Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

In recent years, advanced magnetic resonance imaging(MRI) techniques, such as magnetic resonance spec-troscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic prob-lems such as brain tumor characterization and grading, as these techniques offer a more detailed and non-invasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical deci-sion support systems(CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually inticle is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be intro-duced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

Probabilistic Analysis and Multicriteria Decision for Machine Assignment Problem with General Service Times

In this paper we carried out a probabilistic analysis for a machine repair system with a general service-time distribution by means of generalized Markov renewal processes. Some formulas for the steady-state performance measures. such as the distribution of queue sizes, average queue length, degree of repairman utilization and so on. are then derived. Finally, the machine repair model and a multiple critcria decision-making method are applied to study machine assignment problem with a general service-time distribution to determine the optimum number of machines being serviced by one repairman.

The Impact of Personality Traits and Problem Structures on Management Decision-Making Outcomes

Since decision-making behavior has been in the focus both from a scientific and a professional position, there seems to be a dispute whether rational or intuitive decision making leads to better outcomes. By now, scholars have agreed that effective organizations do not have the luxury to choose between the ＂applications＂ of intuitive or rational decision making. Instead, they try to understand how different factors like personality traits and problem characteristics influence the decision-making process. Reviewing the literature reveals that personality pre-determination and the structure of problems （e.g., well-structured problems （WSPs） versus ill-structured problems （ISPs）） seem to have a significant impact on the decision-making efficiency. Further, the review also shows that there is a lack of application-oriented empirical studies in this area of research. Therefore, the aim of this research paper is to propose a framework for an empirical study on how personality traits and problem structure influence the decision-making process. First, hypotheses are derived from the literature on how personality pre-determination and behavioral patterns in the decision-making process lead to higher socioeconomic efficiency within certain problem categories. Second, a causal model and a setup for a laboratory experiment are proposed to allow testing the hypotheses. Finally, the conclusions provide an outlook on how this research could support organizations in their decision-making processes.

THE SPECTRALITY DECISION PROBLEM

An efficient algorithm for deciding whether a given integer vector is the spectrum of some Boolean function is presented. The algorithm performs a step-by-step spectral decomposition of the input vector and checks at each step a set of necessary conditions for spectrality for the resulting vectors. The algorithm concludes that the input vector cannot lead to a valid Boolean function as soon as a vector not satisfying the conditions is found, which, as proved in the paper, for almost all cases happens after the first step of the decomposition.

Constraints Based Decision Support for Site-Specific Preliminary Design of Wind Turbines

This study presents a decision-support tool for preliminary design of a horizontal wind turbine system. The function of this tool is to assist the various actors in making decisions about choices inherent to their activities in the field of wind energy. Wind turbine cost and site characteristics are taken into account in the used models which are mainly based on the engineering knowledge. The present tool uses a constraint-modelling technique in combination with a CSP solver (numerical CSPs which are based on an arithmetic interval). In this way, it generates solutions and automatically performs the concept selection and costing of a given wind turbine. The data generated by the tool and required for decision making are: the quality index of solution (wind turbine), the amount of energy produced, the total cost of the wind turbine and the design variables which define the architecture of the wind turbine system. When applied to redesign a standard wind turbine in adequacy with a given site, the present tool proved both its ability to implement constraint modelling and its usefulness in conducting an appraisal.

Approximate Dynamic Programming for Stochastic Resource Allocation Problems

A stochastic resource allocation model, based on the principles of Markov decision processes(MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource requests for both instant and future needs. The considered framework can handle two types of reservations(i.e., specified and unspecified time interval reservation requests), and implement an overbooking business strategy to further increase business revenues. The resulting dynamic pricing problems can be regarded as sequential decision-making problems under uncertainty, which is solved by means of stochastic dynamic programming(DP) based algorithms. In this regard, Bellman’s backward principle of optimality is exploited in order to provide all the implementation mechanisms for the proposed reservation pricing algorithm. The curse of dimensionality, as the inevitable issue of the DP both for instant resource requests and future resource reservations,occurs. In particular, an approximate dynamic programming(ADP) technique based on linear function approximations is applied to solve such scalability issues. Several examples are provided to show the effectiveness of the proposed approach.

Airport gate assignment problem with deep reinforcement learning

With the rapid development of air transportation in recent years,airport operations have attracted a lot of attention.Among them,airport gate assignment problem(AGAP)has become a research hotspot.However,the real-time AGAP algorithm is still an open issue.In this study,a deep reinforcement learning based AGAP(DRL-AGAP)is proposed.The optimization object is to maximize the rate of flights assigned to fixed gates.The real-time AGAP is modeled as a Markov decision process(MDP).The state space,action space,value and rewards have been defined.The DRL-AGAP algorithm is evaluated via simulation and it is compared with the flight pre-assignment results of the optimization software Gurobiand Greedy.Simulation results show that the performance of the proposed DRL-AGAP algorithm is close to that of pre-assignment obtained by the Gurobi optimization solver.Meanwhile,the real-time assignment ability is ensured by the proposed DRL-AGAP algorithm due to the dynamic modeling and lower complexity.

Multi-attribute group decision making method under 2-dimension uncertain linguistic variables

A method is proposed to deal with the uncertain multiple attribute group decision making problems,where 2-dimension uncertain linguistic variables(2DULVs)are used as the reliable way for the experts to express their fuzzy subjective evaluation information.Firstly,in order to measure the 2DULVs more accurately,a new method is proposed to compare two 2DULVs,called a score function,while a new function is defined to measure the distance between two 2DULVs.Secondly,two optimization models are established to determine the weight of experts and attributes based on the new distance formula and a weighted average operator is used to determine the comprehensive evaluation value of each alternative.Then,a score function is used to determine the ranking of the alternatives.Finally,the effectiveness of the proposed method is proved by an illustrated example.

Scheduling problem for hot-rolling seamless steel tube production

Taking the seamless tube plant of Baoshan Iron & Steel Complex in China as the background,we analyze the characters of hot rolling seamless steel tube:multi varieties,low volume,complicated production process,flexible production routes.Then integrated scheduling problem for hot rolling seamless steel tube production is studied,which covers two key points;order-grouping problem and solution method for flowshop/jobshop scheduling problem.On the basis of these two problems,integrated scheduling decision system is developed.The design idea,function flow sheet,data processing method,and functional module of visualized human-computer interactive scheduling system implemented in seamless steel tube plant of Shanghai Baoshan Iron & Steel Complex are described into detail.Compared with manual system,the performance of system shows the applicability and superiority in several criteria.

New Explorations for Decision Trees

Traditionally, the decision tree method is defined and used for finding the optimal solution of a Bayesian decision problem. And it is difficult to use the decision tree method to find the sub-optimal solution, not to mention to rank alternatives. This paper discusses how to use the decision tree method for the alternative selecting and ranking. A practical case study is given to illustrate the applicability.

Model for Solving Fuzzy Multiple Objective Problem

In real world decision making problems, the decision maker has to often optimize more than one objective, which might be conflicting in nature. Also, it is not always possible to find the exact values of the input data and related parameters due to incomplete or unavailable information. This work aims at developing a model that solves a multi objective distribution programming problem involving imprecise available supply, forecast demand, budget and unit cost/ profit coefficients with triangular possibility distributions. This algorithm aims to simultaneously minimize cost and maximize profit with reference to available supply constraint at each source, forecast demand constraint at each destination and budget constraint. An example is given to demonstrate the functioning of this algorithm.

Application of Fuzzy Optimization Method in Decision-Making for Personnel Selection

The paper describes the necessity of application of intelligent technologies to support decisions of more objective problems in human resource management. In this paper, we describe the methodology for personnel selection problem for the vacancy with regard to the importance and nonequivalence of numerous indicators characterizing the alternatives. The specific features of the selection problem are highlighted, immersing the problem into a fuzzy environment. A fuzzy multicriterial model of the personnel selection problem is proposed. A technique of order preference by similarity to ideal solition (TOPSIS), was applied for evaluation and regulation of alternatives. This technique is based on criteria of qualitative character, which are hierarchically structured by multiple experts to intellectually support decisions made in personnel selection problem. Using TOPSIS method and generated criteria system an experiment was conducted for evaluation of the candidates during solution of hiring problems. The obtained and reviewed results were compared with results obtained using in reality.

A New Proof for the Tight Range of Optimal Order Quantities for the Newsboy Problem with Mean and Standard Deviation

In the classical Newsboy problem, we provide a new proof for the tight range of optimal order quantities for the newsboy problem when only the mean and standard deviation of demand are available. The new proof is only based on the definition of the optimal solution therefore it is the most straightforward method. It is also shown that the classical Scarf’s rule is the mid-point of the range of optimal order quantities. This provides an additional understanding of Scarf’s order rule as a distribution free decision.

A Theoretical Approach to the Effective Decision-Making Process

This research develops and elaborates studies done for a contribution to the 2019 PIC International Conference 2019 in Malta, about the decision-making process. Decision-making is the act of choosing between two or more courses of action. In the wider process of problem-solving, decision-making involves choosing between possible solutions to a problem, and these decisions can be made through either an intuitive or reasoned process, or a combination of the two. The study of decision-making processes, to be understood as the role of human factors, becomes particularly interesting in complex organizations. This research aims to analyze how an effective team, within organizations, can develop a more correct and effective decision-making, in order to get an optimal solution, overcoming the typical uncertainty. The paper describes the point of departure of decision in complex, time-pressured, uncertain, ambiguous and changing environments. The use of a leading case (the Tenerife air accident, 1977), will lead us to the desired results, i.e. to demonstrate how an effective decisional process, including team dynamics, can be useful to reduce the risk, present in all decisions, and reduce errors. The case of Tenerife air disaster, confirm our research. In that case, in fact, the group dynamics prove not to have worked. Thus, we can state that if a team approach had been followed instead of a more individual one, the results would probably have been different. The central belief of the research, is that classic decision theory could benefit from a team approach, which reduces the risk that a decision may lead to undesirable consequences. As demonstrated with the case study, within organizations, the decision-making is not a solitary action. Decisions, in fact, are made within a team and in order to be able to function effectively in a group, and manage group situations, there are essential skills. The team can then become a resource for the decisional process and problem solving, but it is necessary to understand the dynamics.

Reinforcement Learning Algorithm for Solving Load Commitment Problem Considering a General Load Model

Under the smart grid paradigm, in the near future all consumers will be exposed to variable pricing schemes introduced by utilities. Hence, there is a need to develop algorithms which could be used by the consumers to schedule their loads. In this paper, load scheduling problem is formulated as a LCP （load commitment problem）. The load model is general and can model atomic and non-atomic loads. Furthermore, it can also take into consideration the relative discomfort caused by delay in scheduling any load. For this purpose, a single parameter ＂uric＂ is introduced in the load model which captures the relative discomfort caused by delay in scheduling a particular load. Guidelines for choosing this parameter are given. All the other parameters of the proposed load model can be easily specified by the consumer. The paper shows that the general LCP can be viewed as multi-stage decision making problem or a MDP （Markov decision problem）. RL （reinforcement learning） based algorithm is developed to solve this problem. The efficacy of the algorithm is investigated when the price of electricity is available in advance as well as for the case when it is random. The scalability of the approach is also investigated.

基于颠覆性技术创新项目评价及立项决策模型的研究

为实现我国颠覆性技术创新项目的精准遴选,促进颠覆性创新产业的培育发展,在分析梳理国内外关于颠覆性技术创新项目遴选、立项决策管理方面研究的基础上,分析对比颠覆性技术创新项目与一般科技计划项目的特征属性差异,借鉴海尔迈耶问题的做法,结合当前政策导向,针对颠覆性技术具有隐蔽性、破坏性、不确定性的特征属性,提出相应的项目遴选角度和评审标准的思路,并利用层次分析法构建颠覆性技术创新项目立项评价的三级指标体系。从项目影响力、项目可能性两方面构建颠覆性技术创新项目的决策矩阵模型,为颠覆性创新项目立项决策提供判断依据。

改进的差分演化算法及其在资金决策中的应用

针对传统的基本差分演化算法存在对参数设置太过于敏感,算法容易陷入早熟收敛、稳定性不强等等缺陷,提出了改进措施,采取了对称算法来保持种群多样性,通过变异个体,对称个体,个体三者进行有效竞争,增强算法的全局搜索能力;同时采用了轮盘赌的选择策略和二次变异算子保证种群个体的有效性与多样性,增加算法的全局稳定性.文中的算法应用于求解资金决策分配问题上,通过模拟仿真,该算法在稳定性、收敛速度、求解的精度方面优于其他被比较的算法.

基于距离信息的追逃策略:信念状态连续随机博弈

追逃问题的研究在对抗、追踪以及搜查等领域极具现实意义.借助连续随机博弈与马尔科夫决策过程(Markov decision process, MDP),研究使用测量距离求解多对一追逃问题的最优策略.在此追逃问题中,追捕群体仅领导者可测量与逃逸者间的相对距离,而逃逸者具有全局视野.追逃策略求解被分为追博弈与马尔科夫决策两个过程.在求解追捕策略时,通过分割环境引入信念区域状态以估计逃逸者位置,同时使用测量距离对信念区域状态进行修正,构建起基于信念区域状态的连续随机追博弈,并借助不动点定理证明了博弈平稳纳什均衡策略的存在性.在求解逃逸策略时,逃逸者根据全局信息建立混合状态下的马尔科夫决策过程及相应的最优贝尔曼方程.同时给出了基于强化学习的平稳追逃策略求解算法,并通过案例验证了该算法的有效性.

管理层结构对国际组织决策偏好的影响——以国际货币基金组织为例

基于委托代理理论,本文收集国际货币基金组织人力资源数据,分析管理层员工结构对国际货币基金组织贷款决策偏好的影响。研究发现,管理层员工结构会对国际货币基金组织贷款决策偏好产生正向影响。借款国国民在国际货币基金组织担任中层管理人员越多,担任总裁办公室主任和地区部门主任的管理岗位的国民越多,担任执行董事和副执行董事人数越多,在欧洲和西半球地区部门任职管理人员越多,国际货币基金组织贷款决策更偏好于该国。进一步研究发现,当借款国与美国外交立场一致,获得西方发达国家经济援助越多,借款国国民担任管理人员越多,国际货币基金组织贷款决策会更偏好于该国。中国在参与国际金融治理时,常处于劣势地位。本文从人力资源视角提供中国参与国际金融治理的新思路和新路径,以期提升中国在国际金融治理中的话语权。

纯粹主义、不纯主义与阈值问题——兼论共同体纯粹主义

随着知识论的实用入侵,正统的纯粹主义知识论面临巨大的挑战。实用入侵者认为,纯粹主义蕴含不可信的不可错论,相反,他们自己所倡导的不纯主义不仅与可错论相容,而且解决了其阈值问题。不纯主义解决阈值问题的路径主要有三条:统一性、相关性和共同体路径。然而,三条路径无一例外都失败了,根由在于实用入侵背后的认知决策论。认知决策论盛行于当代科学哲学和知识论,主张把证成视为一种认知决策,因此错置了信念与接受的认识论地位。与之相对,共同体纯粹主义捍卫信念的认识论地位,把信念视为一种探索世界的基本机制,一种跨越目标和语境的承诺状态。总之,共同体纯粹主义吸收了不纯主义和共同体主义的合理要素,兼顾了知识的理论与实践维度。