A medical decision support system for disease diagnosis under uncertainty

This paper presents a decision support system (DSS) modeled by a fuzzy expert system (FES) for medical diagnosis to help physicians make better decisions. The proposed system collects comprehensive information about a disease from a group of experts. To this aim, a cross-sectional study is conducted by asking physicians’ expertise on all symptoms relevant to a disease. A fuzzy rule based system is then formed based on this information, which contains a set of significant symptoms relevant to the suspected disease. Linguistic fuzzy values are assigned to model each symptom. The input of the system is the severity level of each symptom reported by patients. The proposed FES considers two approaches to account for uncertain inputs from patients. Two case studies on kidney stone and kidney infection were conducted to demonstrate how the proposed method could be used. A group of patients were used to validate the effectiveness of the proposed expert system. The results show that the proposed fuzzy expert system is capable of diagnosing diseases with a high degree of accuracy and precision comparing to a couple of machine learning methods.     

Simulating engineering systems under uncertainty and optimal decision making

A new technology (technique) that helps construct a mathematical model of a complex engineering system by optimal decision making based on it is given. To construct the model of an engineering system, methods of regressive analysis are used to transform the initial (experimental) data into a vector (multiobjective) mathematical programming problem. To solve it, methods are presented that rely on criteria normalization and principle of guaranteed result. The technique of constructing models of engineering systems, methods of solving the vector mathematical programming problem and optimal decision making are demonstrated by the test examples in Matlab.     

Decision making under uncertainty in a decision support system for the Red River

Decision support systems (DSSs) are increasingly being used in water management for the evaluation of impacts of policy measures under different scenarios. The exact impacts generally are unknown and surrounded with considerable uncertainties. It may therefore be difficult to make a selection of measures relevant for a particular water management problem. In order to support policy makers to make a strategic selection between different measures in a DSS while taking uncertainty into account, a methodology for the ranking of measures has been developed. The methodology has been applied to a pilot DSS for flood control in the Red River basin in Vietnam and China. The decision variable is the total flood damage and possible flood reducing measures are dike heightening, reforestation and the construction of a retention basin. The methodology consists of a Monte Carlo uncertainty analysis employing Latin Hypercube Sampling and a ranking procedure based on the significance of the difference between output distributions for different measures. The mean flood damage in the base situation is about 2.2 billion US$ for the year 1996 with a standard deviation due to parameter uncertainty of about 1 billion US$. Selected applications of the measures reforestation, dike heightening and the construction of a retention basin reduce the flood damage by about 5, 55 and 300 million US$, respectively. The construction of a retention basin significantly reduces flood damage in the Red River basin, while dike heightening and reforestation reduce flood damage, but not significantly.     

Reliability-based design optimization of multidisciplinary system under aleatory and epistemic uncertainty

Structural and Multidisciplinary Optimization - This paper proposes formulations and algorithms for reliability-based design optimization (RBDO) of both single and multidisciplinary systems under...     

Intelligent decision support for effectively evaluating and selecting ships under uncertainty in marine transportation

This paper presents an intelligent decision support system for evaluating and selecting specific ships under uncertainty. A task-oriented procedure is developed for determining the relative importance of the evaluation and selection criteria with respect to a specific shipping task. A fuzzy multicriteria analysis algorithm is developed for determining the overall performance of each ship across all the selection criteria and their associated sub-criteria. An intelligent decision support system capable of integrating the developments above is proposed for facilitating the ship evaluation and selection process. An example is presented to demonstrate the effectiveness of the proposed intelligent decision support system.     

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.     

Decision support modeling for multiple criteria assessments using a likelihood-based consensus ranking method under Pythagorean fuzzy uncertainty

Artificial Intelligence Review - This paper intends to exploit point operator-oriented likelihood measures to constitute a likelihood-based consensus ranking model aimed at conducting multiple...     

场站飞行后勤保障车辆调度仿真优化

为解决飞行后勤保障车辆调度这类NP难问题,在深入分析航空兵场站飞行后勤保障车辆调度过程活动规律的基础上,综合考虑飞行计划要求、场站保障车辆资源约束及飞行后勤保障条例规定3个因素,提出了基于事件调度/时间推进算法的场站飞行后勤保障车辆调度过程Arena仿真模型.针对以往对于该问题的研究过多注重过程的仿真缺乏仿真优化的问题,利用Arena内嵌的VBA技术,通过对VBAblock块编程,建立了基于禁忌搜索算法的上述仿真模型的优化控制系统.以某型飞机首次出动训练后勤保障任务为例,利用所建立的仿真优化模型,分别对估算法及仿真优化法制定的飞行后勤保障车辆调度计划的保障车辆调度过程进行了仿真.通过对仿真结果的对比分析,验证了所建仿真模型及仿真优化系统的科学性、合理性.     

不确定条件下基于仿真的流程工业调度方案优化

针对流程工业需求和产率不确定性问题,提出了一种基于仿真优化架构的调度问题求解方法,调度方案包括生产装置加工量和加工方案的确定,以及装置间侧线分流点系数的规划。建立了带有不确定性概率分布的调度仿真模型,在此基础上,根据流程工业生产调度的特点,应用多种群遗传算法搜索优化方案。并以某炼厂为例,结果表明此方法具有良好的鲁棒性。     

Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty

A variety of model-based approaches for supporting decision-making under deep uncertainty have been suggested, but they are rarely compared and contrasted. In this paper, we compare Robust Decision-Making with Dynamic Adaptive Policy Pathways. We apply both to a hypothetical case inspired by a river reach in the Rhine Delta of the Netherlands, and compare them with respect to the required tooling, the resulting decision relevant insights, and the resulting plans. The results indicate that the two approaches are complementary. Robust Decision-Making offers insights into conditions under which problems occur, and makes trade-offs transparent. The Dynamic Adaptive Policy Pathways approach emphasizes dynamic adaptation over time, and thus offers a natural way for handling the vulnerabilities identified through Robust Decision-Making. The application also makes clear that the analytical process of Robust Decision-Making is path-dependent and open ended: an analyst has to make many choices, for which Robust Decision-Making offers no direct guidance.     

Task constraints and user-system interaction process under personnel decision support

Abstract

An experiment involving a simulated decision support system was carried out to examine the patterns of user-system interaction and decision information utilization under personnel decision support. A computer simulation program of personnel decision support was developed, using actual personnel management data from eight Chinese enterprises, and the process tracing techniques were adopted. Thirty-six subjects (users) participated in this experiment. A 2 × 2 design of task constraints was formulated including two forms of decision information representations (chunking vs. random) and two types of time pressure (3 minutes vs. 1 minute). The results showed that, in interacting with decision support systems, users’ weights of decision information attributes were closely correlated with the types of information search patterns. Under high time-pressure and chunking representation condition, more selective search strategies were adopted with a similar pattern of the sequential search as it was under low time-pressure. The user-system interaction revealed a linear additive process of information utilization. Implications of the results are discussed in relation to the design of effective decision support systems for complex decision situations.     

A multiple-criteria decision problem for optimal management of farm resources under uncertainty - a case study

An attempt is made to minimize the total operational cost and specific energy for rice cultivation, when the constraints relating to the availability of power sources and crop yield are supposed to be stochastic in nature. The Sequential Linear Goal Programming algorithm has been used to solve the resulting multiple objective optimization problem.     

不确定条件下生产调度问题的三阶段决策方法

提出了一种可综合考虑多种不确定因素的生产调度问题三阶段决策方法. 首先分析了不确定条件下的生产调度的决策过程, 根据过程的特点将其分为基本调度、在线调整和补偿三个阶段; 对变量进行分类, 从而与3个阶段相对应; 分别建立了基于情景分析以及子问题最(近)优解的两种三阶段调度数学模型, 并给出了变量的处理方法和阶段性的模型求解方法, 来作为不同阶段的决策依据; 最后以化工批处理过程的短期调度为例, 说明了三阶段调度建模和决策过程的实现方法.     

Fuzzy modeling for intelligent decision making under uncertainty

We consider here the problem of decision making under uncertainty. We suggest an approach for the construction of decision functions which allow for the inclusion of probabilistic information as well as for the inclusion of information about the decision maker's attitude and preferences. Use is made of the fuzzy modeling technology to construct these functions from specifications provided by the decision maker.     

Learning decision tree for ranking

Decision tree is one of the most effective and widely used methods for classification. However, many real-world applications require instances to be ranked by the probability of class membership. The area under the receiver operating characteristics curve, simply AUC, has been recently used as a measure for ranking performance of learning algorithms. In this paper, we present two novel class probability estimation algorithms to improve the ranking performance of decision tree. Instead of estimating the probability of class membership using simple voting at the leaf where the test instance falls into, our algorithms use similarity-weighted voting and naive Bayes. We design empirical experiments to verify that our new algorithms significantly outperform the recent decision tree ranking algorithm C4.4 in terms of AUC.

JUDGES: A descriptive group decision support system for the ranking of items

A set of tools for group decision support are presented. Decision problems involving several decision makers, here-after called judges, that have to rank several alternatives, are considered. The toolbox is called JUDGES. It includes the four following procedures:

• - a hierarchical representation of the judges allows to display the existing conflicts between groups of judges,

• - enhanced box-plots representations of the alternatives are generated in order to detect those that are responsible for the major conflicts,

• - specific advice is issued to each judge in order to reach more easily a consensus,

• - a general framework for a pairwise group preference structure is proposed, and can be used to finalise the decision.

These procedures are embedded in an interactive software, implemented on micro-computer, which currently simulates the use on a network. Actual network implementation is foreseen in the near future. Several applications are presented and future developments are discussed.

A decision support system for computer-aided process planning

A decision support system framework is presented to aid the Decision Maker to resolve conflicting, non-commensurate objectives to set up parameters for machining operations, using a Multiple Criteria Decision Making approach.     

Relating decision under uncertainty and multicriteria decision making models

This short overview paper points out the striking similarity between decision under uncertainty and multicriteria decision making problems, two areas which have been developed in an almost completely independent way until now. This pertains both to additive and non‐additive (including qualitative) approaches existing for the two decision paradigms. This leads to an emphasis on the remarkable formal equivalence between postulates underlying these approaches (like between the “sure‐thing principle” and mutual preferential independence of criteria). This analogy is exploited by surveying classical results as well as very recent advances. This unified view should be fruitful for a better understanding of the postulates underlying the approaches, for cross‐fertilization, and for adapting artificial intelligence uncertainty representation frameworks to preference modelling. © 2000 John Wiley & Sons, Inc.     

Naive Bayes for optimal ranking

This paper presents experimental comparisons between the declarative encodings of various computationally hard problems in Answer Set Programming (ASP) and Constraint Logic Programming over Finite Domains (CLP(FD)). The objective is to investigate how solvers in the two domains respond to different problems, highlighting the strengths and weaknesses of their implementations, and suggesting criteria for choosing one approach over the other. Ultimately, the work in this paper is expected to lay the foundations for a transfer of technology between the two domains, for example by suggesting ways to use CLP(FD) in the execution of ASP.     

Simulation-based optimization of Markov decision processes: An empirical process theory approach

We generalize and build on the PAC Learning framework for Markov Decision Processes developed in Jain and Varaiya (2006). We consider the reward function to depend on both the state and the action. Both the state and action spaces can potentially be countably infinite. We obtain an estimate for the value function of a Markov decision process, which assigns to each policy its expected discounted reward. This expected reward can be estimated as the empirical average of the reward over many independent simulation runs. We derive bounds on the number of runs needed for the convergence of the empirical average to the expected reward uniformly for a class of policies, in terms of the V-C or pseudo dimension of the policy class. We then propose a framework to obtain an ?-optimal policy from simulation. We provide sample complexity of such an approach.