, thereby minimizing trading errors and costs. While mechanical trading systems exist, they are usually designed for a specific stock, stock index, or other financial asset, and are often highly dependent on preselected inputs and model parameters that are expected to continue providing trading information well after the initial training or back-tested model development period. The following research leads to a detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output. To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising. Simulations with stock indexes illustrate how traders can generate higher returns using the developed adaptive decision support system model. The benefits of adding adaptive and intelligent decision making to forecasts are also discussed.     

On the design of an intelligent micro-based DSS for line balancing

The emergence of decision support systems, artificial intelligence, and microcomputers in the information systems area provides us with some new insights for the design and operation of productive systems. In this paper, the authors illustrate how an intelligent microcomputer-based decision support system (DSS) was developed to solve line balancing problems. The conceptual framework, technical issues, and minute details for effectively designing such a system are addressed. Directions for future research are also discussed.     

A research framework for Web-based open decision support systems

With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Web-based technologies are leading a major stream of researching decision support systems (DSS). In this paper, we propose a formal definition and a conceptual framework for Web-based open DSS (WODSS). The formal definition gives an overall view of WODSS and creates a uniform research framework for various decision support systems. The conceptual framework based on browser/broker/server computing mode employs the electronic market to mediate decision-makers and providers, and facilitate sharing and reusing of decision resources. We also analyze the basic functions and develop an admitting model, a trading model and a competing model of electronic market in WODSS based on market theory in economics. These models reveal the key mechanisms that drive WODSS function efficiently. Finally, an illustrative example is studied to support the proposed ideas.     

基于数据挖掘的智能决策支持系统

传统决策支持系统由于其功能的不足.不能满足现代企业的决策需求.而数据挖掘技术是解决该问题的有效途径之一.文章分析了数据挖掘技术在决策支持系统中的应用现状.提出了一种基于数据挖掘的智能决策支持系统的体系结构,并给出了总体结构模型和各子系统功能描述.     

An algorithmic framework for frequent intraday pattern recognition and exploitation in forex market

We present a knowledge discovery-based framework that is capable of discovering, analyzing and exploiting new intraday price patterns in forex markets, beyond the well-known chart formations of technical analysis. We present a novel pattern recognition algorithm for Pattern Matching, that we successfully used to construct more than 16,000 new intraday price patterns. After processing and analysis, we extracted 3518 chart formations that are capable of predicting the short-term direction of prices. In our experiments, we used forex time series from 8 paired-currencies in various time frames. The system computes the probabilities of events such as “within next 5 periods, price will increase more than 20 pips”. Results show that the system is capable of finding patterns whose output signals (tested on unseen data) have predictive accuracy which varies between 60 and 85% depending on the type of pattern. We test the usefulness of the discovered patterns, via implementation of an expert system using a straightforward strategy based on the direction and the accuracy of the pattern predictions. We compare our method against three standard trading techniques plus a “random trader,” and we also test against the results presented in two recently published studies. Our framework performs very well against all systems we directly compare , and also, against all other published results.     

Multi‐agent distributed intelligent system based on fuzzy decision making

Generally decision making for solving ill‐structured problems in DSS takes place in uncertain situations. The main drawbacks of existing traditional DSS are inefficiencies associated with dealing with complex models and large databases. Usually a fuzzy DSS has many input variables and, hence, its knowledge base, containing the totality of fuzzy rules, is very large. Large rule base leads to disadvantages in speed, reliability, and complexity of DSS. This paper introduces an alternative concept for designing fuzzy DSS based on multi‐agent distributed artificial intelligent technology and fuzzy decision making. The main idea of the proposed DSS is based on granulation of the overall system intelligence between cooperative autonomous intelligent agents capable of competing and cooperating with each other in order to propose a total solution to the problem and organization (combining individual solutions) of the proposed solution into the final solution. It is supposed that every agent in DSS is characterized by a set of fuzzy criteria of unequal importance and definition of a “winner” agent is based on multi‐criteria fuzzy decision making involving unequal objectives. © 2000 John Wiley & Sons, Inc.     

数据仓库在钻井工程决策中的应用研究

提出了开发钻井工程决策支持系统的一种新技术———数据仓库技术,给出了基于数据仓库的DSS的体系结构,并详细介绍了系统中面向主题的数据仓库的构建方法。     

An intelligent logistics support system for enhancing the airfreight forwarding business

Abstract: Recent research related to the aircraft container loading and scheduling problem for airfreight forwarding business has seen significant advances in terms of load plan optimization, taking into account the cost and volume of packed boxes. In today's competitive industrial environment, it is essential that freight forwarders are able to collaborate with carriers (airline companies) to achieve the best possible selection of logistics workflow. However, study of contemporary research publications indicates that there is a dearth of articles related to the design and implementation of an intelligent logistics system to support decision‐making on carrier selection, aircraft container loading plans as well as carrier benchmarking. This paper presents an intelligent logistics support system (ILSS) which is able to provide expert advice related to the airfreight forwarding business, enhancing the logistics operations in relevant activities within the value chain of tasks. ILSS comprises a heuristics‐based intelligent expert system which supports carrier searching and cargo trading planning as well as load plan generation. The proposed approach is meant to enhance various operations in the airfreight forwarding business, adopting computational intelligence technologies such as rule‐based reasoning to provide domain advice and heuristics to support the generation of load plans. After potential outcomes are generated by the heuristics‐based intelligent expert system, a neural network engine is applied to support prediction of unexpected events. To validate the viability of this approach, a production system using the ILSS has been developed and subsequently applied in an emulated airfreight forwarding environment. The application results indicate that the operation time from searching for potential carriers to the execution of the order is greatly reduced. In this paper, details related to the structure, design and implementation of the ILSS are also covered with the inclusion of the actual program codes for building the prototype.     

基于面向对象技术的综合评价决策支持系统的数据库研究

论述了面向对象技术在具有分层结构的综合评价决策支持系统 (DLCEDSS)中建立数据库子系统的具体应用 ,解决了传统决策支持系统中数据存储的弊端 ,为建立大型的决策支持系统的数据库提供了理论基础和方法。