虚拟产品设计方案神经网络决策模型研究

虚拟原型逼真设计作为虚拟产品开发研究的一个方面,是近几年发展起来的新技术,主要研究虚拟环境下的产品建模问题以及基于仿真的集成产品和过程设计问题。生命周期虚拟产品设计方案评价模型是虚拟原型逼真设计模型实现的重要模块之一,论文在设计方案多目标多级模糊综合决策模型的基础上,研究并建立了设计方案ANN多目标决策模型,该模型已应用于仪表新产品开发决策支持系统中。     

半在线入库堆垛问题的动态求解算法

堆场垛位优化问题一直是仓储管理的难点和焦点之一,垛位优化可以保证物料装卸和出入库的高效率,同时对保证合同交货期也起着至关重要的作用。针对仓储和生产一体化下的入库堆垛问题,本文通过分析将其归结为一类半在线的A型装箱问题,并依据问题的特点,建立了最小化总倒垛次数的优化模型。根据货场天车在相邻入库过程中存在空闲作业量的特点,设计了一种前序货物允许移动的动态堆垛策略,结合堆垛约束后嵌入到经典装箱启发式算法中,最后通过仿真算例验证了该策略的有效性。     

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.     

Decision support for flight re-routing in Europe

Congestion has plagued air traffic in the US and in Europe for the last 20 years. To protect air traffic control from overloads, air traffic flow management tries to anticipate and prevent overloads and to limit resulting delays. This paper focuses on understanding the requirements for developing re-routing decision support systems (DSS). It identifies participants in re-routing decisions and investigates the concept of, and need for, a re-routing decision support system. A re-routing demonstrator is discussed as a first step in the development of a DSS and a demonstrator for pre-tactical and tactical re-routings is described. User feedback is presented and issues of automation and complexity of re-routing DSS are discussed. Finally, the integration of re-routing DSS in future air traffic management systems is addressed.     

Building Linear Programming Models from Structural Model Components

Model management research investigates the formulation, analysis and interpretation of models. This paper focuses on the formulation aspects of modeling so that the task can be supported by decision support systems (DSS) environments. Given the knowledge intensive nature of the formulation process, the development of a modeling tool requires explicating the knowledge pertaining to modeling. This involves comprehending not only the static knowledge about model components (e.g. decision variables, coefficients, associated indices and constants), but also the process knowledge required to construct models from model pieces. The proposed top-down approach configures equations by exploiting the structural modeling knowledge inherent in equation components. The possible representation of equations at various abstraction levels is introduced, the aim being to uncover the structural model components together with the process knowledge required for their appropriate configuration. As part of developing this conceptual model, the role of semantic and syntactic information in model building is investigated. The paper proposes an approach where the formulation semantics are captured as a simple 'action-resource' view which composes models by identifying and piecing together the equation components. The process of equation construction is illustrated with examples from the linear programming (LP) modeling domain. The proposed top-down approach is contrasted with a bottom-up method.     

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.     

Progressive design methodology for complex engineering systems based on multiobjective genetic algorithms and linguistic decision making

This work focuses on a design methodology that aids in design and development of complex engineering systems. This design methodology consists of simulation, optimization and decision making. Within this work a framework is presented in which modelling, multi-objective optimization and multi criteria decision making techniques are used to design an engineering system. Due to the complexity of the designed system a three-step design process is suggested. In the first step multi-objective optimization using genetic algorithm is used. In the second step a multi attribute decision making process based on linguistic variables is suggested in order to facilitate the designer to express the preferences. In the last step the fine tuning of selected few variants are performed. This methodology is named as progressive design methodology. The method is applied as a case study to design a permanent magnet brushless DC motor drive and the results are compared with experimental values.     

The Importance of Active Exploration,Optical Flow,and Task Alignment for Spatial Learning in Desktop 3D Environments

The Decision Support Systems (DSS) field has grown rapidly, drawing technology from many disciplines and pursuing applications in a variety of domains but developing little underlying theoretical structure and poor linkage between research and practice. This article presents a classification scheme for DSS techniques that provides a common theoretical framework for DSS research and also structures and simplifies the process of designing application systems. The classification system is functional, grouping DSS techniques according to their ability to provide similar kinds of support (i.e., functions) to a human decision maker. It is also cognitively based, defining the kinds of support that decision makers need in terms of architectural features and procedural aspects of human cognition. The classification is expressed as a taxonomy, encompassing six primary classes of decision support techniques representing the six general kinds of cognitive support that human decision makers need. The six classes are process models, which assist in projecting the future course of complex processes; choice models, which support integration of decision criteria across aspects and/or alternatives; information control techniques, which help in storage, retrieval, organization, and integration of data and knowledge; analysis and reasoning techniques, which support application of problem-specific expert reasoning procedures; representation aids, which assist in expression and manipulation of a specific representation of a decision problem; and judgment amplification/refinement techniques, which help in quantification and debiasing of heuristic judgments. Additional distinctions are provided to distinguish the individual techniques in each of these primary categories. The taxonomy also has practical use as a design aid for decision support systems. The kinds of decision support needs represented by the taxonomy are general and can be used to guide the analysis and decomposition of a given decision prior to decision aid design. Specific needs for assistance can then be tied to specific computational techniques in the taxonomy. Methodological suggestions for using the taxonomy as a design aid are given.     

Dynamic tolerant skyline operation for decision making

Skyline operation is typical multicriteria decision making well documented in data engineering. The assumption of skyline operation is settled human preference, which may be subject to huge challenges in practical decision-making applications because it simplifies preference scenarios that are usually dynamic. This study establishes the mathematical formulation of dynamic preference in real settings. A decision approach called tolerant skyline operation (T-skyline) is completely developed, including its conceptual modeling, computation methods, and a skyline maintenance mechanism on a database. The method is established and its computation mechanism is designed, and both are evaluated through an empirical study of personnel selection and evaluation. We also analyze computation efficiency and system stability. The decision targets are fully achieved, the computation results are satisfactory, and the computation efficiency is rational. The effectiveness and advantages of the approach are significant, as illustrated in different real-world settings. Experiments facilitated the examination of the design and development of T-skyline operation by adopting real and public datasets to evaluate players in the National Basketball Association in the United States. The experiment results validate the practical viability of our decision model, which can inspire discussions in sport industries. The methodology used in this study is valuable for further academic research, particularly for the interdisciplinary investigation of decision making and data engineering.     

Diverse reasoning in automated model formulation

Diverse reasoning supports a dynamic integration of various reasoning methods in a computerized system. This paper describes a control blackboard approach to simulate the control features observed in the expert's model formulation protocols. The diverse reasoning concept is incorporated so that the model formulation process is dynamically in a plan-directed, action-directed, or data-directed fashion. The diverse reasoning concept facilitates the control features simulation. By analyzing the diverse reasoning behavior in the proposed system, this paper contributes to a better understanding of and support to the modeling process for the design of intelligent decision support systems. The usefulness of the prototype system is also evaluated using an empirical experiment.     

A decision support system for design of mass production machining lines composed of stations with rotary or mobile table

This paper describes a decision support system (DSS) developed in order to offer to machining line designers a cognitive aid for early design stages. The aim of DSS is to assist the decision makers in finding the configuration of a new line that will meet quality and productivity requirements and minimize the investment costs. The current version of DSS is oriented to design of mass production machining lines composed of machines with rotary or mobile tables. This decision support system is based on mathematical models and methods which were devised to provide the designers with the optimal parameters of new line configuration including the required number of working stations of different types, the number of working positions at each station and spindle heads at each working position. The system is implemented under Autodesk Inventor and includes the modules for part modeling, process planning and machining system configuration. Its modular character and open architecture make upgrading with new mathematical tools suitable for other machining systems easy and fast. Moreover, it can be employed either as a separate software or integrated in a Product Life-cycle Management (PLM) tool.     

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。     

Are you sure? Prediction revision in automated decision‐making

With the rapid improvements in machine learning and deep learning, decisions made by automated decision support systems (DSS) will increase. Besides the accuracy of predictions, their explainability becomes more important. The algorithms can construct complex mathematical prediction models. This causes insecurity to the predictions. The insecurity rises the need for equipping the algorithms with explanations. To examine how users trust automated DSS, an experiment was conducted. Our research aim is to examine how participants supported by an DSS revise their initial prediction by four varying approaches (treatments) in a between‐subject design study. The four treatments differ in the degree of explainability to understand the predictions of the system. First we used an interpretable regression model, second a Random Forest (considered to be a black box [BB]), third the BB with a local explanation and last the BB with a global explanation. We noticed that all participants improved their predictions after receiving an advice whether it was a complete BB or an BB with an explanation. The major finding was that interpretable models were not incorporated more in the decision process than BB models or BB models with explanations.     

Decision support in a guaranteed student loan program: Design and implementation of a model-based system

The development and implementation of a model-based DSS is described in this paper. The DSS was designed to assist executives at a public agency in the administration of the Guaranteed Student Loan Program. The purpose of the paper is to illustrate that a model-based DSS is an effective alternative to building a mathematical optimization model. In addition, we propose a design methodology for developing such a model-based DSS on a personal computer.     

基于语义推理的DSS模型研究与应用

基于语义网理论建立基于语义推理的DSS模型。该模型引用W3C提出的资源描述框架（RDF）以描述业务领域中各要素,形成领域本体。并对领域中业务数据进行语义标注,构建事实库。在所构建事实库的基础上,系统根据预设规则进行推理,以达到决策支持的目的。最后基于以上方法对施工项目设备推荐领域进行实例建模,建立设备推荐系统。该系统将Semantic Web与Ontology理论引入到施工项目领域,以解决施工项目过程中存在的设备选型推荐问题,并作为语义Web理论在该领域内应用的一次尝试。     

基于GA的仪表新产品功能结构方案推理机模型关键技术研究

虚拟原型逼真设计是近几年发展起来的新技术,主要研究虚拟环境下的产品建模问题以及基于仿真的集成产品和过程设计问题．产品功能结构方案推理机模型是虚拟原型逼真设计实现的关键技术之一．针对功能模块组合方案优化问题的复杂性,研究了基于遗传算法（GA）的功能结构方案推理机模型的实现技术,提出了双链异或杂交算子和自适应调节杂交概率和突变算子选择等算法,结合仪表产品设计应用实例,验证了模型的优越性,并将其应用于仪表新产品开发决策支持系统——仪表LC—QDPDS软件系统中．     

A method for model selection using reinforcement learning when viewing design as a sequential decision process

In an emerging paradigm, design is viewed as a sequential decision process (SDP) in which mathematical models of increasing fidelity are used in a sequence to systematically contract sets of design alternatives. The key idea behind SDP is to sequence models of increasing fidelity to provide sequentially tighter bounds on the decision criteria thereby removing inefficient designs from the tradespace with the guarantee that the antecedent model only removes design solutions that are dominated when analyzed using the more detailed, high-fidelity model. In general, efficiency in the SDP is achieved by using less expensive (low-fidelity) models early in the design process, before using high-fidelity models later on in the process. However, the set of multi-fidelity models and discrete decision states result in a combinatorial combination of model sequences, some of which require significantly fewer model evaluations than others. Unfortunately, the optimal modeling policy can not be determined at the onset of the SDP because the computational costs of executing all models on all designs and the discriminatory power of the resulting bounds are unknown. In this paper, the model selection problem is formulated as a finite Markov decision process (MDP) and an online reinforcement learning (RL) algorithm, namely, Q-learning, is used to obtain and follow an approximately optimal modeling policy, thereby overcoming the optimal modeling policy limitation of the current SDP. The outcome is a Reinforcement Learning based Design (RL-D) methodology able to learn efficient sequencing of models from sample estimates of the computational cost and discriminatory power of different models while analyzing design alternatives in the tradespace throughout the design process. Through application to two different design examples, the RL-D is shown to (1) effectively identify the approximate optimal modeling policy and (2) efficiently converge upon a choice set.

     

Uncertainties in FE modeling of slope stability

Modeling uncertainty refers to the difference between the real-world phenomenon and that represented in a mathematical model. Sources contributing to the modeling uncertainty in finite element (FE) analysis of slope stability are described with emphasis on formulation and interpretation uncertainties. Quantification of modeling uncertainty is difficult and a general methodology does not exist. When a broad data base is available, the overall uncertainty associated with a particular finite element analysis approach can be estimated. Such data bases can be obtained, for example, from centrifuge experiments of subscale model slopes. The evaluation process is illustrated and the importance of modeling uncertainty in probabilistic analysis of slope stability is discussed.     

Computer based support of reasoning in the presence of fuzziness

Many important problems encountered in managerial decision making are unstructured, making them difficult to solve by preset algorithms. Much of the complexity of such problems is due to the reasoning that is needed to construct solution procedures for each problem instance. Thus, any Decision Support System (DSS) designed for such problems should support this reasoning activity, in addition to data access and computational activities. Yet, existing DSS generally do not provide much reasoning support. One of the difficulties faced in building automated systems to support such reasoning is that much of the knowledge typically available for unstructured problems is imprecise, where imprecision may be caused by either fuzziness, uncertainty, or both. In this paper, we address the problem of supporting problem solving with fuzzy knowledge. We develop a formal method for representing fuzzy knowledge, using a framework of mathematical logic. Using this method, fuzziness in all the major constructs needed to describe knowledge can be represented. Relationships can be constructed using fuzzy operators and terms, and components in relationships can be weighted by their relative significance. Also, computational procedures and data access procedures can be directly integrated into the reasoning process. Knowledge thus represented can be manipulated using suitable reasoning mechanisms. The fuzzy inference methods we present, enable the generation of acceptable solutions to problems even when some of the knowledge used is highly imprecise and/or incomplete. Other desirable features, such as explanation of solution procedures and user participation in problem solving, are also supported by our methodology. In addition, we develop bounding procedures, which convey the imprecision in the reasoning process, and also help to reduce the complexity of the search process by pruning poor solutions. Finally, we describe a prototype system which implements the methods developed in this paper. Using example problems processed by the system, we illustrate the versatility of these methods, and also highlight their major features and potential utility in practical applications.