Incorporating deeply uncertain factors into the many objective search process

This paper proposes an approach for including deeply uncertain factors directly into a multi-objective search procedure, to aid in incorporating divergent quantitative scenarios within the model-based decision support process. Specifically, we extend Many Objective Robust Decision Making (MORDM), a framework for finding and evaluating planning solutions under multiple objectives, to include techniques from robust optimization. Traditional MORDM first optimized a problem under a baseline scenario, then evaluated candidate solutions under an ensemble of uncertain conditions, and finally discovered scenarios under which solutions are vulnerable. In this analysis, we perform multiple multi-objective search trials that directly incorporate these discovered scenarios within the search. Through the analysis, we have created multiple problem formulations to show how methodological choices of severe scenarios affect the resulting candidate planning solutions. We demonstrate the approach through a water planning portfolio example in the Lower Rio Grande Valley of Texas.     

Resolving Conflicting Insights in Asynchronous Collaborative Visual Analysis

Analyzing large and complex datasets for critical decision making can benefit from a collective effort involving a team of analysts. However, insights and findings from different analysts are often incomplete, disconnected, or even conflicting. Most existing analysis tools lack proper support for examining and resolving the conflicts among the findings in order to consolidate the results of collaborative data analysis. In this paper, we present CoVA, a visual analytics system incorporating conflict detection and resolution for supporting asynchronous collaborative data analysis. By using a declarative visualization language and graph representation for managing insights and insight provenance, CoVA effectively leverages distributed revision control workflow from software engineering to automatically detect and properly resolve conflicts in collaborative analysis results. In addition, CoVA provides an effective visual interface for resolving conflicts as well as combining the analysis results. We conduct a user study to evaluate CoVA for collaborative data analysis. The results show that CoVA allows better understanding and use of the findings from different analysts.     

Bridging the Data Analysis Communication Gap Utilizing a Three‐Component Summarized Line Graph

Communication‐minded visualizations are designed to provide their audience—managers, decision‐makers, and the public—with new knowledge. Authoring such visualizations effectively is challenging because the audience often lacks the expertise, context, and time that professional analysts have at their disposal to explore and understand datasets. We present a novel summarized line graph visualization technique designed specifically for data analysts to communicate data to decision‐makers more effectively and efficiently. Our summarized line graph reduces a large and detailed dataset of multiple quantitative time‐series into (1) representative data that provides a quick takeaway of the full dataset; (2) analytical highlights that distinguish specific insights of interest; and (3) a data envelope that summarizes the remaining aggregated data. Our summarized line graph achieved the best overall results when evaluated against line graphs, band graphs, stream graphs, and horizon graphs on four representative tasks.     

A Bayesian network model for likelihood estimations of acquirement of critical software vulnerabilities and exploits

ContextSoftware vulnerabilities in general, and software vulnerabilities with publicly available exploits in particular, are important to manage for both developers and users. This is however a difficult matter to address as time is limited and vulnerabilities are frequent.ObjectiveThis paper presents a Bayesian network based model that can be used by enterprise decision makers to estimate the likelihood that a professional penetration tester is able to obtain knowledge of critical vulnerabilities and exploits for these vulnerabilities for software under different circumstances.MethodData on the activities in the model are gathered from previous empirical studies, vulnerability databases and a survey with 58 individuals who all have been credited for the discovery of critical software vulnerabilities.ResultsThe proposed model describes 13 states related by 17 activities, and a total of 33 different datasets.ConclusionEstimates by the model can be used to support decisions regarding what software to acquire, or what measures to invest in during software development projects.     

Integrated risk and multi-objective optimization of energy systems

This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sold to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.     

Bad-scenario-set Robust Optimization Framework With Two Objectives for Uncertain Scheduling Systems

This paper proposes a robust optimization framework generally for scheduling systems subject to uncertain input data, which is described by discrete scenarios. The goal of robust optimization is to hedge against the risk of system performance degradation on a set of bad scenarios while maintaining an excellent expected system performance. The robustness is evaluated by a penalty function on the bad-scenario set. The bad-scenario set is identified for current solution by a threshold, which is restricted on a reasonable-value interval. The robust optimization framework is formulated by an optimization problem with two conflicting objectives. One objective is to minimize the reasonable value of threshold, and another is to minimize the measured penalty on the bad-scenario set. An approximate solution framework with two dependent stages is developed to surrogate the biobjective robust optimization problem. The approximation degree of the surrogate framework is analyzed. Finally, the proposed bad-scenario-set robust optimization framework is applied to a scenario job-shop scheduling system. An extensive computational experiment was conducted to demonstrate the effectiveness and the approximation degree of the framework. The computational results testified that the robust optimization framework can provide multiple selections of robust solutions for the decision maker. The robust scheduling framework studied in this paper can provide a unique paradigm for formulating and solving robust discrete optimization problems.      

A Multiple Objective Linear Programming Approach to Power Generation Planning with Demand-Side Management (DSM)

The optimization of an economic indicator has traditionally been the sole objective function of mathematical programming models for power generation expansion planning. Recently, however, other evaluation aspects, such as environmental concerns, were also given an explicit role as objective functions in mathematical models. Models become thus more realistic so that decision makers are able to grasp the inherent conflicts and trade-offs among the distinct objectives in selecting a best compromise plan. A significant change in the planning processes has also occurred concerning new planning methodologies integrating demand-side management (DSM) techniques, in an attempt to change the levels and forms of electricity use by the consumers. This paper presents a multiple objective linear programming model for power generation expansion planning incorporating DSM. The objective functions are the total expansion cost, the environmental impact associated with the installed power capacity and the environmental impact associated with the energy output. DSM is included by modelling it as a new generating group along with the generating alternatives from the supply side. Five categories of constraints are considered related to the reliability of the supply system, the availability of the generating units, the capacity of the DSM-equivalent generating group, the total capacity installed throughout the planning period, the pollutant emissions. Some results are presented derived by using an interactive method, aimed at assisting decision makers in a progressive and selective search of good compromise solutions.     

Constructing,evaluating and visualizing value and utility functions for decision support

Formal methods of decision analysis can help to structure a decision making process and to communicate reasons for decisions transparently. Objectives hierarchies and associated value and utility functions are useful instruments for supporting such decision making processes by structuring and quantifying the preferences of decision makers or stakeholders. Common multi-attribute decision analysis software products support such decision making processes but they can often not represent complex preference structures and visualize uncertainty induced by uncertain predictions of the consequences of decision alternatives. To stimulate strengthening these aspects in decision support processes, we propose a set of visualization tools and provide a software package for constructing, evaluating and visualizing value and utility functions. In these tools we emphasize flexibility in value aggregation schemes and consideration and communication of prediction uncertainty. The use of these tools is demonstrated with an illustrative example of river management decision support.     

An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty

In this study, an interactive decision support system (UREM-IDSS) has been developed based on an inexact optimization model (UREM, University of Regina Energy Model) to aid decision makers in planning energy management systems. Optimization modeling, scenario development, user interaction, policy analysis and visual display are seamlessly integrated into the UREM-IDSS. Uncertainties in energy-related parameters are effectively addressed through the interval linear programming (ILP) approach, improving the robustness of the UREM-IDSS for real-world applications. Thus, it can be used as an efficient tool for analyzing and visualizing impacts of energy and environmental policies, regional/community sustainable development strategies, emission reduction measures and climate change in an interactive, flexible and dynamic context. The Region of Waterloo has been selected to demonstrate the applicability and capability of the UREM-IDSS. A variety of scenarios (including a reference case) have been identified based on different energy management policies and sustainable development strategies for in-depth analysis of interactions existing among energy, socio-economy, and environment in the Region. Useful solutions for the planning of energy management systems have been generated, reflecting complex tradeoffs among energy-related, environmental and economic considerations. Results indicate that the UREM-IDSS can be successfully used for evaluating and analyzing not only the effects of an individual policy scenario, but also the variations between different scenarios compared with a reference case. Also, the UREM-IDSS can help tackle dynamic and interactive characteristics of the energy management system in the Region of Waterloo, and can address issues concerning cost-effective allocation of energy resources and services. Thus, it can be used by decision makers as an effective technique in examining and visualizing impacts of energy and environmental policies, regional/community development strategies, emission reduction measures, and climate change within an integrated and dynamic framework.     

Multicriteria decision making under uncertainty: a visual approach

Decision problems at the strategic level tend to have multiple criteria and outcomes that are uncertain. Many of the current decision‐making tools are too simplistic to incorporate the important features. This paper considers a multicriteria decision‐making scenario in which the outcomes of the decisions, evaluated on different criteria, are uncertain. The main contribution of this paper is the presentation of a tool that enables decision makers to visualize the expected payoff and likelihood that the payoff of a decision does not fall short of a preset target value. Furthermore, it presents decision makers with a tool that shows the tradeoff between expected payoff and downside risk. A variety of solution techniques are suggested that build upon this visualization.     

Scenario Development and Practical Decision Making under Uncertainty: Application to Requirements Engineering

In this paper, we address the question of how flesh and blood decision makers manage the combinatorial explosion in scenario development for decision making under uncertainty. The first assumption is that the decision makers try to undertake ‘robust’ actions. For the decision maker a robust action is an action that has sufficiently good results whatever the events are. We examine the psychological as well as the theoretical problems raised by the notion of robustness. Finally, we address the false feeling of decision makers who talk of ‘risk control’. We argue that ‘risk control’ results from the thinking that one can postpone action after nature moves. This ‘action postponement’ amounts to changing look-ahead reasoning into diagnosis. We illustrate these ideas in the framework of software development and examine some possible implications for requirements analysis.     

Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA results are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the results of basic DEA models. The paper formally shows how the results of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides DEA results which are consistent with the framework and are ready-to-analyze with data mining tools, thanks to their specially designed table-based structures. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.     

Exploring scientific information for policy making under deep uncertainty

Each actor evaluating potential management strategies brings her/his own distinct set of objectives to a complex decision space of system uncertainties. The diversity of these objectives and uncertainties requires detailed and rigorous analyses that respond to multifaceted challenges. The utility of this information depends on the accessibility of scientific information to decision makers. This paper demonstrates data visualization tools for presenting scientific results to decision makers in two case studies, La Paz/El Alto, Bolivia, and Yuba County, California. Visualization output from the case studies combines spatiotemporal, multivariate and multirun/multiscenario information to produce information corresponding to the objectives and uncertainties described by key actors. These tools can manage complex data and distill scientific information into accessible formats. Using the visualizations, scientists and decision makers can navigate the decision space and potential objective trade-offs to facilitate discussion and consensus building. These efforts can help identify stable negotiated agreements between different stakeholders.     

Robust optimization models for project scheduling with resource availability cost

We address a project scheduling problem with resource availability cost for which the activity durations are uncertain. The problem is formulated within the robust optimization framework, where uncertainty is modeled via a set of scenarios. The proposed solution method is based on the scatter search methodology and employs advanced strategies, such as dynamic updating of the reference set, a frequency-based memory mechanism, and path relinking. A multistart heuristic was also developed and comparative results are reported. The tradeoffs for risk-averse decision makers are discussed.     

Ensemble mating selection in evolutionary many-objective search

Traditional multi-objective evolutionary algorithms have encountered difficulties when handling many-objective problems. This is due to the loss of selection pressure incurred by the growing size of objective space. A variety of environmental selection operators have been proposed to address the issue, each has its distinct benefits and drawbacks. We develop a novel ensemble framework to enhance the effectiveness and robustness of many-objective optimization. The framework incorporates multiple environmental selection operators to guide the search, which are then viewed as voters to construct a mating pool. We design an ensemble mating selection strategy that makes decisions based on the preference information provided by the voters: individuals elected by more voters will be assigned larger possibilities to enter the mating pool. By doing so, high quality offspring can be reproduced from the elected promising candidates. To accommodate the multiple selection operators for voting, the framework maintains multiple parallel populations, where each population is updated by one of the selection operators. An instantiation of the framework with three popular operators is presented as a prime example. Extensive experiments have been conducted on a number of many-objective problems to examine the effectiveness of the proposed approach. Experimental results show that the mating selection strategy is capable of improving the quality of the obtained solution set.     

适用于多模式虚拟学习的交互医学可视化及模拟

医学图像及其衍生的知识是医学教育中的一个关键学习部分，透过不同的图像模式，町以使医生及医科学生更有效地学习人体的体内结构．而计算机可视化技术最近的发展正满足了医生、外科医生及医科学生与日俱增的需求，为深入地了解人体生理学及病理学提供了宝贵的资料．本文提出了一个网上交互医学学习系统，并提供了一个虚拟现实工具包以支持二维医学图像序列观察、体数据和表面数据的三维可视化、目视放大镜．以及医学数据的交互变形模拟．其中人体组织物理特性的模拟以有限元法为基准．通过优化而获得有关的变形模型参数．该工具包可应用于个性化的二维及三维数据场可视化、优化的学习及手术规划和在线的交互医学教育．     

Decision Analytics mit Heatmap-Visualisierung von mehrschrittigen Ensembledaten

Today’s forecasting techniques, which are integrated into several information systems, often use ensembles that represent different scenarios. Aggregating these forecasts is a challenging task: when using the mean or median (common practice), important information is lost, especially if the underlying distribution at every step is multimodal. To avoid this, the authors present a heatmap visualization approach. It is easy to visually distinguish regions of high activity (high probability of realization) from regions of low activity. This form of visualization allows to identify splitting paths in the forecast ensemble and adds a “third alternative” to the decision space. Most forecast systems only offer “up” or “down”: the presented heatmap visualization additionally introduces “don’t know”. Looking at the heatmap, regions can be identified in which the underlying forecast model cannot predict the outcome. The authors present a software prototype with interactive visualization to support decision makers and discuss the information gained by its use. The prototype has already been presented to and discussed with researchers and practitioners.     

生物特征信息处理开发包BITK及其应用平台

由于人类固有的生物特征能用来进行可靠的身份认证和识别,近10年来生物特征识别已经成为研究热点.为了对生物特征识别研究提供软件支持,设计研发了生物特征信息处理开发包 (Biometric Information Processing Toolkit,BITK).BITK是一个C 软件开发包.它以非线性数据流模型来组织整体计算流程,提供了一个可扩展、可重用的框架来整合生物特征识别领域的算法和数据结构.BITK还设计了一个精干的可视化框架以及一个管理各种生物特征采集仪的一致性框架.此外,在BITK基础上研发了生物特征信息处理平台 (BITK-based Application Platform,BITKAPP).该平台通过灵活的plug-in架构和友好的用户界面,充分发挥BITK的功能,并降低了BITK的使用门槛.在实际应用中,BITK和BITKAPP向研究人员提供了一套高效率的研究、实验及开发手段,并且作为支撑平台完成了第一届生物特征识别竞赛(the 1st Biometric Verification Competition)的多模态数据采集任务.     

Multiproduct aggregate production planning with fuzzy demands and fuzzy capacities

Given the uncertain market demands and capacities in production environment, this paper discusses some practical approaches to modeling multiproduct aggregate production planning problems with fuzzy demands, fuzzy capacities, and financial constraints. By formulating the fuzzy demand, fuzzy equation, and fuzzy capacities, a fuzzy production-inventory balance equation for single period and a dynamic balance equation are formulated as fuzzy/soft equations and they represent the possibility levels of meeting the market demands. Using this formulation and interpretation, a fuzzy multiproduct aggregate production planning model is developed, and its solutions using parametric programming, best balance and interactive techniques are introduced to cater to different scenarios under various decision making preferences. Using the proposed models and techniques, first, the decision maker can select a preferred production plan with a common satisfaction level or different combinations of preferred possibility level and satisfaction levels, according to the market demands and available production capacities, and second, the obtained structure of the optimal solution can help decision maker in aggregate production planning. The decision maker can also make a preferred and reasonable production plan corresponding to one's most concerned criteria. Hence, decision makers not only can come up with a reasonable aggregate production plan with minimum efforts, but also have more choices of making a preferred aggregate plan based on his most concerned criteria. These models can effectively enhance the capability of an aggregate plan to give feasible family disaggregation plans under different scenarios with fuzzy demands and capacities. Simulation and the results of analysis on the proposed techniques are also given in detail in this paper.     

DAVID influence diagram processing system in environmental management

The DAVID influence diagram processing system for the Apple Macintosh is reviewed, and tested with a model for restoration management of a eutrophicated lake. A Bayesian influence diagram is a network of nodes connected with directed arcs. The nodes represent probabilistic variables, decisions and objective functions. A diagram describes the flow of information, causality and probabilistic dependencies in a system. In this framework, a management problem can be formulated, analyzed and evaluated in an intuitively clear domain. In environmental management, the decision making often involves a large number of uncertain, interrelated quantities and attributes with information of very varying quality. The approach and the software appeared to be very applicable, specially when cognitive inference has to be incorporated with numerical information. The structural formulation of the problem was straightforward, and so was the evaluation including sensitivity tests and risk analysis.