Portraying the practice of decision-making in requirements engineering: a case of large scale bespoke development

Complex decision-making is a prominent aspect of requirements engineering (RE) and the need for improved decision support for RE decision-makers has been identified by a number of authors in the research literature. A first step toward better decision support in requirements engineering is to understand multifaceted decision situations of decision-makers. In this paper, the focus is on RE decision-making in large scale bespoke development. The decision situation of RE decision-makers on a subsystem level has been studied at a systems engineering company and is depicted in this paper. These situations are described in terms of, e.g., RE decision matters, RE decision-making activities, and RE decision processes. Factors that affect RE decision-makers are also identified.     

Optimal system selection in fuzzy environment

The problem of the selection of an optimal decision from a set of finite number of alternatives is considered, when there is a single or multiple decision-makers present. In the single decision-maker case, the optimal decision is the one that satisfies pre-specified multiple objectives. A method is given to select the optimal decision based on membership grade. However, in the multiple decision-makers case, the problem of arriving at group decision, when each decision-maker gives his preference, has been discussed. Nevertheless, in both the situations decision-makers have the freedom to modulate their decision functions by some measure of their satisfaction or preference. Results from the theory of fuzzy sets have been applied to develop the solution methods for different situations. Illustrations are included to show the usage of the methodology.     

基于语言型混合算子的模糊信息聚合方法

在多属性决策过程中经常会用到聚合算子,有序加权平均聚合(OWA)算子是最常用的聚合算子之一,通常用于聚合确切的数值.然而,现实世界部分信息的不确定性以及决策者对一些信息的模糊性,使得部分信息不能用确切的数值表示,从而导致OWA算子及其扩展算子向着多元化发展.对此,给出一种语言型混合有序加权平均聚合(LHOWA)算子,同时研究该算子所应具备的一些基本性质,并给出一种基于该算子的语言型信息聚合方法,用于多属性决策过程中模糊信息的聚合.最后,通过一个煤矿安全评价的算例对所提出方法的优越性进行了验证.     

Algorithms for fuzzy multi expert multi criteria decision making (ME-MCDM)

Inherent complexity and uncertainty in a business environment necessitate the participation of many experts in multi criteria decision making. However, participation of many experts makes the conflict aggregation process difficult. To handle this difficulty, we propose two algorithms namely possibility measure and averaging conflict aggregation. In possibility measure, we integrate the possibility theory of fuzzy logic with a maximal containment method that is designed based on the decision problem. Possibility measure algorithm for ME-MCDM involves computationally expensive multiple information processing steps. Therefore to test and compare this algorithm, averaging conflict aggregation algorithm is proposed that requires fewer mathematical information processing steps. Based on the proposed algorithms, a decision support system (DSS) is developed. We present a case study of supplier evaluation to compare both of the proposed algorithms with the help of developed DSS.     

多属性群决策权重调整自适应算法

研究多属性群决策中权重确定问题。对属性权重采用目标规划法进行优化;计算专家个体决策结果与群体决策结果的灰色关联度,据此对初始专家权重进行调整更新,进而调整更新初始群决策结果;根据2阶Minkowski距离继续对专家权重进行自适应调整,直至计算出稳定的专家权重和群决策结果。通过对风电机组设备供应商的评价表明了该方法的可行性与实用性。     

A case-based reasoning approach for building a decision model

A methodology based on case-based reasoning is proposed to build a topological-level influence diagram. It is then applied to a project proposal review process. The formulation of decision problems requires much time and effort, and the resulting model, such as an influence diagram, is applicable only to one specific problem. However, some prior knowledge from the experience in modeling influence diagrams can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem-solving experience to solve new problems.
In this paper, we suggest case-based decision class analysis (CB-DCA), a methodology based on case-based reasoning, to build an influence diagram. CB-DCA is composed of a case retrieval procedure and an adaptation procedure. Two measures are suggested for the retrieval procedure, one a fitting ratio and the other a garbage ratio. The adaptation procedure is based on decision-analytic knowledge and decision participants' domain-specific knowledge. Our proposed methodology has been applied to an environmental review process in which decision-makers need decision models to decide whether a project proposal is accepted or not. Experimental results show that our methodology for decision class analysis provides decision-makers with robust knowledge-based support.     

A Knowledge Management Framework for the Support of Decision Making in Humanitarian Assistance/Disaster Relief

The major challenge in current humanitarian assistance/disaster relief (HA/DR) efforts is that diverse information and knowledge are widely distributed and owned by different organizations. These resources are not efficiently organized and utilized during HA/DR operations. We present a knowledge management framework that integrates multiple information technologies to collect, analyze, and manage information and knowledge for supporting decision making in HA/DR. The framework will help identify the information needs, be aware of a disaster situation, and provide decision-makers with useful relief recommendations based on past experience. A comprehensive, consistent and authoritative knowledge base within the framework will facilitate knowledge sharing and reuse. This framework can also be applied to other similar real-time decision-making environments, such as crisis management and emergency medical assistance. Received 9 October 2000 / Revised 6 January 2001 / Accepted in revised form 5 March 2001     

对偶犹豫模糊集的相关系数及其应用

对偶犹豫模糊集因其可以给决策者提供更多的决策信息成为模糊决策的热点研究问题,相关性指标可以用来度量两个模糊信息之间的相关关系,熵可以用来度量模糊信息的不确定程度。提出了一种基于对偶犹豫模糊集相关系数和熵的模糊多属性群决策方法。定义了对偶犹豫模糊集相关系数的概念,讨论了其基本性质;提出了两种对偶犹豫模糊集的熵,在此基础上,给出了模糊多属性群决策的权重确定方法;基于对偶犹豫模糊集相关系数和熵,提出了一种属性权重完全未知条件下的模糊多属性群决策方法;通过案例分析说明了该方法的有效性和可行性。     

Investigating the buying process for the introduction of data processing in small and medium-sized firms

Analysing the buying process and the post-purchase experiences may be of interest to both the supplier and potential buyers. The buying behaviour during the introduction of data processing and computer-assisted information processing in 69 small and medium-sized firms were investigated using a standardized questionnaire. For all firms, the decision to buy involved a decision to obtain a system, with unity of hardware, system software, and application software — thus software support had the major influence on the decision. The assumption that in producer markets buyers decide more rationally and with greater market transparency only holds true for so called “intensive decision-makers”. This subset is the result of a cluster analysis that groups the firms according to their decision-making behaviour. A comparison of the “intensive decision-makers” with all others on their post-purchase development of computer-assisted information processing presents the following finding: Over an equal time span, the “intensive decision-makers” achieved a higher level of information processing (as measured by the number of DP-applications installed, the possibility of on-line operation of the firms' departments, and the creation of integrated solutions) than the rest of the sample.     

A novel approach for combination of individual and group decisions based on fuzzy best-worst method

The process of decision-making in an enterprise may either keep the business on track or derail it. Thus, a senior decision maker often use a group of experts as the supportive team to ensure appropriate decisions. The experts often have different expertise level regarding their knowledge, talent, proficiency, and experience. In this study, we first extend the best-worst method based on the linguistic preferences of decision-makers about importance of attributes. These preferences are converted into triangular fuzzy numbers to be utilized in the linear programming model. That is, in contrast with the original best-worst method in which the preferences towards the attributes are crisp, fuzzy preferences are considered in the proposed method to reflect the imprecise comments of experts. Second, we propose a novel group decision making approach based on the fuzzy best-worst method to combine the opinion of senior decision-maker and the opinions of the experts. Indeed, our model helps the senior decision-maker to make a significant trade-off between democratic and autocratic decision-making styles. From sensitivity analyses on two numerical examples, we show that, when there is conflict between senior decision-maker and group of decision-makers, the consistency of group decision-making (democracy) will increase as it tends to individual decision-making (autocracy).     

A new multiple decisions fusion rule for targets detection in multiple sensors distributed detection systems with data fusion

Currently, multiple sensors distributed detection systems with data fusion are used extensively in both civilian and military applications. The optimality of most detection fusion rules implemented in these systems relies on the knowledge of probability distributions for all distributed sensors. The overall detection performance of the central processor is often worse than expected due to instabilities of the sensors probability density functions. This paper proposes a new multiple decisions fusion rule for targets detection in distributed multiple sensor systems with data fusion. Unlike the published studies, in which the overall decision is based on single binary decision from each individual sensor and requires the knowledge of the sensors probability distributions, the proposed fusion method derives the overall decision based on multiple decisions from each individual sensor assuming that the probability distributions are not known. Therefore, the proposed fusion rule is insensitive to instabilities of the sensors probability distributions. The proposed multiple decisions fusion rule is derived and its overall performance is evaluated. Comparisons with the performance of single sensor, optimum hard detection, optimum centralized detection, and a multiple thresholds decision fusion, are also provided. The results show that the proposed multiple decisions fusion rule has higher performance than the optimum hard detection and the multiple thresholds detection systems. Thus it reduces the loss in performance between the optimum centralized detection and the optimum hard detection systems. Extension of the proposed method to the case of target detection when some probability density functions are known and applications to binary communication systems are also addressed.     

Further results on the asymptotic agreement problem

The problem of reaching a concensus between two decision-makers provided with different information is considered. The problem in which the decision-makers may have different underlying probability models is studied. Results are developed to characterize the likelihood of an agreement being reached eventually in terms of the nature of the inter-decision-maker communications. The problem in which the decision-makers are aware of the possibility that they may have different models is treated. It is found that in this case a deadlock can be reached where neither decision maker can learn additional information from the concensus process and they cannot reach a concensus decision. This result indicates that incorporating human uncertainty in probability assessment into the asymptotic agreement problem can lead to outcomes not anticipated in the general theory previously developed     

基于S型曲线的指标权重确定方法

针对动态系统的多指标多决策者综合评价问题,目前的赋权法很少考虑决策者评价可信度及指标权重随评价对象变化而变化的情况,因此提出一种基于置信度的参数可调的S型曲线指标赋权模型.在专家打分法的基础上,通过引入专家权威性和差异性系数来确定专家置信度,提高专家评价的可信度,运用可调参数来校准基于置信度的指标赋权模型,使其能较好地解决评价者与动态系统中指标权重之间的非线性关系,增加了赋权模型的灵活性.结合实例确定指标权重的结果符合客观实际,验证了此方法的可行性和实用性,它为确定指标权重提供了一种新的有效途径.     

Diagnostic reasoning and medical decision-making with fuzzy influence diagrams

Influence diagrams have been widely used as knowledge bases in medical informatics and many applied domains. In conventional influence diagrams, the numerical models of uncertainty are probability distributions associated with chance nodes and value tables for value nodes. However, when incomplete knowledge or linguistic vagueness is involved in the reasoning systems, the suitability of probability distributions is questioned. This study intends to propose an alternative numerical model for influence diagrams, possibility distributions, which extend influence diagrams into fuzzy influence diagrams. In fuzzy influence diagrams, each chance node and value node is associated with a possibility distribution which expresses the uncertain features of the node. This study also develops a simulation algorithm and a fuzzy programming model for diagnosis and optimal decision in medical settings.     

A SIMPLE DECISION RULE ON FUZZY EVENTS

Decision making in real problems is done in a fuzzy environment. Thus, Fuzzy-Bayes decision rules have been proposed to cope with a fuzzy state of nature. These decision rules are based on the probability of fuzzy events, or the possibility measure of fuzzy events. Furthermore, a decision rule based on fuzzy utility functions and the possibility distribution of fuzzy events are constructed. However, in these decision rules the fuzziness of the fuzzy expected utility is very big, because these decision rules are based on the extension principle for calculation of the fuzzy expected utility. In this article, avoiding the large fuzziness of the expected utility, we proposed a simple decision rule based on the representation interval of the possibility distributions of fuzzy events and the representation value of the fuzzy utility function. Further, we discuss the application of this simple decision rule to the decision problems, in which the decision maker obtains the one-peak symmetric possibility distribution of a state of nature and the one-peak symmetric membership functions of fuzzy events on a state of nature, by his or her knowledge and his or her belief.     

Conceptual design evaluation considering the ambiguity semantic variables fusion with conflict beliefs: An integrated Dempster-Shafer evidence theory and intuitionistic fuzzy -VIKOR

Quantifying the uncertain linguistic evaluation from decision-makers (DMs) is one of the most challenging parts in the conceptual design decision. Although fuzzy decision models have been widely used to capture potential uncertainty by assigning a fuzzy term with the certain belief, the ambiguity subjective evaluation of semantic variables with conflict beliefs derived from DMs have not been well addressed. To solve this drawback, a concept decision model based on Dempster-Shafer (DS) evidence theory and intuitionistic fuzzy -Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) considering the ambiguity semantic variables fusion is proposed. Firstly, by incorporating semantic variables of intuitionistic fuzzy sets (IFSs), the diversified semantic judgments and its belief will be taken into account to form an ambiguity semantic initial decision matrix; secondly, the DS combination rule will be used to fuse the different semantic variables of multi-DMs in each scheme, update the belief of each semantic variable, and then the semantic fusion value matrix of the scheme will be constructed; finally, the weight of each evaluation objective will be calculated based on the value matrix and information entropy model, IFS-VIKOR model will be constructed to rank the concepts. A case study of the tree climbing and trimming machine will be employed to verify the proposed decision model. This decision model considering diversifying semantic variables and the conflict belief is proven to be effective compared with the IFS-SAW and ISF-TOPSIS.     

基于可能性理论的案例决策方法研究

提出基于可能性理论与基于案仍推理相结合的双层CBDT决策方法。第1层：决策者应用基于案例推理方法，通过建立在连续集合上问题的可能性分布，结合不同的决策方法对决策问题的不确定性进行定性分析；第2层：利用期望效用理论在与问题的可能性分布相关联的方案中选择具体方案。该方法可避免以往基于案例决策方法中所蕴含的一些技术难题。     

改进证据分类合成方法在农作物生长环境评估中的应用

针对传统DS证据理论存在处理冲突证据的不足,基于证据间的相似度引入了信 息熵属性,修正了证据分类属性,结合证据间相似度属性将证据集重新划分为可信度高证据 、一般性证据和冲突证据,对分类的证据集赋予不同的重要性系数,并加以修正改进。改进后使得一般性证据和高冲突证据向可信度高的证据意见靠拢,最后利用DS组合规则对于修正后的证据进行合成。针对农作物生长环境中多个传感器获取的数据构造其所对应证据的基本概率分配函数,利用模糊理论对基本概率分配函数进行取值。实验采用各类传感器测得的真实数据集进行实验,结果表明改进的方法既能够很好地解决冲突问题,同时能降 低证据的不确定性。     

Conceptual modeling of causal map: Object oriented causal map

This study has been started from question “Is there a methodology that can make causal map comprised of causality as a database by using conceptual modeling method?” In this research, causal map is proposed to represent causal relation by using conceptual modeling method. Therefore, we formalize causality as a cognitive rule to allow us to control changes in the decision making environment. Such causality is embedded in the real world (application domain). And, user (decision maker) use to represent set of causality which decision-makers have retrieved from the set of knowledge or experiences in application domain. Such set of causality that decision-makers possess in their know–how and short (long) term memory are usually formalized by causal map. It is verified for users whether this causal map is helpful in solving their problems. By extending the basis of conceptual modeling theory (ontology theory, classification theory, decomposition theory, and semantic network theory), we introduce a concept of causal entity diagram and address why causal map is needed to analyze a specific domain knowledge for given decision problem solving. Finally, object oriented causal map (O2CM) were employed to verify usefulness of causal map for user (decision maker) in this study.