Different kinds of models to integrate the OR-expert's knowledge into Decision Support Systems

This paper presents an approach to integrate OR-expert's knowledge about the modelling of real world problems into a Decision Support System (DSS). First, the problem of modelling is considered and a short survey over the structure of DSS is given. Then a model of different phases of interaction between a DSS and its user is discussed. In the framework of this phase model, different kinds of models are necessary to enable a DSS to perform the support of its user. These models, representing the OR-expert's knowledge, and their various interrelations are discussed. Finally, a prototype computer implementation of this approach is presented and further research topics are figured out.     

A Case Study in Community Care Using Systems Thinking

This paper uses a recent application of System Dynamics in community care to contribute to a clarification of the role of System Dynamics as a framework for strategic debate. A revised framework for System Dynamics within a philosophy of ‘modelling as learning’ is summarized. This framework is now referred to as Systems Thinking and combines knowledge acquisition and both qualitative and quantitative modelling, supported by microworlds and archetypes to aid dissemination of insights. The emphasis of the paper is on using qualitative models interactively with different groups of participants in the problem domain. The purpose is to assist the development of a shared understanding of how culture, power and politics combine to affect the behaviour of a process, when subjected to externally superimposed changes in responsibility.     

Towards the integration of system modelling with scenario planning to support strategy: the case of the UK energy industry

A key aspect of strategy is sense-making of the unfolding uncertainty in the business environment and responding appropriately to achieve organisational objectives. However, uncertainty means that there is more than one future open to an organisation. Sense-making is therefore problematic. Scenario planning is one approach to sense-making that helps to explore and understand uncertainty, aiming for the identification of potential predetermined elements in the business environment. In this paper we propose the integration of systems modelling with scenario planning to support the exploration of uncertainty, identify knowledge gaps that set a (subsequent) research agenda, understand the role and usefulness of historical data, and model research outcomes to help reveal potential predetermined elements. Specifically, scenario planning identifies potential predetermined elements in the business environment; system modelling in the form of behaviour-over-time graphs, causal mapping and feedback loops helps in structuring and linking variables and their interaction to provide an understanding of the systemic drivers of these predetermined elements. Methodological integration of this type enhances modellers’ ability to support strategy in organisations.     

Semiqualitative modelling of bioprocesses

This paper deals with semiqualitative modelling of bioprocesses with a view to their supervision. An analysis of several approaches for modelling shows the difficulties involved in taking into account in a same framework, quantitative and qualitative knowledge, generally available about a process that we want to control. We propose an original approach, placed in the context of semiqualitative modelling, that is supported by a knowledge model the variables and parameters of which are defined by intervals. For these semiqualitative models, we study their properties in simulation and prediction, and more precisely, their fitting based on experimental data. We show that pertinent predictions in a short time can be obtained, making of these semiqualitative models interesting tools for the development of systems for bioprocess supervision     

Decision support models in climate policy

Climate change is considered among the most critical risks that global society faces in this century. So far, climate policy strategies have been evaluated by means of a variety of climate-economy models, or Integrated Assessment Models (IAMs), in the aim of supporting climate-related decision making. However, their inherent complexity, the number and nature of driving assumptions, and usual exclusion of stakeholders from the modelling process raise the issue of the extent to which they can provide fruitful insights for policy makers. Moreover, as with all modelling frameworks, IAMs inevitably fail to incorporate all relevant types of uncertainty and risk when used as stand-alone tools. This exclusion can have a significant impact on the model outcomes, but can be mitigated if experts’ knowledge is elicited in a structured manner and effectively taken into account, towards identifying such factors or reducing respective knowledge gaps. At the same time, a growing number of research publications have been suggesting decision support frameworks for assessing specific aspects in climate policy, based on “bottom-up” approaches and participatory processes. The objective of this paper is to provide a critical review of such frameworks—namely Portfolio Analysis, Multiple Criteria Decision Making and Fuzzy Cognitive Maps—in order to explore their strengths and weaknesses in this area, and propose a new integrative approach, appropriately exploiting blends of these frameworks, to productively complement IAMs, towards enhancing climate policy support.     

Multicriteria evidential reasoning decision modelling and analysis—prioritizing voices of customer

In this paper, a new methodology is investigated to support the prioritization of the voices of customers through various customer satisfaction surveys. This new methodology consists of two key components: an innovative evidence-driven decision modelling framework for representing and transforming large amounts of data sets and a generic reasoning-based decision support process for aggregating evidence to prioritize the voices of customer on the basis of the Evidential Reasoning (ER) approach. Methods and frameworks for data collection and representation via multiple customer satisfaction surveys were examined first and the distinctive features of quantitative and qualitative survey data are analysed. Several novel yet natural and pragmatic rule-based functions are then proposed to transform survey data systematically and consistently from different measurement scales to a common scale, with the original features and profiles of the data preserved in the transformation process. These new transformation functions are proposed to mimic expert judgement processes and designed to be sufficiently flexible and rigorous so that expert judgements and domain specific knowledge can be taken into account naturally, systematically and consistently in the transformation process. The ER approach is used for synthesizing quantitative and qualitative data under uncertainty that can be caused due to missing data and ambiguous survey questions. A new generic method is also proposed for ranking the voices of customer based on qualitative measurement scales without having to quantify assessment grades to fixed numerical values. A case study is examined using an Intelligent Decision System (IDS) to illustrate the application of the decision modelling framework and decision support process for prioritizing the voices of customer for a world-leading car manufacturer.     

Using system dynamics to analyse disruption and delay in complex projects for litigation: can the modelling purposes be met?

System dynamics (SD) is a modelling approach that has been used to analyse disruption and delay (D&D) for litigation in a number of cases over the last 30 years. However, there is a lack of literature addressing the question of whether or not it is actually a suitable modelling approach to take in this environment. This paper explores this question by considering whether or not SD is capable of meeting the modelling purposes of analysing D&D for litigation. The author's experience as part of a team which has carried out post-mortem analysis on projects for a number of litigation cases is used to consider the degree to which SD can meet these modelling purposes. This process highlights limitations of using SD. An understanding of these limitations is important, so that a modeller can make an informed decision about the appropriateness of SD as a modelling approach to support any specific claim for compensation.     

Mathematical modelling as a tool for the connection of school mathematics

We start introducing some aspects of the theoretical framework: the Anthropological Theory of Didactics (ATD). Then, we consider on the research domain commonly known as “modelling and applications” and briefly describe its evolution using the ATD as an analytical tool. We propose a reformulation of the modelling processes from the point of view of the ATD, which is useful to identify new educational phenomena and to propose and tackle new research problems. Finally, we focus on the problem of the connection of school mathematics. The reformulation of the modelling processes emerges as a didactic tool to tackle this research problem. We work on the problem of the articulation of the study of functional relationships in Secondary Education and present a teaching proposal designed to reduce the disconnection in the study of functional relationships in Spanish Secondary Education.     

不同信任环境下在线知识社区知识系统演化研究

信任作为在线知识社区中的社会影响因素,对社区中的成员进行沟通学习、知识共享有着重要的作用。不同的在线知识社区有着不同的信任环境,而信任环境的不同会影响社区中用户的学习模式和观点传播。基于此,本文提出了基于信任与Deffaunt的组合观点影响模型。信任模型主要将社区中的信任分为认知信任和情感信任,通过调节参数结构,对应不同信任环境中信任的动态演化过程。Deffaunt模型作为基本观点影响模型,模拟了不同信任环境下的在线知识社区的知识观点的演化过程。实验结果发现,信任环境的高低决定了社区中的观点是否收敛,并且社区中的群体理性人占比和信任程度都能影响观点的收敛速度。     

Logical soft systems modelling for information source analysis—The case of Hongkong Telecom

The paper describes the use of logico-linguistic modelling, a logically enhanced version of Soft System Methodology, to identify the information needed to support decisions made in a marketing department of a Hong Kong telecommunications company. This case indicates that logical complexity need not be a barrier to stakeholder comprehension. However, cross-cultural difficulties were encountered when using the basic SSM method.     

面向集群创新网络异质企业的知识扩散建模及仿真研究

基于合作的集群创新网络知识扩散已经成为企业实现知识创新的重要手段,而集群创新网络知识扩散的动力学过程强烈依赖于异质企业间知识扩散能力的影响,为此,本文综合考虑了企业间不同接触数量、知识吸收和传播能力、知识淘汰率等异质性因素,建立了基于传染病理论的知识扩散模型,验证了由各异质因素构成的知识扩散再生数对知识扩散均衡和扩散效果的影响,并结合仿真实验进一步得出:在知识扩散前期,集群创新网络应发挥hub节点及异质网络的优势加快知识扩散,在中后期应注意企业关系发展的均衡性及企业接触邻居的规模性;相比过于强调知识交流的广泛性,加强企业传播能力和吸收能力的培养对网络知识扩散效果的提升更具意义;随着时间演化,企业知识淘汰率也会影响网络知识扩散的收敛情况。     

A Hypermedia-based Delphi Tool for Knowledge Acquisition in Model Building

The traditional Delphi method has been used for many years as an approach for knowledge capture and consensus building in a variety of social and managerial areas, including model building. This paper describes research into the development and initial application of a computerized version of the Delphi method to assist model building in situations where the domain experts to be consulted are large in number and geographically dispersed. Specifically, it has been developed to assist System Dynamics model building, where the purpose of the modelling was to help with the design and assessment of computer-based information systems. The features of a Hypermedia-based Delphi system, which was developed for the Apple Macintosh are described and the results of its application to a modelling exercise are discussed, with particular emphasis on its ability to create consensus influence diagrams and graphical relationships.     

Extending decision support systems: The integration of data,knowledge, and model management

The proliferation of desktop computing has once again rekindled the interest in making computerized tools available to managers and other decision makers. This paper elaborates on a model that integrates data, knowledge, and model management and shows how decision support systems (DSSs) can be extended to support managers in a truly novel way. The model, the Knowledge/Data Model (KDM), is explained and the significance of its applicability to the management of data, knowledge, and models is illustrated through several examples. KDM continues to evolve and is being applied to domains from computer chip design to production and inventory management systems.     

On the formulation of stochastic linear programs using algebraic modelling languages

This paper considers extensions to algebraic modelling languages to support formulation, instantiation and solver integration for stochastic linear programs (SLPs). We present a taxonomy of SLP problem types and analyze formulation requirements including distribution handling by class of problem. We demonstrate suggested formulations for most problem classes, show solver input in the S-MPS standard, and propose consistency checks for constraints involving stochastic data items. Some unresolved difficulties are identified.     

Probability masses fitting in the analysis of manufacturing flow lines

The analysis of manufacturing systems with finite capacity and with general service time distributions is made of two steps: the distributions have first to be transformed into tractable phase-type distributions, and then the modified system can be analytically modelled. In this paper, we propose a new alternative in order to build tractable phase-type distributions, and study its effects on the global modelling process. Called “probability masses fitting” (PMF), the approach is quite simple: the probability masses on regular intervals are computed and aggregated on a single value in the corresponding interval, leading to a discrete distribution. PMF shows some interesting properties: it is bounding, monotonic, refinable, it approximates distributions with finite support and it conserves the shape of the distribution. With the resulting discrete distributions, the evolution of the system is then exactly modelled by a Markov chain. Here, we focus on flow lines and show that the method allows us to compute upper and lower bounds on the throughput as well as good approximations of the cycle time distributions. Finally, the global modelling method is shown, by numerical experiments, to compute accurate estimations of the throughput and of various performance measures, reaching accuracy levels of a few tenths of a percent.     

基于传播模式演化的组织内知识传播研究

组织内个体之间的知识传播存在不同的模式,传播模式会影响传播效果,反过来传播模式会随着传播的进行而发生演化,反作用于知识传播,进而影响组织绩效和创新。基于此,本文先归纳了六种知识传播模式之间的演化规则,然后对应不同类型的组织,利用复杂网络设置了多组仿真实验,模拟了传播模式不断演化时组织知识传播的动态过程。对比分析实验结果发现:专家对组织知识传播至关重要,专家能够大大提高组织知识储量;顺畅的沟通机制不仅能显著提高组织知识储量,还能让知识传播更兼顾公平;组织内个体的知识分享习惯是其获取知识的关键,越懂得分享的人越能获取更多的知识。     

政府不同策略下用户创新知识交互差异化研究

知识交互是用户创新的重要环节,基于知识基础观和博弈论,考虑政府在用户创新中对企业与用户知识交互的激励作用,构建“政府-企业-用户”三方演化博弈模型,探讨政府不同策略下企业与用户之间知识交互行为的演化路径,分析不同约束条件下主体策略选择情况,并进行数值模拟。研究结果表明:政府政策激励在推动用户创新知识交互过程中有重要作用。当企业与用户知识交互紧密时,政府趋向于采取支持策略。当企业与用户知识交互收益小于某一阈值时,政府支持是决定用户创新知识交互的关键变量。当企业与用户知识交互收益处于某个范围时,政府支持并不会显著影响企业和用户的知识交互行为。