A framework for performance measurement during production ramp-up of assembly stations

Production ramp-up is an important phase in the lifecycle of a manufacturing system which still has significant potential for improvement and thereby reducing the time-to-market of new and updated products. Production systems today are mostly one-of-a-kind complex, engineered-to-order systems. Their ramp-up is a complex order of physical and logical adjustments which are characterised by try and error decision making resulting in frequent reiterations and unnecessary repetitions. Studies have shown that clear goal setting and feedback can significantly improve the effectiveness of decision-making in predominantly human decision processes such as ramp-up. However, few measurement-driven decision aides have been reported which focus on ramp-up improvement and no systematic approach for ramp-up time reduction has yet been defined. In this paper, a framework for measuring the performance during ramp-up is proposed in order to support decision making by providing clear metrics based on the measurable and observable status of the technical system. This work proposes a systematic framework for data preparation, ramp-up formalisation, and performance measurement. A model for defining the ramp-up state of a system has been developed in order to formalise and capture its condition. Functionality, quality and performance based metrics have been identified to formalise a clear ramp-up index as a measurement to guide and support the human decision making. For the validation of the proposed framework, two ramp-up processes of an assembly station were emulated and their comparison was used to evaluate this work.     

Representation of decision making processes in an ecological evaluation system

This paper deals with the problems of knowledge representation for a decision support system (DSS) applicable in a dynamic environment. Some special principles concerning environment applications are considered in order to understand better human decision making and behavior. The approach of representing static and dynamic aspects of a system and reflecting them using deep knowledge representation is proposed. The formalization of multiple objective decision making mechanisms is considered. The results of modeling cognitive processes leading to decisions are demonstrated by an example developed during the design stages of an ecological evaluation system.     

Integrated decision support for aviation safety inspectors

This paper presents a systems viewpoint for developing an advanced decision support system for aircraft safety inspectors. Research results from a Federal Aviation Administration (FAA) sponsored project to use neural network and expert systems technology to analyze aircraft maintenance databases are summarized. One of the main objectives of this research is to define more refined “alert” indicators for national comparison purposes that can signal potential problem areas by aircraft type for safety inspector consideration.

Integration aspects are addressed on two levels: (1) integration of the various technical components of the decision support system, and (2) integration of the decision support system with individual behavior, management systems and organizational structure, as well as corporate culture across both formal and informal dimensions. The paper summarizes the creation of strategic “inspection profiles” for aging aircraft and reliability curve fitting for structural components both based upon using neural network technology. Also, the potential use of a model-based expert system to facilitate field inspection diagnostics is presented. Finally, a framework for developing an intelligent decision system to support aircraft safety inspections is proposed that links expert systems, neural networks, as well as a paradigm of the decision making process typically used in unstructured situations.     

Multi-criteria decision analysis for emergency medical service assessment

In emergency management, it is important to arrange the medical resources according to emergency needs. This paper proposes a multi-criteria decision analysis model that combines grey theory and multi-criteria decision making theory to assess the current medical resource situation, find satisfactory solutions, and help the emergency decision makers to take appropriate responses in a timely manner. In addition, the best alternative is identified by the global optimal solution which can provide a theoretical guidance for decision makers to optimize the allocation of medical resources. To validate the proposed model, this paper conducts a case study of medical service assessment in provinces of East China. The results demonstrate that the proposed model can provide objective and comprehensive assessment of medical resources.     

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.     

Multi-confidence rule acquisition and confidence-preserved attribute reduction in interval-valued decision systems

Rule acquisition is one of the most important objectives in the analysis of decision systems. Because of the interference of errors, a real-world decision system is generally inconsistent, which can lead to the consequence that some rules extracted from the system are not certain but possible rules. In practice, however, the possible rules with high confidence are also useful in making decision. With this consideration, we study how to extract from an interval-valued decision system the compact decision rules whose confidences are not less than a pre-specified threshold. Specifically, by properly defining a binary relation on an interval-valued information system, the concept of interval-valued granular rules is presented for the interval-valued decision system. Then, an index is introduced to measure the confidence of an interval-valued granular rule and an implication relationship is defined between the interval-valued granular rules whose confidences are not less than the threshold. Based on the implication relationship, a confidence-preserved attribute reduction approach is proposed to extract compact decision rules and a combinatorial optimization-based algorithm is developed to compute all the reducts of an interval-valued decision system. Finally, some numerical experiments are conducted to evaluate the performance of the reduction approach and the gain of using the possible rules in making decision.     

考虑油膜动态过程的海上溢油应急物资调度优化

针对溢油应急响应中海上油膜所具有的动态特性,综合考虑需求点的时变物资需求、运输网络的不确定性以及物资调度决策与外部决策环境之间的相互作用关系之后,构建了效率目标与成本目标相结合的多目标海上溢油应急物资调度优化模型。根据模型的特点,提出了一种基于鲸鱼算法的求解方法。该算法利用非线性收敛因子克服了算法后期易陷入局部最优的不足,同时还引入小生境共享机制以确保解的多样性。最后,通过仿真案例对模型与算法的有效性与可行性进行了验证。结果表明,该方法可以为决策者提供高质量的决策支持。     

基于区间数的应急物资调度决策模型及算法研究

应急物资调度决策是应急物流管理的重要研究问题之一,是影响应急救援效果的关键环节,加强应急物资调度方法研究,对提升我国应急物流管理水平,提高突发事件的应急保障能力和应对效果具有重要现实意义和理论研究价值.为了提高应急物资调度决策的有效性,基于应急物资调度问题的特点,引入区间数来描述应急物资调度决策中的不确定因素,建立了区间数信息环境下应急物资调度问题的时间最小化决策模型,提出了基于满意度水平的应急物资调度问题有效解的具体算法,最后通过算例分析验证了该方法的有效性.     

Switching defence for switched systems under malicious attacks: A Stackelberg game approach

The security issue of switched systems is researched from a noncooperative dynamic game-theoretic perspective in this paper. A Stackelberg game is developed for the switched autonomous system suffering malicious attacks, and the Stackelberg equilibrium switching and attack strategies are constructed respectively. Afterwards, the proposed game-theoretic approach is extended to switched control systems. A Stackelberg Nash game is consequently established to characterize the hierarchical decision making processes, where the controller and the attacker are the followers who simultaneously make their own decisions, and a sufficient condition is provided for the construction of the Stackelberg Nash equilibrium. Finally, a continuous stirred tank reactor is exploited to validate the effectiveness and applicability of the proposed results.     

面向混合信息的个体优势特征识别与评析方法

针对现有个体优势特征识别与评析方法的不足,考虑指标值以混合信息形式给出,采用理想点效用评价模式,分别针对单层和多层次结构指标体系,围绕个体优势特征识别、个体代理评析、民主代理评析功能,依据竞优思想,根据直觉模糊数的运算规则以及不同类型信息与直觉模糊数的转化关系,提出基于直觉模糊数的面向混合信息值的个体优势特征识别与评析方法,并用算例验证了方法的可行性和实用性。为个体优势特征识别、个体竞优决策及相关组织的竞优管理提供方法与技术支持。     

A survey of quantum-like approaches to decision making and cognition

There has always been a steady interest in how humans make decisions amongst researchers from various fields. Based on this interest, many approaches such as rational choice theory or expected utility hypothesis have been proposed. Although these approaches provide a suitable ground for modeling the decision making process of humans, they are unable to explain the corresponding irrationalities and existing paradoxes and fallacies. Recently, a new formulation of decision theory that can correctly describe these paradoxes and possibly provide a unified and general theory of decision making has been proposed. This new formulation is founded based on the application of the mathematical structure of quantum theory to the fields of human decision making and cognition. It is shown that by applying these quantum-like models, one can better describe the uncertainty, ambiguity, emotions and risks involved in the human decision making process. Even in computational environments, an agent that follows the correct patterns of human decision making will have a better functionality in performing its role as a proxy for a real user. In this paper, we present a comprehensive survey of the researches and the corresponding recent developments. Finally, the benefits of leveraging the quantum-like modeling approaches in computational domains and the existing challenges and limitations currently facing the field are discussed.     

自然灾害情境下的终端供电设施应急抢修决策规划

自然灾害情境造成部分决策信息模糊和应急决策者的消极情绪。考虑消极情绪在自然灾害应急决策的影响,构建了考虑情绪下的前景理论的价值函数。运用反函数原理,根据应急决策者对模糊信息的心理感知价值,构建了其实际值的估计函数,用于解决决策信息模糊下的终端供电设施应急抢修决策规划问题,提出了相应的决策模型和求解算法。采用自主开发的在线实验系统,完成了信息模糊下的电网应急抢修决策实验,验证了决策模型和消极情绪下基于决策者心理感知对模糊信息估值方法的有效性。     

Expert knowledge and computer-aided group decision making: Some pragmatic reflections

Based on a simple outranking method of multi-attribute decision making (MADM), called Dichotomy-Cut, developed in recent years and published elsewhere, this paper describes how to reliably elicit the necessary decision knowledge for implementing various decision modes via this method and its computerized support - UNIDAS 2. The most important features of this method are outlined, namely, how to compose the preference ordered matrices and which are the basic decision rules. Special attention should be paid to specific interactive group decision behaviour of experts. Two examples plainly explain the contents discussed.     

基于粗糙集的突发事件属性约简方法

针对突发事件不完备信息系统中的原始数据存在大量属性冗余的问题,提出一种基于粗糙集的不完备信息系统属性约简方法,以剔除冗余属性,提高知识清晰度。首先对缺失、冗余、噪声以及连续型数据进行预处理;然后进行属性分类,将属性分为条件属性与决策属性,进而建立决策表;最后根据决策表的特征,结合有序加权平均算子的思想,提出一种基于属性重要度的启发式属性约简算法。文末,通过实例验证了方法的正确性与有效性,并利用该方法实现了火灾数据的属性约简。     

电力系统黑启动决策方法比较研究

黑启动作为电力体系安全防御和事故后快速恢复的措施之一,其路径的合理选择对电力系统快速恢复供电具有重要意义。近年来,学者们从不同角度提出了多种黑启动方案决策方法,然而并没有实现各决策方法间的优劣比较。本文引入平均绝对偏差公式,设计了一种黑启动决策方法比较策略,实现了黑启动决策方法的量化比较。在所提比较策略基础上,对常用的黑启动权重确定方法和排序方法进行了实验分析,广东电网上的实验结果表明基于标准差权重和TOPSIS排序的黑启动决策方法具有最高的准确性。本文的价值在于:(1)提出了一种新的比较策略,使黑启动决策方法的量化比较成为可能;(2)通过大量实验确定了一种优化的黑启动决策方法,为后续黑启动决策研究提供了比较基准。     

Evaluación de la habitabilidad de edificios afectados por sismo utilizando la teoría de conjuntos difusos y las redes neuronales artificiales

The fuzzy sets theory and the artificial neural networks are computational intelligence tools which are nowadays widely used in earthquake engineering. This paper develops a method and a computer program which use these computational intelligence tools in order to support the damage and safety evaluation of buildings after strong earthquakes. The model uses an artificial neural network with three layers and a Kohonen learning algorithm; it also uses fuzzy sets in order to manage subjective information such as linguistic qualification of the damage levels in buildings and a fuzzy rule base to support the decision making process. All these techniques are incorporated in the developed computer program. The input data is the subjective and incomplete information about the building state obtained by possibly non experienced evaluators in the field of the seismic performance of buildings. The proposed method is implemented in a tool especially useful in the emergency response phase, when it supports the decision making regarding the building habitability and reparability. In order to show its effectiveness, two examples are included for two different types of buildings.     

Exploring new directions for research in problem structuring methods: on the role of cognitive style

Prior research has argued that cognitive style can have a significant impact on group decision making. In addition, several scholars have proposed that cognitive style can play a key role in the design and use of group decision support systems. However, cognitive style has not received a great deal of attention in the problem structuring methods (PSMs) community. This is surprising, given that PSMs are specifically developed to support a group in their decision making. The purpose of this paper is thus to examine the significance of cognitive style within PSMs. The paper identifies and explores the role of four different cognitive style functions in problem structuring interventions. This analysis is carried out by focusing on the different tasks embedded within a group process supported by PSMs. Implications for the research and practice of PSMs are then discussed.     

Decision maker preferences for information in complex decision making: New directions for OR intervention

Operations research models are used in many business and non-business entities to support a variety of decision making activities, primarily well-defined, operational decisions. This is due to the traditional emphasis of these models on optimal solutions to pre-specified problems. Some attempts have been made to use OR models in support of more complex, strategic decision making. Traditionally, these models have been developed without explicit consideration for the information processing abilities and limitations of the decision makers, who interact with, provide input to, and receive output from such models.Research in judgement and decision making show that human decisions are influenced by a number of factors including, but not limited to, information presentation modes; information content, modes, e.g., quantitative versus qualitative; order effects such as primacy, recency; and simultaneous versus sequential presentation of data.This article presents empirical research findings involving executive business decision makers and their preferences for information in decision making scenarios. These preference functions were evaluated using OR techniques. The results indicate that decision makers view information in different ways. Some decision makers prefer qualitative, narrative, social information, whereas other prefer quantitative, numerical, firm specific information. Results also show that decision making tasks influence the preference structure of decision makers, but that in general, the preference are relatively stable across tasks.The results imply that for OR models to be more useful in support of non-routine decision making, attention needs to be focused on the information content and presentation effects of model inputs and outputs.     

A fast heuristic attribute reduction approach to ordered decision systems

Rough set theory has shown success in being a filter-based feature selection approach for analyzing information systems. One of its main aims is to search for a feature subset called a reduct, which preserves the classification ability of the original system. In this paper, we consider ordered decision systems, where the preference order, a fundamental concept in dominance-based rough set approach, plays a critical role. In recent literature, based on the greedy hill climbing method, many heuristic attribute reduction algorithms are proposed by utilizing significance measures of attributes, and they are extended to deal with ordered decision systems. Unfortunately, they are often time-consuming, especially when applied to deal with large scale data sets with high dimensions. To reduce the complexity, a novel accelerator is introduced in heuristic algorithms from the perspectives of objects and criteria. Based on the new accelerator, the number of objects and the dimension of criteria are lessened thus making the accelerated algorithms faster than their original counterparts while maintaining the same reducts. Experimental analysis shows the validity and efficiency of the proposed methods.     

基于NSGA-II的应急生产任务多目标优化模型及算法研究

重大突发事件发生后,若灾区的应急物资需求不能通过调用储备得到满足,则应急生产将成为灾区应急物资供应的重要保障手段。本文研究重大突发事件发生后应急物资生产任务的优化问题,重点关注原材料生产能力变化对完成应急生产任务的影响,以应急生产任务完成时间最短、完成成本最低为决策目标,研究了包含多个供应商、多个制造商以及单个受灾点的应急物资生产任务多目标规划模型。运用在求解多目标规划问题时具有众多优势的非支配排序多目标遗传算法(NSGA-II)对模型进行求解。通过算例分析,NSGA-II可以得到较好的Pareto前沿,并且可以根据不同情况给出最优的应急物资生产和原材料保障方案。本文的研究还表明,要想更快完成应急生产任务,需要做好原材料、资金、电力、交通等各种要素的配套保障工作。