A real-time man-in-loop threat evaluation and resource assignment in defense

Efficient weapon threat assignment reflects military proficiency and requires prompt decision while managing the available resources. An important problem which commanders/decision makers face is to optimally utilize the resources in complex and time constraints situations. Several solutions have been proposed in the literature. In this paper, an innovative approach is proposed for threat evaluation and weapon assignment (TEWA) by following 3-dimensional stable marriage algorithm (3-D SMA). This proposed model incorporates new parameters and constraints i.e. supply chain, inventory of resources and multiple threats-weapons assignments that outperforms the previous techniques. This suggested model is based on threat perception followed by an integration of parametric based automatic threat evaluation technique for further weapon scheduling and assignment problem keeping in view that the threat with greater threat index has higher priority to be intercepted and weapons’ kill probability. The experimental section shows that our proposed approach has greatly improved in comparison with other approaches. The results showed that the threat neutralization is improved up to 25% reducing the usage of ammunition till 31.1%. The damage of assets abridged to 28.5% in comparison with existing approaches. The proposed approach elucidates that TEWA is an efficient real-time threat perception and optimal multi-threat scheduling problem at weapons’ resolution. It is a three-stage process, where the first stage perceives the threat, the second stage works on threat evaluation and the final stage focuses on weapon scheduling and assignment problem. The addition of new parameters and constraints in the new proposed model makes it a unique approach in which more accurate results, in neutralizing the threats, are obtained with less use of ammunition and damage of assets that makes TEWA more effective and efficient tool for optimum decision making in time critical situations.     

Evidential reasoning based preference programming for multiple attribute decision analysis under uncertainty

Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modelled and analysed using the evidential reasoning (ER) approach. Several types of uncertainty such as ignorance and fuzziness can be consistently modelled in the ER framework. In this paper, both interval weight assignments and interval belief degrees are considered, which could be incurred in many decision situations such as group decision making. Based on the existing ER algorithm, several pairs of preference programming models are constructed to support global sensitivity analysis based on the interval values and to generate the upper and lower bounds of the combined belief degrees for distributed assessment and also the expected values for ranking of alternatives. A post-optimisation procedure is developed to identify non-dominated solutions, examine the robustness of the partial ranking orders generated, and provide guidance for the elicitation of additional information for generating more desirable assessment results. A car evaluation problem is examined to show the implementation process of the proposed approach.     

Data analysis and decision support

In this paper, we consider developmental lines of computer-assisted decision support (with consideration of knowledge-based approaches) for data analysis problems. First, we discuss some situations where it is obviously appropriate to apply computer-assisted decision support in connection with data analysis tasks. Then, a brief historical retrospect is given viewing the development of this area of research and its interfaces to knowledge-based approaches. Against this background we illustrate two prototypes of knowledge-based decision support systems for specific data-analysis problems related to fields of interest of our own. Finally, we indicate possible progress and future activities in this area.     

Admissible solutions of k-extended finite state set and sequence compound decision problems

We treat situations in which independent structurally identical decision problems are to be faced either simultaneously or serially. Recent work on such compound decision problems has centered on finding procedures that satisfy the strengthened asymptotic optimality property of Gilliland and Hannan (Ann. Math. Statist.40 (1969), 1536–1541) and on providing decision rules that at once satisfy an admissibility property and the classical asymptotic optimality property. We suggest a simplified method of accomplishing the first of these goals in general situations and then provide decision rules for finite state components simultaneously admissible and satisfying the strengthened optimality property.     

基于改进VIKOR方法的地震应急物资运输决策问题研究

地震灾害应急救援物资方案的合理选择对与减少人员伤亡,降低灾民的财产损失具有重要影响。本文针对属性权重未知情形下的地震应急物资运输方案决策问题,提出了一种Pythagorean模糊不确定语言与前景理论相结合的改进VIKOR决策方法,即PFUL-PT-VIKOR法。首先,采用Pythagorean模糊不确定语言用于描述和融合专家对地震应急物资运输方案在考虑多种属性影响下的感知信息;其次,利用主客观融合法对属性权重进行求解;然后,提出基于前景理论的改进VIKOR法并得出方案排序;最后,通过算例分析,对所提出方法的有效性和实践性给予验证。结果表明,PFUL-PT-VIKOR模型有助于增强决策专家对不确定突发情景信息感知的知识表示能力,解决属性赋权过于主观或过分依赖样本的困难,并突围了应急决策者隐性心理行为较难定量应用的思维定势,增强了模型的现实适用性,为地震应急物资是否合理运输提供决策支持。     

On the incorporation of adversarial reasoning in course of action analysis and resource allocation

Course of Action analysis and Resource Management are concerned with the allocation of resources over time to effect desired actions as a result of the perceived situation awareness. Decision Support Systems provide automated recommended courses of action to decision makers, considering relevant resource capabilities and constraints. Incorporating potential adversary actions and reactions to the current course of action decision (and the resources effecting the actions) in the decision making process will make the decision support system more robust and increase confidence that the recommended decisions are appropriate responses to the unfolding situations. We discuss research results from the inclusion of possible adversary actions and reactions into the course of action/resource allocation decision making framework. The overall decision problem is formulated as a multi-stage mathematical program. As the problem is NP-hard, an heuristic is developed through a natural problem decomposition. Simulated results show the effectiveness of the heuristic in producing good-quality solutions in an efficient manner.     

An Approximation of a Markov Decision Process for Resource Planning

This study presents an approximation of a Markovian decision process to calculate resource planning policies for environments with probabilistic resource demand. These policies provide a means of periodic determination of the quantity of resources required to be available. Managers may also use these approximation models to perform the sensitivity analysis of resource demand and the cost/reward parameters. The decision policy can be applied to many resource planning situations including manufacturing or construction equipment purchasing or leasing, airline capacity, professional services staffing, and computer/management information systems capacity.     

Ranking efficient DMUs using the Tchebycheff norm

One problem that has been discussed frequently in data envelopment analysis (DEA) literature has been lack of discrimination in DEA applications, in particular when there are insufficient DMUs or the number of inputs and outputs is too high relative to the number of units. This is an additional reason for the growing interest in complete ranking techniques. In this paper a method for ranking extreme efficient decision making units (DMUs) is proposed. The method uses L_∞(or Tchebycheff) Norm, and it seems to have some superiority over other existing methods, because this method is able to remove the existing difficulties in some methods, such as Andersen and Petersen [2] (AP) that it is sometimes infeasible. The suggested model is always feasible.     

Mathematical programming and the sensitivity of multi-criteria decisions

We report on the current state of a project whose aim is to implement a framework for sensitivity analysis in Multi-Criteria Decision Making (MCDM). The framework is largely based on mathematical programming. Due to the potentially large number and nature of the mathematical programmes, it is far from trivial to provide solutions to all of them in acceptable computing times. The challenge is even greater when we recognize that much decision analysis is performed in the context of decision conferences where any sensitivity analysis needs to be conducted in near real time (preferably) on a PC. We present a parallel processing approach to this challenge and point to some of the difficulties still to be resolved. Preliminary results obtained on a network of transputers are discussed.     

Decision making using multiple models

Many real world business situations require classification decisions that must often be made on the basis of judgment and past performance. In this paper, we propose a decision framework that combines multiple models or techniques in a complementary fashion to provide input to managers who make such decisions on a routine basis. We illustrate the framework by specifically using five different classification techniques – neural networks, discriminant analysis, quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), and multinomial logistic regression analysis (MNL). Application of the decision framework to an actual retail department store data shows that it is most useful in those cases where uncertainty is high and a priori classification cannot be made with a high degree of reliability. The proposed framework thus enhances the value of exception reporting, and provides managers additional insights into the phenomenon being studied.     

An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements

With the aim of modeling multiple attribute group decision analysis problems with group consensus (GC) requirements, a GC based evidential reasoning approach and further an attribute weight based feedback model are sequentially developed based on an evidential reasoning (ER) approach. In real situations, however, giving precise (crisp) assessments for alternatives is often too restrictive and difficult for experts, due to incompleteness or lack of information. Experts may also find it difficult to give appropriate assessments on specific attributes, due to limitation or lack of knowledge, experience and provided data about the problem domain. In this paper, an ER based consensus model (ERCM) is proposed to deal with these situations, in which experts’ assessments are interval-valued rather than precise. Correspondingly, predefined interval-valued GC (IGC) requirements need to be reached after group analysis and discussion within specified times. Also, the process of reaching IGC is accelerated by a feedback mechanism including identification rules at three levels, consisting of the attribute, alternative and global levels, and a suggestion rule. Particularly, recommendations on assessments in the suggestion rule are constructed based on recommendations on their lower and upper bounds detected by the identification rule at a specific level. A preferentially developed industry selection problem is solved by the ERCM to demonstrate its detailed implementation process, validity, and applicability.     

Value differences using second-order distributions

Most decision models for handling vague and imprecise information are unnecessarily restrictive since they do not admit for discrimination between different beliefs in different values. This is true for classical utility theory as well as for the various interval methods that have prevailed. To allow for more refined estimates, we suggest a framework designed for evaluating decision situations considering beliefs in sets of epistemically possible utility and probability functions, as well as relations between them. The various beliefs are expressed using different kinds of belief distributions. We show that the use of such distributions allows for representation principles not requiring too hard data aggregation, but still admitting efficient evaluation of decision situations.     

Onn-person noncooperative multicriteria games described in strategic form

This paper undertakes the problem of multicriteria decision support in conflict situations described as a noncooperative game. Construction of such a decision support requires the development of the noncooperative game theory to be generalized for the multicriteria case. New theoretical results in this case are presented. Features of the multicriteria noncooperative games are shown with use of a duopoly model in case of two goods and two criteria of each player. Concepts of the decision support are discussed.     

Fuzzy set based models and methods of multicriteria group decision making

In the paper, the term consensus scheme is utilized to denote a dynamic and iterative process where the experts involved discuss a multicriteria decision problem. This discussion process is conducted by a human or artificial moderator, with the purpose of minimizing the discrepancy between the individual opinions.During the process of decision making, each expert involved must provide preference information. The information format and the circumstances where it must be given play a critical role in the decision process. This paper analyses a generic consensus scheme, which considers many different preference input formats, several possible interventions of the moderator, as well as admitting several stop conditions for interrupting the discussion process. In addition, a new consensus scheme is proposed with the intention of eliminating some difficulties met when the traditional consensus schemes are utilized in real applications. It preserves the experts’ integrity through the intervention of an external person, to supervise and mediate the conflicting situations. The human moderator is supposed to interfere in the discussion process by adjusting some parameters of the mathematical model or by inviting an expert to update his opinion. The usefulness of this consensus scheme is demonstrated by its use to solve a multicriteria group decision problem, generated applying the Balanced Scorecard methodology for enterprise strategy planning. In the illustrating problem, the experts are allowed to give their preferences in different input formats. But the information provided is made uniform on the basis of fuzzy preference relations through the use of adequate transformation functions, before being analyzed. The advantage of using fuzzy set theory for solving multiperson multicriteria decision problems lies in the fact that it can provide the flexibility needed to adequately deal with the uncertain factors intrinsic to such problems.     

Fuzzy set based models and methods of multicriteria group decision making

In the paper, the term consensus scheme is utilized to denote a dynamic and iterative process where the experts involved discuss a multicriteria decision problem. This discussion process is conducted by a human or artificial moderator, with the purpose of minimizing the discrepancy between the individual opinions.During the process of decision making, each expert involved must provide preference information. The information format and the circumstances where it must be given play a critical role in the decision process. This paper analyses a generic consensus scheme, which considers many different preference input formats, several possible interventions of the moderator, as well as admitting several stop conditions for interrupting the discussion process. In addition, a new consensus scheme is proposed with the intention of eliminating some difficulties met when the traditional consensus schemes are utilized in real applications. It preserves the experts’ integrity through the intervention of an external person, to supervise and mediate the conflicting situations. The human moderator is supposed to interfere in the discussion process by adjusting some parameters of the mathematical model or by inviting an expert to update his opinion. The usefulness of this consensus scheme is demonstrated by its use to solve a multicriteria group decision problem, generated applying the Balanced Scorecard methodology for enterprise strategy planning. In the illustrating problem, the experts are allowed to give their preferences in different input formats. But the information provided is made uniform on the basis of fuzzy preference relations through the use of adequate transformation functions, before being analyzed. The advantage of using fuzzy set theory for solving multiperson multicriteria decision problems lies in the fact that it can provide the flexibility needed to adequately deal with the uncertain factors intrinsic to such problems.     

IMGD: an interactive method for multiobjective group decision aid

Multiobjective methods for group decision situations that are proposed in the literature do not generally model power and influence. On the other hand, papers dealing with influence and power in group decision support system (GDSS) are looking for the effects of GDSS on the distribution of power among the group members. This paper proposes an interactive method for group decision aid in multiobjective context integrating the concept of power and influence within the multiperson–multicriteria aspect. The method is designed to be used by a committee to solve a multiple criteria allocation problem. The method is tested on a resource allocation problem in the Municipality of Tunis.     

SMAA - Stochastic multiobjective acceptability analysis

Stochastic multiobjective acceptability analysis (SMAA) is a multicriteria decision support technique for multiple decision makers based on exploring the weight space. Inaccurate or uncertain input data can be represented as probability distributions. In SMAA the decision makers need not express their preferences explicitly or implicitly; instead the technique analyses what kind of valuations would make each alternative the preferred one. The method produces for each alternative an acceptability index measuring the variety of different valuations that support that alternative, a central weight vector representing the typical valuations resulting in that decision, and a confidence factor measuring whether the input data is accurate enough for making an informed decision.     

An integrated decision support approach for project investors in risky and uncertain environments

In project investment decisions, it is often assumed that estimated values of project parameters are certain and they would not deviate by the time. However, project parameters normally change during a life cycle of the project. Therefore, an existence of a deviation or gap between forecasted values and actual values is inevitable. Because of the uncertainty of the future, forecasting the true and exact values of project parameters is almost impossible. In this study, an integrated decision support approach based on simulation and fuzzy set theory is proposed for project investors in risky and uncertain environments. This approach determines the risk levels of the projects and helps investors to make investment decisions. In the scope of the study, a flowchart is presented to guide to decision maker in different situations of information uncertainty that belongs to project parameter values. Via this flowchart, the values of project parameters can be chosen depending on how they are determined (deterministic, stochastic or fuzzy) by project analyst. Besides, calculating and analyzing the project risk in all possible situations would be easier. Illustrative examples are given to demonstrate the application of this approach.     

Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments

This paper enhances cost efficiency measurement methods to account for different scenarios relating to input price information. These consist of situations where prices are known exactly at each decision making unit (DMU) and situations with incomplete price information. The main contribution of this paper consists of the development of a method for the estimation of upper and lower bounds for the cost efficiency (CE) measure in situations of price uncertainty, where only the maximal and minimal bounds of input prices can be estimated for each DMU. The bounds of the CE measure are obtained from assessments in the light of the most favourable price scenario (optimistic perspective) and the least favourable price scenario (pessimistic perspective). The assessments under price uncertainty are based on extensions to the Data Envelopment Analysis (DEA) model that incorporate weight restrictions of the form of input cone assurance regions. The applicability of the models developed is illustrated in the context of the analysis of bank branch performance. The results obtained in the case study showed that the DEA models can provide robust estimates of cost efficiency even in situations of price uncertainty.     

Tentative guidelines to help choosing an appropriate MCDA method

Despite the development of a large number of refined multicriterion decision aid (MCDA) methods, none can be considered as the `super method' appropriate to all decision making situations. Hence, how can one choose an appropriate method to a specific decision situation? Recent experimental studies in psychology and behaviour have revealed, on the one hand, that the human thinking is not to be modelled by logical rules and calculations, and, on the other hand, that the response mode affects the preference formation as well as the use of compensatory or noncompensatory strategies. The aim of this paper is to draw a conceptual framework for articulating tentative guidelines to choose an appropriate MCDA method. This paper also presents the results of the comparison of well known multicriterion aggregation procedures (MCAP) on the basis of these guidelines. In our opinion this study can constitute a first step for proposing a methodological approach to select an appropriate MCDA method to a specific decision making situation. Such an approach should be validated and may be integrated into a decision support system. Moreover, the framework suggested is helpful to develop useful methods and to address neglected issues within the field.