A general methodology for data-based rule building and its application to natural disaster management

Risks derived from natural disasters have a deeper impact than the sole damage suffered by the affected zone and its population. Because disasters can affect geostrategic stability and international safety, developed countries invest a huge amount of funds to manage these risks. A large portion of these funds are channeled through United Nations agencies and international non-governmental organizations (NGOs), which at the same time are carrying out more and more complex operations. For these reasons, technological support for these actors is required, all the more so because the global economic crisis is placing emphasis on the need for efficiency and transparency in the management of (relatively limited) funds. Nevertheless, currently available sophisticated tools for disaster management do not fit well into these contexts because their infrastructure requirements usually exceed the capabilities of such organizations. In this paper, a general methodology for inductive rule building is described and applied to natural-disaster management. The application is a data-based, two-level knowledge decision support system (DSS) prototype which provides damage assessment for multiple disaster scenarios to support humanitarian NGOs involved in response to natural disasters. A validation process is carried out to measure the accuracy of both the methodology and the DSS.     

Temporal semantics and natural language processing in a decision support system

This paper is an attempt to address in an integrated fashion three issues of interest in the decision support system (DSS) context. First, to deal with dynamic problem environments, a mechanism is proposed to represent data and decision models with explicit temporal aspects. Second, in order to provide a natural language interface to such a DSS, this representation of temporal knowledge is used to process queries including those with explicit temporal aspects. Third, the issue of computational efficiency has been addressed using the language of equational logic as a deduction oriented language for problem solving as well as describing environmental knowledge. An example describing the salient features of the system is presented.     

An evolutionary algorithm-based decision support system for managing flexible manufacturing

The high investment cost of flexible manufacturing systems (FMS) requires their management to be effective and efficient. The effectiveness in managing FMSs includes addressing machine loading, scheduling parts and dispatching vehicles and the quality of the solution. Therefore the problem is inevitably multi-criteria, and decision maker's judgement may contribute to the quality of the solution and the systems's performance. On the other hand, each of these problems of FMS is hard to optimize due to the large and discrete solution spaces (NP-hard). The FMS manager must address each of these problems hierarchically (separately) or simultaneously (aggregately) in a limited time. The efficiency of the management is related to the response time.

Here we propose a decision support system that utilizes an evolutionary algorithm (EA) with a memory of “good” past experiments as the solution engine. Therefore, even in the absence of an expert decision maker the performance of the solution engine and/or the quality of the solutions are maintained.

The experiences of the decision maker(s) are collected in a database (i.e., memory-base) that contains problem characteristics, the modeling parameters of the evolutionary program, and the quality of the solution. The solution engine in the decision support system utilizes the information contained in the memory-base in solving the current problem. The initial population is created based on a memory-based seeding algorithm that incorporates information extracted from the quality solutions available in the database. Therefore, the performance of the engine is designed to improve following each use gradually. The comparisons obtained over a set of randomly generated test problems indicate that EAs with the proposed memory-based seeding perform well. Consequently, the proposed DSS improves not only the effectiveness (better solution) but also the efficiency (shorter response time) of the decision maker(s).     

基于知识的多专家决策支持系统

由于决策支持系统（ＤＳＳ）和专家系统（ＥＳ）的不同行为特征,使它们在信息处理中的决策和问题求解方面不能相互兼顾。本文交讨论和ＥＳ结合的可行性,提出一种基于知识的多专家决策支持系统的结构模式,及实现多专家协同求解和决策的方法。     

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。     

MM-DSS: Integrating multimedia and decision-making knowledge in decision support systems

Multimedia (MM) information systems have been utilized for decision-making tasks since they were developed in the early 1990s. Few research studies to date have paid attention to the integrated mode of MM information with a decision support system (DSS) or have investigated the role of MM information and DSS from the perspective of model-based knowledge management. This paper proposes a multimedia decision support system (MM-DSS) to address the above unexplored issues. The proposed MM-DSS manages MM information with the use of a newly developed extended attributed relational graph (ARG) and the structured modeling (SM) technique in order to support complicated decision-making processes intelligently and systematically. This paper illustrates the development of the new extended ARG and the SM method, and their integration into mathematical decision-making knowledge. The proposed MM-DSS responds to the needs of representation, storage, and retrieval of MM information in decision-making processes. Results of simulation show that the proposed MM-DSS is successful in tackling an optimization problem of factory scheduling.     

一个新的支持多学科模型集成的流域决策支持系统框架研究

传统流域决策支持系统往往使用一两个模型来解决某些特定目标,限于系统结构而不能综合考虑多领域问题。提出了一种新的支持多学科模型集成的流域决策支持系统原型,在一个系统中实现多种学科模型的跨时空尺度集成,为流域综合管理提供全面科学支持。系统采用开放式多学科模型管理方法,其脚本扩展机制使得系统可以容纳几乎所有模型,模型因子方法使得形式多样的科学模型模拟结果可以应用到决策方法中,使系统在多学科模型支持下提供科学合理的决策方案。情景驱动的决策流程、向导式交互界面及GIS等图形化工具支持使得系统用户界面友好\,易用性强。     

Adaptive decision support systems

The effectiveness of decision support systems (DSS) is enhanced through dynamic adaptation of support to the needs of the decision maker, to the problem, and to the decision context. We define this enhanced DSS as adaptive decision support systems (ADSS) and propose its architecture. In an ADSS, the decision maker controls the decision process. However, the system monitors the process to match support to the needs. The proposed architecture evolves from the traditional DSS models and includes an additional intelligent‘Adaptation’ component. The ‘Adaptation’ component workd with the data, model, and interface components to provide adaptive support. The architecture also integrates enhancements proposed in the past research. In this paper, we have illustrated the proposed architecture with two examples, a prototype system, and results from a preliminary empirical investigations     

Composite decision support by combining cost-benefit and multi-criteria decision analysis

This paper concerns composite decision support based on combining cost-benefit analysis (CBA) with multi-criteria decision analysis (MCDA) for the assessment of economic as well as strategic impacts within transport projects. Specifically a composite model for assessment (COSIMA) is presented as a decision support system (DSS). This COSIMA DSS ensures that the assessment is conducted in a systematic, transparent and explicit way. The modelling principles presented are illuminated with a case study concerning a complex decision problem. The outcome demonstrates the approach as a valuable DSS, and it is concluded that appraisals of large transport projects can be effectively supported using a combination of CBA and MCDA. Finally, perspectives of the future modelling work are given.     

Algorithms for fuzzy multi expert multi criteria decision making (ME-MCDM)

Inherent complexity and uncertainty in a business environment necessitate the participation of many experts in multi criteria decision making. However, participation of many experts makes the conflict aggregation process difficult. To handle this difficulty, we propose two algorithms namely possibility measure and averaging conflict aggregation. In possibility measure, we integrate the possibility theory of fuzzy logic with a maximal containment method that is designed based on the decision problem. Possibility measure algorithm for ME-MCDM involves computationally expensive multiple information processing steps. Therefore to test and compare this algorithm, averaging conflict aggregation algorithm is proposed that requires fewer mathematical information processing steps. Based on the proposed algorithms, a decision support system (DSS) is developed. We present a case study of supplier evaluation to compare both of the proposed algorithms with the help of developed DSS.     

Decision support systems: Alternative perspectives and schools

This paper reviews and compares four distinct decision support system (DSS) perspectives or ‘schools’: Decision analysis, decision research, decision calculus, and implementation process. Each school represents a relatively coherent perspective for DSS development; all four address the development and use of computer-based tools that support and aid managers in their role as decision makers. They differ, however, in terms of the nature of the decision situation envisaged, the phase of the decision process considered, the primary aims for a DSS development effort, the nature of the learning to be achieved and the phase of the development process that is emphasized. The purpose of the review is an attempt to establish a more constructive approach to understanding the central tenets and challenges for DSS. At the same time, the unique concerns and distinct contributions of the different schools suggest that it is difficult to define a single approach that satisfies equally well all considerations and all demands.     

Applying machine learning to model management in decision support systems

Model management systems have become increasingly important in handling complicated decision problems in decision support systems (DSS). Aiming at overcoming the weaknesses of currently used model management systems, we present a new framework of model management system which is capable of performing model manipulation more effectively. The new approach incorporates machine learning to acquire model manipulation knowledge, stored in the form of schemata, and to refine these acquired schemata. In addition, we also address two issues that have so far been overlooked in the DSS literature: (1) to refine existing model representations as more experiences are accumulated and (2) to create model selection heuristics adaptive to the DSS environment.     

基于次协调逻辑的空战决策支持系统研究

为了解决传统逻辑推理在引入两个相互矛盾的事实时,推理将停止,继而提供不出有价值的结论的问题,给出了基于次协调逻辑理论的一种推理方法.在此基础上构造了次协调逻辑辅助推理空战决策支持系统,在传统逻辑推理因矛盾停止时,启动次协调推理,使得空战决策支持能够在矛盾中求协调,避免了系统陷入平庸状态.在某型空战模拟器上使用取得了初步成效,从仿真结果看具有次协调逻辑辅助推理的空战决策支持系统对复杂的空战环境具有良好的适应性.     

A decision support systems approach to adjust maintenance float system availability levels

In this paper, a decision support system (DSS) is designed to predict the system availability levels for equipment maintenance float problems. The DSS is developed by using the analytic hierarchy process (AHP) to prioritize the improvements made in the maintenance management practice through the adoption of total quality management (TQM). The resulting regression metamodel can be used to predict and explain system availability. Sensitivity analysis can be easily generated through the DSS. The application of this DSS helps to effectively control the cost of maintenance floats as a result of TQM implementation, generates quick solutions, and provides the decision maker with the flexibility of carrying out sensitivity analysis and planning for the future by looking at the long-term impacts of TQM on the maintenance float system.     

基于库存控制的数据仓库与决策支持

通过对传统决策支持系统已经不能满足当今企业对决策支持的需要的分析,提出了采用数据仓库（DW）,联机分析处理（OLAP）及数据挖掘技术为企业提供决策必要性,设计了一种利用数据仓库（DW）技术和联机分析处理（OLAP）、数据挖掘（DM）等数据仓库工具提供库存控制决策支持的方案,并着重阐述了联机分析处理（OLAP）与数据挖掘是如何为企业库存控制提供决策支持的。     

Cognitive biases and decision support systems development: a design science approach

Abstract.  This paper presents design science research that aims to improve decision support systems (DSS) development in organizations. Evolutionary development has been central to DSS theory and practice for decades, but a significant problem for DSS analysts remains how to conceptualize the improvement of a decision task during evolutionary DSS development. The objective of a DSS project is to improve the decision process and outcome for a manager making an important decision. The DSS analyst needs to have a clear idea of the nature of the target decision task and a clear strategy of how to support the decision process. Existing psychological research was examined for help with the conceptualization problem, and the theory of cognitive bias is proposed as a candidate for this assistance. A taxonomy of 37 cognitive biases that codifies a complex area of psychological research is developed. The core of the project involves the construction of a design artefact – an evolutionary DSS development methodology that uses cognitive bias theory as a focusing construct, especially in its analysis cycles. The methodology is the major contribution of the project. The feasibility and effectiveness of the development methodology are evaluated in a participatory case study of a strategic DSS project where a managing director is supported in a decision about whether to close a division of a company.     

Model management system for IRT-based test construction decision support system

Decision support system (DSS) has become widespread for some specific domains in recent years. However, DSS for IRT-based (item response theory) test construction has not yet been developed. This domain basically imposes a semi-structured or unstructured decision and, therefore, involves a very complex modeling process. This study develops a model management system (MMS) architecture to assist a non-expert user in manipulating test construction process efficiently and effectively. This architecture consists of four components: problem analysis, model type selection, model formulation and solver. The model type selection subsystem is further organized into three levels of hierarchy, i.e., environment, structure and parameter. A prototype is presented to demonstrate the feasibility of this architecture. The results indicate that this approach can be applied for providing an integrated, flexible and user-friendly DSS environment for producing better quality of results in less solution time.     

Flexible decision support in dynamic inter-organisational networks

An effective Decision Support System (DSS) should help its users improve decision making in complex, information-rich environments. We present a feature gap analysis that shows that current decision support technologies lack important qualities for a new generation of agile business models that require easy, temporary integration across organisational boundaries. We enumerate these qualities as DSS Desiderata, properties that can contribute both effectiveness and flexibility to users in such environments. To address this gap, we describe a new design approach that enables users to compose decision behaviours from separate, configurable components, and allows dynamic construction of analysis and modelling tools from small, single-purpose evaluator services. The result is what we call an ‘evaluator service network’ that can easily be configured to test hypotheses and analyse the impact of various choices for elements of decision processes. We have implemented and tested this design in an interactive version of the MinneTAC trading agent, an agent designed for the Trading Agent Competition for Supply Chain Management.     

Decision support system based on hierarchical co-evolutionary fuzzy approach: A case study in detecting gamma ray signals

Decision support systems are powerful technologies for complex decision making and problem solving. However, constructing an accurate and interpretable decision support system (DSS) for any domain is a challenge. In this paper, a novel hierarchical co-evolutionary fuzzy system called HiCEFS is presented that can autonomously derive a fuzzy rule-based DSS from exemplar data. Most of the important components in HiCEFS, including irregular shaped membership functions (ISMFs) and fuzzy rules, are generated using a hierarchical co-evolutionary genetic algorithm that simultaneously co-evolves these components in separate genetic populations. Owing to its generic learning capability, the HiCEFS approach can be easily applied to produce DSSs for classification and regression tasks in various domains. As a case study, HiCEFS is employed to construct a DSS for detecting gamma ray signals. Experimental results show that the system is able to successfully discern the gamma rays from background hadrons, and performs superior to other established techniques.     

A decision support system for quantitative measurement of operational efficiency in a blood collection facility

A decision support system (DSS) is presented that allows users to input, analyze, and output data derived from blood banking operations. The DSS developed is a hybrid system that is both data and model driven. The system provides information, models, and data manipulation tools to assist users in the quantitative measurement of the operational efficiency in a blood collection facility. A relational database was developed to address the four major variables, which impact the cost per unit of blood being collected. Using visual basic, a user interface and mathematical model were developed establishing the relationships to analyze cost per unit of collected blood. Using inputs from users and historical financial data, the DSS calculates the cost per unit as each of the major variables is altered. Real life situations by the mobile operations team at a blood collection facility were used to test the DSS.