基于“人本服务”的决策问题算法与模型研究
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摘要
针对现有决策支持系统在使用过程中人是系统服务被动的接受者，系统产生的结果与使用者理想中的决策方案大相径庭，不同决策者对同一个系统的评价不同，因决策系统的可靠性不强而弃用等问题，本文基于人是提升决策效能的关键要素的观点，提出基于“人本服务”的决策问题算法与模型研究，强调人也是一种“决策因素”，应充分考虑人的偏好对决策结果的影响。基于“人本服务”决策问题算法与模型研究目的是为了建立一个完善的基于“人本服务”的人机协同决策支持系统提供算法与模型服务。
融入“人本服务”的决策问题是传统决策问题形式上的一种创新和突破，主要研究以“人本服务”理念为基础的决策问题的算法与模型，提出“人本服务”的概念，分析“人本服务”的属性和内涵，“人本服务”的偏好模型和参数体系，以此为基础研究融入人的偏好活动的决策属性赋权和信息集结与排序算法，决策任务分解策略和求解流程算法，决策目标调整规则，决策者满意度度量函数及决策效果评估算法，决策流程模型的提取与优化、决策者之间社会关系网模型，偏好模型的自学习和自适应机理等。为基于“人本服务”的人机协同决策支持系统的发展提供先进的算法支撑与模型方案，切实提高决策系统的人性化服务功能与可靠性。
本文中具有创新性的研究成果如下：
（1）“人本服务”决策问题赋权研究。从易于决策者选择的主观角度与决策对象之间相互博弈的客观角度，分别提出基于属性值相似关系与优势关系的赋权算法；指出决策对象与理想对象之间的相似度与决策对象的优势度之间存在等价关系；利用各个决策对象与理想对象的相似度大小及各个决策对象的优势度大小进行排序择优。
（2）“人本服务”决策方案排序研究。提出区间数与联系数之间相互转化的算法及用联系数形式度量的可能度表达式；在基于最小离差、可能度、相似度等基础上，提出5种等价的确定属性值与决策方案之间优势关系的算法；针对具有相同可能度的不同区间数的优势顺序判定问题，提出风险偏好型、风险中立型、风险规避型预期理论模型；提出综合加权优势度的信息集结算法对决策方案进行排序择优。
（3）“人本服务”决策任务分解策略与流程算法研究。提出2种分解策略：先对决策者分类再求解策略，根据决策者对风险偏好的不同将其归类（风险偏好型、风险中立型、风险规避型），根据决策者的偏好特征选择适合相应的算法，决策结果自动匹配相应的偏好和决策任务求解流程。边对决策者分类边求解策略：从客观角度提取有效属性并赋权，根据决策者的决策活动的选择判断逐步嵌入决策者偏好，动态地、循序渐进地一边进行决策任务的分解，任务分解完成的同时也得到了相应决策结果。
（4）“人本服务”决策目标调整与可靠性研究。通过“人本服务”决策结果与决策者心理预期的对比，分析人的行为偏好对决策结果的影响；对效益型、成本型、中间型指标构建指标期望函数，通过计算每个备选方案在决策者心理阈值属性上的满意度，调整决策目标并组建新的决策表；以决策者满意度为原则对属性赋权及信息集结得到备选方案的决策者综合满意程度，进一步对备选方案排序择优，提高决策结果的可靠性。
（5）“人本服务”决策流程提取与优化研究。提出基于邻接矩阵的决策流程提取算法，利用模型中的频率、时间、决策活动与决策者信息，预测流程图中各层节点与下层节点连接构成的边及“边权”，计算流程中前后活动之间的条件概率、每条路径的概率及完成时间，确定决策活动在决策者之间流动的路径、概率、时间，通过预测对决策流程进行优化；针对群决策问题确定决策者之间相互依赖度的社会关系网，分析决策者的偏好和使用习惯，不断完善其偏好模型，并据以优化决策问题中风险测度算法参数体系。
In the existing decision-making support systems, humans are oftentimes the submissive recipients,and thus, the results produced by these systems sometimes differ significantly with the users’ idealdecision-making scheme, which greatly impacts the reliability and reputation of these systems. Ashuman is a key element in improving the effect and efficiency of decision making problems, wepropose a “human-centered service” model for complex decision-making problems, highlighting thathuman is also an indispensable “decision-making factor” and human preferences could also affect thedecision-making results considerably. The goal of studying on methods and models based on“human-Centered Service”(HCS) decision making problems is to build a complete system conceptabout computer-aided human centric decision support system, which can provide advanced methodsand models support for the development of human-machine coordination decision support system.
The decision making problem with human centered service is one of the innovation of the forms intraditional decision problem.“Human-Centered Service” decision making problems are oneinnovation and breakthrough for traditional decision problem. The research primarily focuses on themethod and model of decision making problems based on the HCS. To improve the human servicesfunctions and reliability, this paper primarily focuses on several significant aspects, including designprocesses of decision-making problem recognition, task-driven decision decomposition strategies andthe mutual transformation methods among these different strategies, decision-making schemeadjustment algorithms and effect assessment models based on decision-makers psychologicalthreshold, and decision-makers psychological expectation rules and information fusion methods basedon decision-makers satisfaction, etc. The new methods and models break through the existingarchitecture for decision support system, and provide more efficient auxiliary decision.
In this paper the main innovative research results are shown as follows:
(1) Study on Criteria Weights for Decision Making Problems Based on “Human-Centered Service”.This paper mainly focuses on the uncertainty of criteria weights problem. In this section, we firstpropose two new methods to obtain the criteria weights based on the similarity relation andadvantageous relation, respectively. Then, we points out the similarity relationship and advantageousrelationship between the decision making alternative(s) and the ideal decision alternative has therelation of equivalence with the probability measure of criteria value. Later, with the help ofmaximizing deviation algorithm rules, we propose the criteria weights based on the similarity andadvantageous of criteria values, then following the similarity and advantageous between decision making alternative and the ideal standard, we rank and pick over all decision making alternatives.
(2) Study on Alternatives Ranking for Decision Making Problems Based on “Human-CenteredService”. In this section, first, we put forward transformation methods for interval number intoconnection number, and use the connection number propose a new possibility degree formula forinterval numbers. Then, we evaluate the magnitude of interval number for decision making advantagematrix according to its possibility degree. Later, we research on the chanellege that different intervalnumbers have the same possibility degree values. Third, we divide the decision makers into threedifferent types according to their risk preferences and we proposed three risk preferences assumptionmodels corresponding to different type decision makers to determine the advantage relation forinterval numbers. Finally, we propose advantage relation, advantage degree of the alternatives,advantage matrix to rank all the alternatives, and find out the best one based on the weightedcombinatorial advantage values (WCAV) of alternatives.
(3) Study on Decomposition Strategies ans Criteria Reduction in Multi-Criteria Decision Making(MCDM) Problems Based on“Human-Centered Service”. In this paper, we propose two differentstrategies to address the “large decision table”(e.g. a large number of criteria) challenge in multiplecriteria decision making with interval number. One strategy: we first classify decision makers (DMs)into three types according to their risk preferences, then we use different methods to find usefulcriteria, obtain criteria weight for useful criteria, information fusion, and rank alternatives accordingto weighted combinatorial advantage values (WCAV) corresponding to different types DMs, then weselect the most desirable choice(s) for different risk preference decision makers. Another strategy: wefirst find out the useful criteria and obtain criteria depending on the data. Then, using the differentmethod to rank and select the most desirable choice(s) corresponding to different types DMs.
(4) Study on Revisiting and Reliability Targets for Multiple Criteria Decision Making ProblemBased on “Human-Centered Service”. The idea in this section is that the decision maker’s utility orvalue may not depend on the levels of performance on different criteria, but instead on whether thelevels meet a target or threshold on one or more criteria. First, we build the expectation functions ofdecision makers for three different type criteria. Then we filter off the useless alternatives and build anew decision table that can reach to the basic satisfaction degree on those criteria that decisionmaker’s with an expection vaule or interval. Second, we propose a new method to obtain criteriaweights using the satisfaction degree of the decision makers. Third, we also propose a new definitionfor satisfaction degree of alternatives. The best alternative(s) in decision making found by thetraditional ranking methods does not have an equivalence relation with the alternative(s) that have thebiggest combinatorial satisfaction degree of decision makers.
(5) Study on Decision Processes Abstraction and Optimization Based on Process Mining for “Human-Centered Service”. In this section, we use adjacency matrix to extract3types of annotationsinformation (i.e., frequencies, remaining times, activities) and use them to construct new processmodels from the event logs. First, a new process model based on adjacency matrix is proposed.Second, by adding the stage and frequency for every activity into the matrix, another new processmodel based on stage adjacency matrix is further proposed to avoid the possible loops or self-loops.Third, based on the second new model with a multi-stage structure, we compute the conditionalprobability from every stage to next stage through the frequency. These new process models can beused to predict what will actually happen, how possible to reach the next activity, and how soon forthe ongoing process instances to finish. Later, another two handover process models are extractedbased on person adjacency matrix abstraction such as how work transfer from one person to another.The new handover process models can be used to optimize the handover business process throughprobability and time prediction. We use the decision process models to analyze decision maker’spreferences and improve the human preference models.
融入“人本服务”的决策问题是传统决策问题形式上的一种创新和突破，主要研究以“人本服务”理念为基础的决策问题的算法与模型，提出“人本服务”的概念，分析“人本服务”的属性和内涵，“人本服务”的偏好模型和参数体系，以此为基础研究融入人的偏好活动的决策属性赋权和信息集结与排序算法，决策任务分解策略和求解流程算法，决策目标调整规则，决策者满意度度量函数及决策效果评估算法，决策流程模型的提取与优化、决策者之间社会关系网模型，偏好模型的自学习和自适应机理等。为基于“人本服务”的人机协同决策支持系统的发展提供先进的算法支撑与模型方案，切实提高决策系统的人性化服务功能与可靠性。
本文中具有创新性的研究成果如下：
（1）“人本服务”决策问题赋权研究。从易于决策者选择的主观角度与决策对象之间相互博弈的客观角度，分别提出基于属性值相似关系与优势关系的赋权算法；指出决策对象与理想对象之间的相似度与决策对象的优势度之间存在等价关系；利用各个决策对象与理想对象的相似度大小及各个决策对象的优势度大小进行排序择优。
（2）“人本服务”决策方案排序研究。提出区间数与联系数之间相互转化的算法及用联系数形式度量的可能度表达式；在基于最小离差、可能度、相似度等基础上，提出5种等价的确定属性值与决策方案之间优势关系的算法；针对具有相同可能度的不同区间数的优势顺序判定问题，提出风险偏好型、风险中立型、风险规避型预期理论模型；提出综合加权优势度的信息集结算法对决策方案进行排序择优。
（3）“人本服务”决策任务分解策略与流程算法研究。提出2种分解策略：先对决策者分类再求解策略，根据决策者对风险偏好的不同将其归类（风险偏好型、风险中立型、风险规避型），根据决策者的偏好特征选择适合相应的算法，决策结果自动匹配相应的偏好和决策任务求解流程。边对决策者分类边求解策略：从客观角度提取有效属性并赋权，根据决策者的决策活动的选择判断逐步嵌入决策者偏好，动态地、循序渐进地一边进行决策任务的分解，任务分解完成的同时也得到了相应决策结果。
（4）“人本服务”决策目标调整与可靠性研究。通过“人本服务”决策结果与决策者心理预期的对比，分析人的行为偏好对决策结果的影响；对效益型、成本型、中间型指标构建指标期望函数，通过计算每个备选方案在决策者心理阈值属性上的满意度，调整决策目标并组建新的决策表；以决策者满意度为原则对属性赋权及信息集结得到备选方案的决策者综合满意程度，进一步对备选方案排序择优，提高决策结果的可靠性。
（5）“人本服务”决策流程提取与优化研究。提出基于邻接矩阵的决策流程提取算法，利用模型中的频率、时间、决策活动与决策者信息，预测流程图中各层节点与下层节点连接构成的边及“边权”，计算流程中前后活动之间的条件概率、每条路径的概率及完成时间，确定决策活动在决策者之间流动的路径、概率、时间，通过预测对决策流程进行优化；针对群决策问题确定决策者之间相互依赖度的社会关系网，分析决策者的偏好和使用习惯，不断完善其偏好模型，并据以优化决策问题中风险测度算法参数体系。
In the existing decision-making support systems, humans are oftentimes the submissive recipients,and thus, the results produced by these systems sometimes differ significantly with the users’ idealdecision-making scheme, which greatly impacts the reliability and reputation of these systems. Ashuman is a key element in improving the effect and efficiency of decision making problems, wepropose a “human-centered service” model for complex decision-making problems, highlighting thathuman is also an indispensable “decision-making factor” and human preferences could also affect thedecision-making results considerably. The goal of studying on methods and models based on“human-Centered Service”(HCS) decision making problems is to build a complete system conceptabout computer-aided human centric decision support system, which can provide advanced methodsand models support for the development of human-machine coordination decision support system.
The decision making problem with human centered service is one of the innovation of the forms intraditional decision problem.“Human-Centered Service” decision making problems are oneinnovation and breakthrough for traditional decision problem. The research primarily focuses on themethod and model of decision making problems based on the HCS. To improve the human servicesfunctions and reliability, this paper primarily focuses on several significant aspects, including designprocesses of decision-making problem recognition, task-driven decision decomposition strategies andthe mutual transformation methods among these different strategies, decision-making schemeadjustment algorithms and effect assessment models based on decision-makers psychologicalthreshold, and decision-makers psychological expectation rules and information fusion methods basedon decision-makers satisfaction, etc. The new methods and models break through the existingarchitecture for decision support system, and provide more efficient auxiliary decision.
In this paper the main innovative research results are shown as follows:
(1) Study on Criteria Weights for Decision Making Problems Based on “Human-Centered Service”.This paper mainly focuses on the uncertainty of criteria weights problem. In this section, we firstpropose two new methods to obtain the criteria weights based on the similarity relation andadvantageous relation, respectively. Then, we points out the similarity relationship and advantageousrelationship between the decision making alternative(s) and the ideal decision alternative has therelation of equivalence with the probability measure of criteria value. Later, with the help ofmaximizing deviation algorithm rules, we propose the criteria weights based on the similarity andadvantageous of criteria values, then following the similarity and advantageous between decision making alternative and the ideal standard, we rank and pick over all decision making alternatives.
(2) Study on Alternatives Ranking for Decision Making Problems Based on “Human-CenteredService”. In this section, first, we put forward transformation methods for interval number intoconnection number, and use the connection number propose a new possibility degree formula forinterval numbers. Then, we evaluate the magnitude of interval number for decision making advantagematrix according to its possibility degree. Later, we research on the chanellege that different intervalnumbers have the same possibility degree values. Third, we divide the decision makers into threedifferent types according to their risk preferences and we proposed three risk preferences assumptionmodels corresponding to different type decision makers to determine the advantage relation forinterval numbers. Finally, we propose advantage relation, advantage degree of the alternatives,advantage matrix to rank all the alternatives, and find out the best one based on the weightedcombinatorial advantage values (WCAV) of alternatives.
(3) Study on Decomposition Strategies ans Criteria Reduction in Multi-Criteria Decision Making(MCDM) Problems Based on“Human-Centered Service”. In this paper, we propose two differentstrategies to address the “large decision table”(e.g. a large number of criteria) challenge in multiplecriteria decision making with interval number. One strategy: we first classify decision makers (DMs)into three types according to their risk preferences, then we use different methods to find usefulcriteria, obtain criteria weight for useful criteria, information fusion, and rank alternatives accordingto weighted combinatorial advantage values (WCAV) corresponding to different types DMs, then weselect the most desirable choice(s) for different risk preference decision makers. Another strategy: wefirst find out the useful criteria and obtain criteria depending on the data. Then, using the differentmethod to rank and select the most desirable choice(s) corresponding to different types DMs.
(4) Study on Revisiting and Reliability Targets for Multiple Criteria Decision Making ProblemBased on “Human-Centered Service”. The idea in this section is that the decision maker’s utility orvalue may not depend on the levels of performance on different criteria, but instead on whether thelevels meet a target or threshold on one or more criteria. First, we build the expectation functions ofdecision makers for three different type criteria. Then we filter off the useless alternatives and build anew decision table that can reach to the basic satisfaction degree on those criteria that decisionmaker’s with an expection vaule or interval. Second, we propose a new method to obtain criteriaweights using the satisfaction degree of the decision makers. Third, we also propose a new definitionfor satisfaction degree of alternatives. The best alternative(s) in decision making found by thetraditional ranking methods does not have an equivalence relation with the alternative(s) that have thebiggest combinatorial satisfaction degree of decision makers.
(5) Study on Decision Processes Abstraction and Optimization Based on Process Mining for “Human-Centered Service”. In this section, we use adjacency matrix to extract3types of annotationsinformation (i.e., frequencies, remaining times, activities) and use them to construct new processmodels from the event logs. First, a new process model based on adjacency matrix is proposed.Second, by adding the stage and frequency for every activity into the matrix, another new processmodel based on stage adjacency matrix is further proposed to avoid the possible loops or self-loops.Third, based on the second new model with a multi-stage structure, we compute the conditionalprobability from every stage to next stage through the frequency. These new process models can beused to predict what will actually happen, how possible to reach the next activity, and how soon forthe ongoing process instances to finish. Later, another two handover process models are extractedbased on person adjacency matrix abstraction such as how work transfer from one person to another.The new handover process models can be used to optimize the handover business process throughprobability and time prediction. We use the decision process models to analyze decision maker’spreferences and improve the human preference models.
引文
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