On the organization of large shared Model bases

Central to the Model Management (MM) function is the creation and maintenance of a knowledge-based model repository. The Model Knowledge Base (MKB) provides the basis by which information about models can be shared to facilitate consistent and controlled utilization of existing models for decision making, as well as the development of new models. Various schemes for representing individual models have been proposed in the literature. This paper focuses on how best to structure, control, and administer a large MKB to support organization-wide modeling activities. Guided by a recently proposed systems framework for MM, we describe a number of concepts which are useful for capturing the semantics and structural relationships of models in an MKB. These concepts, and the nature of the MMS functions to be supported, are then used to derive specific information management requirements for model bases. Four major requirements are identified: (1) management of composite model configurations; (2) management of model version histories; (3) support for the model consultation and selection functions of an MMS; and (4) support for multiple logical MKBs (private, group, and public). We argue that traditional record-based approaches to data management appear to fall short of capturing the rich semantics present in an MM environment. The paper proposes an architecture for an MMS, focusing on its major component — the MKB Management Subsystem. An implementation of this architecture is briefly described.     

Eliciting and mapping qualitative preferences to numeric rankings in group decision making

Group work is becoming the norm in organizations. From strategy planning committees to quality management teams, organizational members are collaborating on problem solving. One area of team support that is often desired is the scoring and ranking of decision alternatives on qualitative/subjective domains, and the aggregation of individual preferences into group preferences. In this paper we present a new conceptual approach to qualitative preference elicitation and aggregation. This approach is based on well established decision analysis techniques. It significantly advances the state of the art of group decision making by addressing four common limitations: (1) the inability to deal with vagueness of human decision makers in articulating preferences; (2) difficulties in mapping qualitative evaluation to numeric estimates; (3) problems in aggregating individual preferences into meaningful group preference; and (4) the lack of simple user friendly techniques for dealing with a large number of decision alternatives. Our approach is easy to implement in stand alone personal computers and groupware. We illustrate this with a real-world problem.     

Developing a Visual Interactive Model for Corporate Cash Management

The operational research/management science journals contain an extensive literature that addresses the corporate cash management problem; yet few, if any, companies make use of any of this published work in their daily cash-management decision making. A review of the literature suggests that the reason for this lack of applications may well be poor problem formulation—the problems that are solved in the literature as ‘cash management’ problems evolve from a ‘hard systems’ view of real-world cash management. However, the problem as perceived by cash managers involves both dynamic and loosely structured components which are difficult to model using classical (i.e. ‘hard systems’) approaches.We therefore decided to approach the cash management problem as an experiment in the use of a novel visual interactive problem solving (VIPS) methodology. The aim of the experiment was to develop an implementable, visual interactive model to support daily cash management decision making. Working closely with a corporate cash manager, we first developed a visual model of his daily decision problem and then agreed on the feasible options and the interactive requirements. At this stage, the problem was sufficiently well defined for a mathematical model to be built and the visual model made ‘smart’.This paper discusses the results of this experiment and suggests that VIPS may have distinct advantages as a problem-solving technique in loosely structured, ‘messy’ problem situations.     

Visual-interactive decision modeling (VIDEMO) in policy management: Bridging the gap between analytic and conceptual decision modeling

Policy decision making is a process, rather than a means to an end, stretching over a long time span in a dynamic environment. The advent of easily accessible modeling paradigms promotes the use of sophisticated tools to support policy decision making. It is argued, however, that to be successful in practice, the analytic approaches must be flexible and their role in the problem solving process transparent. In this paper we discuss the concept of visual interactive decision modeling (VIDEMO) in policy management. After positioning decision modeling in the context of problem solving, a generic modeling environment is proposed. It provides the necessary flexibility at the structural level coupled with the required transparency at the formal and resolution levels. The system is based on the premise that policy decision makers can only benefit from the power of analytic modeling if they are supported where and how they want to be supported, without having the analytic tool posing a frame to problem perception, problem analysis, and decision making. In its final version, the proposed VIDEMO approach bridges the gap between analytic and conceptual decision modeling.     

FMMSIC: a hybrid fuzzy based decision support system for MMS (in order to estimate interrelationships between criteria)

One of the main tasks in exploitation of ore-body is to select a suitable mining method. In mining method selection (MMS) problems, a decision procedure has to choose the best exploitation method that satisfies the evaluation criteria. It is generally hard to find a mining method that meets all the criteria simultaneously, therefore a good compromise solution is preferred as the final selection. Furthermore, the MMS problem is an inherently uncertain activity. To deal with the uncertainty, this paper presents an hybrid decision support system based on the fuzzy multi attribute decision making, named the fuzzy mining method selection with interrelation criteria (FMMSIC). FMMSIC models the relative weights of criteria by combining the fuzzy analytic network process and fuzzy entropy, and discusses using these hybrid techniques to determine the overall weights. Subsequently, the technique for order preference by similarity to an ideal solution method was modified by various normalization norms according to the MMS problem condition. Finally, to illustrate how the FMMSIC is used for the MMS problems, an empirical study of a real case is conducted. It shows by means of an application that the FMMSIC is well suited as a decision support system for the MMS.     

MEDSS: A Multi-Agent Environmental Decision Support System framework for model management

Model Management Systems (MMS) have become increasingly important in handling complicated problems in Decision Support Systems (DSS). The primary goal of MMS is to facilitate the development and the utilization of quantitative models to improve decision performance. Much current research focuses on model construction. Where early research used deductive reasoning approaches to construct new models, more recent efforts use inductive reasoning mechanisms. Both approaches have their drawbacks. Deductive reasoning methods require a strong domain theory (which may not exist or may be too complex to apply) and ignore previous solving experience. Inductive reasoning methods can take advantage of precedents or prototypical cases, but do not employ domain knowledge. Both methods are limited in learning capacity. This study proposes a Multi-Agent Environmental Decision Support System, which integrates an Inductive Reasoning Agent, and an Environmental Learning Agent to perform new model formation and problem solving. New models can be generated by the coordination of both the Inductive Agent and the Deductive Agent. At the same time, a model repair process is undertaken by the Environmental Learning Agent when the prediction resulting from existing knowledge fails.     

装备维修器材生产路径双目标问题优化决策方法研究

在装备维修器材供应保障中,针对精确保障背景下部队用户对器材保障精度的要求,构建了最小化总成本和最大化订单精准执行率的双目标优化决策模型。在ε-约束法框架内,开发可生成近似Pareto前沿的两阶迭代启发式算法,并采用模糊逻辑决策法选择符合决策者偏好的折中最优解。随机实例测试结果表明所提出的模型和算法可以很好地应用在双目标优化问题的研究中,并在求解不同规模实例时表现出优异的性能。     

A dual-interval vertex analysis method and its application to environmental decision making under uncertainty

In this study, a dual-interval vertex analysis (DIVA) method is developed, through incorporating the vertex method within an interval-parameter programming framework. The developed DIVA method can tackle uncertainties presented as dual intervals that exist in the objective function and the left- and right-hand sides of the modeling constraints. An interactive algorithm and a vertex analysis approach are proposed for solving the DIVA model. Solutions under an associated α-cut level can be generated by solving a series of deterministic submodels. They can help quantify relationships between the objective function value and the membership grade, which is meaningful for supporting in-depth analyses of tradeoffs between environmental and economic objectives as well as those between system optimality and reliability. A management problem in terms of regional air pollution control is studied to illustrate applicability of the proposed approach. The results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers to identify desired pollution-abatement strategies with minimized costs and maximized environmental efficiencies.     

Bilevel programming approach applied to the flow shop scheduling problem under fuzziness

This paper presents a fuzzy bilevel programming approach to solve the flow shop scheduling problem. The problem considered here differs from the standard form in that operators are assigned to the machines and imposing a hierarchy of two decision makers with fuzzy processing times. The shop owner considered higher level and assigns the jobs to the machines in order to minimize the flow time while the customer is the lower level and decides on a job schedule in order to minimize the makespan. In this paper, we use the concepts of tolerance membership function at each level to define a fuzzy decision model for generating optimal (satisfactory) solution for bilevel flow shop scheduling problem. A solution algorithm for solving this problem is given. Mathematics Subject Classification: 90C70, 90B36, 90C99     

A new multiple attribute group decision making method in intuitionistic fuzzy setting

The multiple attribute group decision making (MAGDM) problem with intuitionistic fuzzy information investigated in this paper is very useful for solving complicated decision problems under uncertain circumstances. Since experts have their own characteristics, they are familiar with some of the attributes, but not others, the weights of the decision makers to different attributes should be different. We derive the weights of the decision makers by aggregating the individual intuitionistic fuzzy decision matrices into a collective intuitionistic fuzzy decision matrix. The expert has a big weight if his evaluation value is close to the mean value and has a small weight if his evaluation value is far from the mean value. For the incomplete attribute weight information, we establish some optimization models to determine the attribute weights. Furthermore, we develop several algorithms for ranking alternatives under different situations, and then extend the developed models and algorithms to the MAGDM problem with interval-valued intuitionistic fuzzy information. Numerical results finally illustrate the practicality and efficiency of our new algorithms.     

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.     

On learning process of a newsvendor with censored demand information

A behavioural study on the newsvendor problem without demand distribution knowledge or realized demands is conducted. We configure a censored-information scenario, where only sales data in history are provided. With laboratory experiments, we analyse participants ordering levels and ordering oscillations over time. It is observed that participants perform a significant learning process in the censored-information scenario. Moreover, we find that participants make orders by anchoring on the previous-period sales and adjusting to an adapted inventory level. On the basis of the observations, we propose an exponential-type learning (EXP) model to describe the behaviour of decision makers. Comparing with a chasing model that is popularly used in the full-information scenario, the EXP model is more recommended in the censored-information scenario.     

基于群体一致性的犹豫模糊多属性决策方法

犹豫模糊集允许一个元素属于一个集合的隶属度可以是多个不同的值,是表达决策者之间偏好不一致性的有力工具。针对决策者评价偏差不宜过大的问题,提出了一种基于群体一致性的犹豫模糊多属性决策方法。首先, 我们定义了犹豫模糊元的犹豫度函数,进而定义了犹豫模糊元的一致性指数;在此基础上,构建了基于群体一致性指数最大化的权重优化模型,通过求解优化模型可以得到属性的权重向量。然后,运用灰色关联分析法实现对方案的排序和择优。最后,通过实例分析说明了该方法的可行性和有效性。     

A rule-based risk decision-making approach and its application in China's customs inspection decision

This paper addresses a kind of risk decision-making problem existing widely in public administration and business management, which is characterized by (1) occurrence probabilities of states of nature can be estimated by analysing historical observations, but historical observations of different objects are unhomogeneous, (2) the relation between observations and occurrence probabilities of states of nature are affected by some qualitative and quantitative indicators, (3) it is a real-time decision-making problem, that is, there are many decisions for different objects to be made in a limited time, (4) considering decision's execution, impact of resource constrains is an important issue in decision-making process. In this paper, we develop a rule-based approach to address the problem. In the proposed approach, a two-step clustering method is employed to classify objects into categories, and observations in each category can be approximately viewed as homogeneous. For objects in each category, occurrence probabilities of states of nature are estimated by logistic regression, and the decision rule is obtained through solving an optimization model, which is to minimize the total decision risks while satisfying resource constrains. Effect and efficacy of our approach are illustrated through its application to China's customs inspection decision.     

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。     

Additive aggregation with variable interdependent parameters: the VIP analysis software

We consider the aggregation of multicriteria performances by means of an additive value function under imprecise information. The problem addressed here is the way an analysis may be conducted when the decision makers are not able to (or do not wish to) fix precise values for the importance parameters. These parameters can be seen as interdependent variables that may take several values subject to constraints. Firstly, we briefly classify some existing approaches to deal with this problem. We argue that they complement each other, each one having its merits and shortcomings. Then, we present a new decision support software—VIP analysis—which incorporates approaches belonging to different classes. It proposes a methodology of analysis based on the progressive reduction of the number of alternatives, introducing a concept of tolerance that lets the decision makers use some of the approaches in a more flexible manner.     

A multiobjective decomposition-coordination framework with an application to vehicle layout and configuration design

This short paper addresses both researchers in multiobjective optimization as well as industrial practitioners and decision makers in need of solving optimization and decision problems with multiple criteria. To enhance the solution and decision process, a multiobjective decomposition-coordination framework is presented that initially decomposes the original problem into a collection of smaller-sized subproblems that can be solved for their individual solution sets. A common solution for all decomposed and, thus, the original problem is then achieved through a subsequent coordination mechanism that uses the concept of epsilon-efficiency to integrate decisions on the desired tradeoffs between these individual solutions. An application to a problem from vehicle configuration design is selected for further illustration of the results in this paper and suggests that the proposed method is an effective and promising new solution technique for multicriteria decision making and optimization. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A framework for decision support systems for scheduling problems

Considering a decision support system as a tool where executive's judgment can be included along with the mathematical tool kit of the management scientist, this paper shows the need to include problem management as an integral component of the decision support system for scheduling problems. A methodology based on the resolution of conflicts among various constraints in scheduling problems is proposed to implement the problem management system in a decision support system for these problems. The paper concludes with some guidelines to create a workable framework for providing effective decision support to solve scheduling problems and the identification of some fruitful directions for future research.