A technological,organisational, and environmental analysis of decision making methodologies and satisfaction in the context of IT induced business transformations

Although Operational Research (OR) has successfully provided many methodologies to address complex decision problems, in particular based on the rationality principle, there has been too little discussion regarding their limited consideration in IT evaluation practice and associated decision making satisfaction levels in an organisational context. The aim of this paper is to address these issues through providing a current account of diffusion and infusion of OR methodologies in IT decision making practice, and by analysing factors affecting decision making satisfaction from a Technological, Organisational, and Environmental (TOE) framework in the context of IT induced business transformations. We developed a structural equation model and conducted an empirical survey, which supported four out of five developed research hypotheses. Our results show that while Decision Support Systems (DSSs), holistic IT evaluation methods, and management support seem to positively affect individual satisfaction, legislative regulation has an adverse effect. Results also revealed a persistent methodology diffusion and infusion gap. The paper discusses implications in each of these aspects and presents opportunities for future work.     

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.     

Decision maker preferences for information in complex decision making: New directions for OR intervention

Operations research models are used in many business and non-business entities to support a variety of decision making activities, primarily well-defined, operational decisions. This is due to the traditional emphasis of these models on optimal solutions to pre-specified problems. Some attempts have been made to use OR models in support of more complex, strategic decision making. Traditionally, these models have been developed without explicit consideration for the information processing abilities and limitations of the decision makers, who interact with, provide input to, and receive output from such models.Research in judgement and decision making show that human decisions are influenced by a number of factors including, but not limited to, information presentation modes; information content, modes, e.g., quantitative versus qualitative; order effects such as primacy, recency; and simultaneous versus sequential presentation of data.This article presents empirical research findings involving executive business decision makers and their preferences for information in decision making scenarios. These preference functions were evaluated using OR techniques. The results indicate that decision makers view information in different ways. Some decision makers prefer qualitative, narrative, social information, whereas other prefer quantitative, numerical, firm specific information. Results also show that decision making tasks influence the preference structure of decision makers, but that in general, the preference are relatively stable across tasks.The results imply that for OR models to be more useful in support of non-routine decision making, attention needs to be focused on the information content and presentation effects of model inputs and outputs.     

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.     

Artificial intelligence and human decision making

Decision is obviously related to reasoning. One of the possible definitions of artificial intelligence (AI) refers to cognitive processes and especially to reasoning. Before making any decision, people also reason, it is therefore natural to explore the links between AI and decision making. This paper distinguishes between two aspects of decision making: diagnosis and look-ahead. It is shown that, on the one hand, AI has many relationships with diagnosis (expert systems, case-based reasoning, fuzzy set and rough set theories). On the other hand, AI has not paid enough attention to look-ahead reasoning, whose main components are uncertainty and preferences. These aspects of AI and decision making are reviewed in the paper.     

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).     

A survey of decision support system applications (1995–2001)

To extend two previous surveys of specific decision support system (DSS) applications over the period (January 1971–December 1994), we have conducted a follow-up survey covering the period between 1995 and 2001. A total of 210 published applications are identified. To examine the development pattern of a specific DSS over time, we analysed and summarized the survey results according to (1) the area of application, (2) the year of publication in each area of application, (3) the distribution of underlying tools in DSSs, (4) a classification based on Alter's taxonomy, and (5) the management level (operational, tactical, or strategic) for which the DSS was designed.     

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.     

A knowledge-based multi-role decision support system for ore blending cost optimization of blast furnaces

Literature illustrates the difficulties in obtaining the lowest-cost optimal solution to an ore blending problem for blast furnaces by using the traditional trial-and-error method in iron and steel enterprises. To solve this problem, we developed a cost optimization model which we have implemented in a multi-role-based decision support system (DSS). On the basis of analyzing the business flow and working process of ore blending, we propose an architecture of DSS which is built based on multi-roles. This DSS construction pre-processes the data for materials and elements, builds a general database, abstracts the related optimal operations research models and introduces the reasoning mechanism of an expert system. A non-linear model of ore blending for blast furnaces and its solutions are provided. A database, a model base and a knowledge base are integrated into the expert system-based multi-role DSS to meet the different demands of data, information and decision-making knowledge for the various roles of users. A comparison of the results for the DSS and the trial-and-error method is provided. The system has produced excellent economic benefits since it was implemented at the Xiangtan Iron & Steel Group Co. Ltd., China.     

Success and survival of operational research groups—A review

Operational Research groups were established in private and public sector organizations following on from their success supporting the military in the Second World War. In the early years their numbers grew rapidly but by the mid-1970s evidence started to accumulate that they were being viewed less favourably. Many organizations chose not to set up OR groups. Established groups were closed down or dispersed to operating units in organizations, particularly during the recessionary periods. In 1993 following on the closures of some major OR groups in the UK, the Operational Research Society decided to fund research to examine those factors influencing a group's success and survival. This review, which forms part of the research, first examines the history of OR groups in the UK and North America, arguing that in practice they were one of a number of internal organizational consultancies. As such they were judged by their competitive performance. Despite the intent of the founding fathers of OR many groups found their scope was limited to mainly tactical problems using analytical model based approaches although there were exceptions that were successful in making major contributions at the strategic level also. Within their chosen fields of activity however many groups flourished whilst some failed. This review presents a comprehensive list of influences on success and survival. They include changes in the external environment, together with shifts in managerial style and culture, organizational factors, and the OR group itself. The review concludes that, while OR groups can do much to protect themselves from closure, organizational changes within a hostile managerial culture demand a creative response that acknowledges new requirements. Areas where an OR group has greatest influence over its effectiveness include its leadership and personnel, project balance and management. In addition, to be successful OR groups need to positively develop and market their services in an increasingly competitive managerial climate.     

决策偏好不确定条件下群体决策收敛一致性研究

决策个体之间交互影响是达成群体一致意见的重要因素。本文引入决策个体决策偏好的不确定性以及个体间不对称的影响权重,以此为基础建立群体决策动态观点演化模型,并进行matlab仿真。文中取决策个体的偏好不确定性为常数,作为决策个体对自身观点确定性的大致考量,并把群体中所有决策者的决策偏好不确定性分为相同和不相同两种情况来讨论。仿真实例证明:决策个体观点的不确定性是最终影响群体决策一致性的重要因素,当决策个体偏好不确定性较大时,决策群体更容易得到一致意见;当偏好不确定性较小时,决策群体难以达成一致意见,最终形成几个观点簇;在初始观点分布均匀的情况下,群体观点最终会演化出怎样的观点形态,是由群体中观点不确定性较小的群体所持有的观点决定的。研究内容有助于理解群体决策中一致观点的演化规律,能够为群体决策的引导策略提供参考和借鉴。     

Multi-choice programming: an overview of theories and applications

This paper presents an exhaustive literature review on theories and applications in the field of multi-choice programming (MCP). The increasing competition in the business world has given rise to the situations where decision-makers are offered with multiple options/information to optimally decide on a single task. Under such circumstances a number of decision-making problems are falling into the scenario of MCPs. Thus, ever-increasing applicability of the MCPs is making more and more Operations Research practitioners to focus on them. This paper analyses all the related work and presents the same by classifying the research problems into two broad categories: theories and applications. As an aid to assist future researchers and practitioners, key insights on the evolution of various variants of MCPs have also been systematically delineated. The paper also throws light on some research gaps and concludes with the scope of future research in this area.     

Incorporating expertise into decision analysis based DSS

Artificial Intelligence/Expert Systems (AI/ES) techniques are proposed as means of combining the expertise of the experienced decision analyst with the computational methods of Decision Analysis (DA) to provide a user-friendly problem structuring, solution and querying environment which can be accessed directly by those responsible for managerial decisions.Such a system can be implemented as an Expert System shell. The key features of the shell are the inclusion of knowledge from previous problems through problem templates and the use of a combination of natural language and influence diagrams to communicate with users.This shell has the potential to further expand the use of DA by helping to overcome the DA skill shortage and to address some of the difficulties associated with purely AI/ES approaches to complex business problem solving. The greatest benefit of an Expert Decision Analyst is through integration with organisational DSS to support valuable information processing activities such as strategic decision-making.     

A transdisciplinary approach for formalized integrated planning and decision-making in complex systems

Multi-sectoral systems, such as cities or regional economies, face strategic challenges of optimal development due to the complexity of interacting perspectives, interests, and preferences of decision-makers and stakeholders. In coping with these challenges, integrated approaches in strategic planning and decision support promise to generate more efficient and effective results than sectoral approaches. In this article, we follow the concept of operational research (OR) by applying a formalized approach to cross-sectoral integrated planning and decision-making in complex, i.e., multi-sectoral systems. The presented Transdisciplinary Integrated Planning and Synthesis (TIPS) approach relies on (soft) OR methods combined in a multi-methodological framework that is adapted to the cognitive skills and habits of the stakeholders and experts involved in mutual and joint learning processes (transdisciplinarity). This methodological contribution structures, formalizes, and empirically illustrates cross-sectoral integrated strategic planning step by step, and evaluates the TIPS methodology against requirements for integrated approaches derived from literature.     

Operations research applications in Taiwan: A linguistic approach

This paper discusses the utilization of specified Operations Research (OR) techniques and the application areas of OR in Taiwan's companies. Since the data solicited from the business organizations are represented by fuzzy linguistic terms, a linguistic approach is used to transform the data to total utility values for ranking. The results show that decision making with computers and statistical forecasting are the two most frequently used OR techniques, while Markovian decision processes and queuing models are the two least used ones. Regarding the application areas, manufacturing firms consider production whereas service companies consider finance as the major area for application. Both agree that human resource is the area receiving the least attention. With the linguistic approach of this study, more valuable results are derived.     

Approaches to multistage one-shot decision making

In this research, multistage one-shot decision making under uncertainty is studied. In such a decision problem, a decision maker has one and only one chance to make a decision at each stage with possibilistic information. Based on the one-shot decision theory, approaches to multistage one-shot decision making are proposed. In the proposed approach, a decision maker chooses one state amongst all the states according to his/her attitude about satisfaction and possibility at each stage. The payoff at each stage is associated with the focus points at the succeeding stages. Based on the selected states (focus points), the sequence of optimal decisions is determined by dynamic programming. The proposed method is a fundamental alternative for multistage decision making under uncertainty because it is scenario-based instead of lottery-based as in the other existing methods. The one-shot optimal stopping problem is analyzed where a decision maker has only one chance to determine stopping or continuing at each stage. The theoretical results have been obtained.     

基于联系数不确定性分析的区间数多属性决策

区间数多属性决策通常是在某一算法下得到方案的排序,很少考虑区间数不确定性的影响,通过区间数转换成联系数,可以客观地反映区间数的不确定性,在此基础上,建立基于联系数的多属性决策模型,从而对不确定性进行较全面的取值分西,使决策结果更为合理.     

中国运筹学发展研究报告

运筹学是自20世纪三四十年代发展起来的一门新兴交叉学科,主要研究如何应用数学和计算的理论与方法对社会系统和工程系统做出最优或满意的决策。为了更好地推动中国运筹学及相关领域的研究和应用,学会组织国内运筹学领域的部分专家,并参考了国内外运筹学多个分支代表性人物对运筹学的起源和发展的回忆与评述,共同完成了此报告。报告概述了运筹学的主要特征和方法,简述了运筹学的发展历程,剖析了运筹学研究中的成功经验,综述了运筹学几个主要分支的发展状况,介绍了运筹学中十几个有代表性的难题,展望了运筹学未来发展的方向。希望此报告能引起读者进一步思考运筹学的本质,运筹学是如何成长和将如何发展,并在一定程度上推动中国运筹学更好地发展。     

Possible new applications of the interactive programming based on aspiration levels—case of pure and mixed strategies

The interactive programming (IP) using aspiration levels is a well-known method applied to multi-criteria decision making under certainty (M-DMC). However, some essential analogies between M-DMC and scenario-based one-criterion decision making under uncertainty (1-DMU) have been recently revealed in the literature. These observations give the opportunity to adjust the IP to a totaly new issue. The goal of the paper is to create two novel procedures for uncertain problems on the basis of the IP ideas: the first one for pure strategy searching and the second for mixed strategy searching. In many ways, they allow a better consideration of the decision maker's preferences than classical decision rules. One of their significant advantages consists in analyzing particular scenarios sequentially. Another strong point is that the new procedures can be used by any kind of decision makers (optimists, moderate, pessimists). The new approaches may be helpful when solving problems under uncertainty with partially known probabilities. Both methods are illustrated in the paper on the basis of two fictitious decision problems concerning the choice of an optimal location and the optimization of the stock portfolio structure.

     

基于二次联系数的区间数多属性决策方法及应用

针对属性权重与属性值都是区间数的多属性决策问题,提出基于联系数基本决策模型的二次联系数概念,再利用二次联系数值的变化对区间数多属性决策中的不确定性进行分析,从而客观地反映出区间数多属性决策中不确定性对决策结果的影响,算法简明清晰,结论可靠。