A survey of decision support system applications (1988–1994)

To extend a previous survey of specific decision support system (DSS) applications over the period (January 1971–April 1988), we have conducted a follow-up survey of DSS applications published between May 1988 and December 1994. Two hundred seventy-one published applications are identified. This survey reveals that there appear to be more creative applications of optimisation and suggestion model-based DSS than simulation-based applications. This is evidenced by a proportional increase of optimisation and suggestion models and a decrease of representation models. Moreover, group decision support systems, executive support systems, and knowledge-based systems applications are becoming more prevalent in many organisations. Although management science (MS)/operational research (OR) models continue to play critical roles, there is a clear observable trend in the DSS model area that three non-MS/OR tools are emerging as powerful DSS tools: graphics, artificial intelligence, and visual interactive modeling.     

Artificial intelligence for decision support systems in the field of operations research: review and future scope of research

Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.

     

Prototyping an intelligent decision support system for improving urban infrastructures management

This paper describes an intelligent decision support system (IDSS) dedicated to coordinated management of urban infrastructures (SIGIU). This system identifies the data and related treatments common to several municipal activities and defines the requirements and functionalities of the computer tools developed to improve the delivery, performance and coordination of municipal services to the population. The resulting cooperative system called SIGIU is composed of a global planning and coordination system (SYGEC) and a set of integrated operating systems (SYDEX), each of them being associated with a specific urban system (sewerage, waterworks, etc.). In order to support the decision-making process, an IDSS was developed as a knowledge-based system provided with inference mechanisms that enables SYGEC and SYDEX to make strategic choices in terms of technical interventions on municipal infrastructures. The knowledge-based system stores experts' knowledge as well as solutions to past problems. Preliminary implementation results show that SIGIU effectively and efficiently supports the decision-making process related to managing urban infrastructures.     

A Comparison of Potential Applications of Expert Systems and Decision Support Systems

This paper describes ways in which decision support systems (DSS) and expert systems (ES) may be differentiated. It proposes a simplified systemic view which emphasises the functional differences of the environments within which each type of system is applied, thereby avoiding some of the problems often associated with attempting to define DSS and ES according to their constituent parts. This functional emphasis allows DSS and ES to be placed within a useful existing theoretical framework which takes account of contextual influences. The results of two independent surveys of DSS and ES applications in a sample domain-accountancy-are used to assess the view of their functionality held by users of the systems, particularly their functional differences. Some conclusions are drawn about the role of the user in the construction of such systems.     

An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented.     

A case-based fuzzy multicriteria decision support model for tropical cyclone forecasting

This paper proposes a multi-stage framework for intelligent decision support. The proposed framework integrates case-based reasoning and fuzzy multicriteria decision making techniques. It potentially leads to more accurate, flexible and efficient retrieval of alternatives that are most similar and most useful to the current decision situation. Additionally, the framework provides intelligent assistance in articulating domain expert's preferences through outranking relations. We illustrated the proposed approach in the context of tropical cyclone prediction. Ten years of historical observation data about tropical cyclones was represented within fuzzy multicriteria decision-making problem. We describe a prototype intelligent decision support system, which helps the forecaster in retrieving best-fitted solutions in terms of both usefulness and similarity to the current observed case.     

Validation of decision-aiding spreadsheets: the influence of contingency factors

This paper describes an investigation into the influence of contingency factors on the validation of spreadsheet-based decision support systems (DSS), and develops a methodological framework for validation that takes account of the effect of contingency factors. The research extends and confirms previous research by one of the authors who identified relevant contingency factors. The perceived influence of these contingency factors on validation effort was investigated by an empirical study, and the results of this study were then used to derive contingency-related guidance for validation. This was evaluated by a sample of practitioners and academics with experience in the development of spreadsheet-based DSS, and was then incorporated into a methodological framework for validation. The framework is designed to enable managers, decision-makers, project leaders and other non-OR specialists to identify the extent of validation effort likely to be required to ensure that the spreadsheet models that they build are valid. The research has implications for the validation of other types of OR models.     

Dynamic construction of statistical models in managerial DSS

The fields of operations research (OR) and artificial intelligence (AI) provide complementary methods that may be combined into managerial decision support systems (DSS). However, the management domain is substantially different from domains in which prior expert systems have been developed. Consequently, successful application of OR/AI techniques in managerial DSS requires careful analysis and additional development. Ongoing research concerning design and implementation of managerial DSS is discussed. A prototype system capable of constructing linear statistical models of direct and indirect relationships from a knowledge base of relationships is described and evaluated.     

Relationships between the decision support system subspecialities and reference disciplines: An empirical investigation

This is a comprehensive study, that, by means of an empirical assessment of the DSS literature, systematically identifies the DSS reference disciplines and traces how concepts and findings by researchers in the contributing disciplines have been picked up by DSS researchers to be applied, extended, and refined in the development of DSS research subspecialties. Cluster analysis was employed to an author cocitation frequency matrix derived from a comprehensive database of the DSS literature over the period of 1970 through 1993. Twelve clusters were uncovered consisting of six major areas of DSS research (group DSS, foundations, model management, user interfaces, implementation, and multiple criteria DSS) and six contributing disciplines (multiple criteria decision making, cognitive science, organization science, artificial intelligence, group decision making, and systems science).     

作战方案评估智能决策支持系统研究

作战决策是作战指挥的核心，作战指挥智能决策支持系统（IDSS）是用于辅助指挥员进行作战决策的计算机软件。它是指挥自动化的重要组成部分。本通过研究作战方案评估IDSS，探讨了作战指挥IDSS的结构及功能。     

Integrated decision support for aviation safety inspectors

This paper presents a systems viewpoint for developing an advanced decision support system for aircraft safety inspectors. Research results from a Federal Aviation Administration (FAA) sponsored project to use neural network and expert systems technology to analyze aircraft maintenance databases are summarized. One of the main objectives of this research is to define more refined “alert” indicators for national comparison purposes that can signal potential problem areas by aircraft type for safety inspector consideration.

Integration aspects are addressed on two levels: (1) integration of the various technical components of the decision support system, and (2) integration of the decision support system with individual behavior, management systems and organizational structure, as well as corporate culture across both formal and informal dimensions. The paper summarizes the creation of strategic “inspection profiles” for aging aircraft and reliability curve fitting for structural components both based upon using neural network technology. Also, the potential use of a model-based expert system to facilitate field inspection diagnostics is presented. Finally, a framework for developing an intelligent decision system to support aircraft safety inspections is proposed that links expert systems, neural networks, as well as a paradigm of the decision making process typically used in unstructured situations.     

A flexible concept for decision support: Evaluation and selection of suitable product development activities

Because of pragmatic reasons actual decision behaviour often differs from rationality requirements of decision theory. Against this background, a new concept for decision support has been developed opening up the opportunity to choose between different levels of evaluation. The characteristic feature of the proposed concept is its foundation in rationality postulates of decision theory while considering pragmatic requirements simultaneously. Thereby, the concept must be perceived as a frame within which the decision process may be formed in a flexible way. It has been drawn up and is presented in connection with the development of a decision support system (DSS) for comparative evaluation and selection of product development activities.     

A strategic decision support system framework for energy-efficient technology investments

Energy systems optimization under uncertainty is increasing in its importance due to on-going global de-regulation of the energy sector and the setting of environmental and efficiency targets which generate new multi-agent risks requiring a model-based stakeholders dialogue and new systemic regulations. This paper develops an integrated framework for decision support systems (DSS) for the optimal planning and operation of a building infrastructure under appearing systemic de-regulations and risks. The DSS relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons bounded by stopping time moments. This allows to model impacts of potential extreme events and structural changes emerging from a stakeholders dialogue, which may occur at any moment of the decision making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The DSS implementation via an algebraic modeling language (AML) provides an environment that enforces the necessary stakeholders dialogue for robust planning and operation of a building infrastructure. Such a framework allows the representation and solution of building infrastructure systems optimization problems, to be implemented at the building level to confront rising systemic economic and environmental global changes.     

运筹学在整车物流智能调度决策支持系统中的研究与应用

本文基于整车物流智能调度决策支持系统的研发、实施与运维的成功应用,论述运筹学在智能化上的应用路径以及实践驱动的学术路径。该系统是国内较早在汽车物流企业实现落地的智能化调度系统,其所形成的思想理论与方法技术揭示了运筹学在智能化应用上的核心价值,以及实践驱动的学术价值,对解决“卡脖子”难题提供示范性思路。本文提出运筹学在智能化研发上“三环七步”的整体研发框架。首先,分析智能化需求的运筹学特征,详细介绍汽车整车物流的发展趋势、瓶颈及智能调度需求;其次,论述运筹学系统模型的作用与建模方法,分析汽车整车物流系统模型的决策要素、目标及约束,提出汽车整车物流智能调度的运筹学应用问题。然后,提出“模式装箱”的新装箱理论问题,明确问题的计算难解性、可解性及核心科学特征。进一步,建立汽车整车物流调度应用问题与科学问题的混合整数线性规划模型;提出求解汽车整车物流调度问题的分支定界算法,以及大规模问题求解的时空分解及滚动求解方法与技术;提出面向运筹应用的生产测试及压力测试方法,给出汽车整车物流调度的测试分析的流程与结果。此外,提出深度集成整车运输管理系统与仓库管理系统、优化算法引擎驱动的分布式、多视图、多系统融合的智能调度决策支持系统。最后,论述该系统在实施过程中的推广使用和运维情况,并对运筹学应用及实践驱动的科学研究进行总结与展望。     

Representation of decision making processes in an ecological evaluation system

This paper deals with the problems of knowledge representation for a decision support system (DSS) applicable in a dynamic environment. Some special principles concerning environment applications are considered in order to understand better human decision making and behavior. The approach of representing static and dynamic aspects of a system and reflecting them using deep knowledge representation is proposed. The formalization of multiple objective decision making mechanisms is considered. The results of modeling cognitive processes leading to decisions are demonstrated by an example developed during the design stages of an ecological evaluation system.     

A framework for performance measurement during production ramp-up of assembly stations

Production ramp-up is an important phase in the lifecycle of a manufacturing system which still has significant potential for improvement and thereby reducing the time-to-market of new and updated products. Production systems today are mostly one-of-a-kind complex, engineered-to-order systems. Their ramp-up is a complex order of physical and logical adjustments which are characterised by try and error decision making resulting in frequent reiterations and unnecessary repetitions. Studies have shown that clear goal setting and feedback can significantly improve the effectiveness of decision-making in predominantly human decision processes such as ramp-up. However, few measurement-driven decision aides have been reported which focus on ramp-up improvement and no systematic approach for ramp-up time reduction has yet been defined. In this paper, a framework for measuring the performance during ramp-up is proposed in order to support decision making by providing clear metrics based on the measurable and observable status of the technical system. This work proposes a systematic framework for data preparation, ramp-up formalisation, and performance measurement. A model for defining the ramp-up state of a system has been developed in order to formalise and capture its condition. Functionality, quality and performance based metrics have been identified to formalise a clear ramp-up index as a measurement to guide and support the human decision making. For the validation of the proposed framework, two ramp-up processes of an assembly station were emulated and their comparison was used to evaluate this work.     

Interactive multicriteria procedure exploiting a knowledge-based module to select electricity production alternatives: The CASTART system

For some decisional problems, it is difficult to have a set of potential alternatives a priori. This is true, for instance, when the object of the decision is dependent on an external environment, as in the case of disturbance situations in industrial processes. Computer assistance must support, first, the elaboration of a potential alternatives set and, second, the decision-making activity which will determine the decision elements leading to recovery from the disturbance. This paper deals with such a situation encountered in an electricity production context. An interactive multicriteria procedure exploiting a knowledge-based module to select electricity production alternatives is presented. Section 1 introduces the electricity production-consumption framework and identifies the Spinning Reserve Mobilization (SRM) function. Next, our contribution to the systematization and the experimentation of a decision-making process which supports the dispatcher confronted with an SRM situation is described. The decision-making process includes an intelligent module and a decisional procedure. The module and the procedure, and their interconnections, are developed in Section 2. Section 3 describes the DSS called CASTART which implements this process. An overview of DSS functionalities is given, as well as the results obtained by a dispatcher working in cooperation with the system, in the case of a simulated production incident.     

Initiating a state of the art system for real-time supply chain coordination

Intelligent Wireless Web (IWW) employs the capabilities of high speed wireless networks and exploits the parallel advancements in Internet-based technologies such as the Semantic Web, Web Services, Agent-based Technologies, and context awareness. Considering its great potentials to be applied in business systems, we have devised an innovative model, based on the IWW services, for a typical mobile real-time supply chain coordination system which has been developed and tested in a real operational environment. Our article investigates the proposed system in this way: at the start, the building blocks of the IWW are discussed in detail. Then, we fully explain the basic concepts of mobile real-time supply chain coordination and concentrate on the motivations to implement such a modern system. The vision of intelligent wireless web services, as discussed in this paper, centers on the need to provide mobile supply chain members highly specific data and services in real-time on an as-needed basis, with the flexibility of use for the user. In this regard, we investigate nine enabling technologies of the IWW for our system and discuss how, by exploiting the convergence and synergy between different technologies, it has become possible to deliver intelligent wireless web support to mobile real-time supply chain coordination. Afterwards, a practical framework is clearly established in four phases. This initiative system has been implemented in the laboratory and has passed the evaluation processes successfully. Further details will be announced in near future in another research article.     

Intelligent management systems in operations: a review

Operations management is an area that has recently started to benefit from the use of AI techniques such as expert systems, neural networks and genetic algorithms. These techniques can extend the usefulness of OR modelling and enable new types of decision tasks to be supported by computer-based systems. This paper attempts to review ‘intelligent’ decision support systems and their potential to address some of the problems faced in various areas of operations management. Some useful techniques developed in the field of artificial intelligence are outlined and examples of attempts to use these approaches to support decision making in various areas of operations management are described. Recognising the scale of a complete review of all these areas, emphasis has been given to the most significant and more recent publications.     

An interactive decision-making approach based on intelligent systems to redraw district boundaries in regional planning

Environmental planning decisions have become a major challenge and are receiving increasing social attention. To contend with the absence of a decision theory in this domain and to solve conflicts in group decision-making, an interactive decision approach has been defined and supported by a computer-based system to automate the different steps of the modelling process. In this paper, the emphasis is on the advent of intelligent systems and the improvement of management support. This support enables individual decision-makers to augment their knowledge about a problem domain which results in a gain in productivity. The different steps of the approach are described and illustrated through a worked example which is a specific decision problem and deals with redrawing the district boundaries in regional planning.