A vague set based decision support approach for evaluating research funding programs

Scientific Research Assessment (SRA) is receiving increasing attention in both academic and industry. More and more organizations are recognizing the importance of SRA for the optimal use of scarce resources. In this paper, a vague set theory based decision support approach is proposed for SRA. Specifically, a family of parameterized S-OWA operator is developed for the aggregation of vague assessments. The proposed approach is introduced to evaluate the research funding programs of the National Natural Science Foundation of China (NSFC). It provides a soft and expansive way to help the decision maker in NSFC to make his decisions. The proposed approach can also be used for some other agencies to make similar assessment.     

Set choice problems with incomplete information about the preferences of the decision maker

We consider the problem of selecting a predetermined number of objects from a given finite set. It is assumed that the preferences of the decision maker on this set are only partially known. Our solution approach is based on the notions of optimal and non-dominated subsets. The properties of such subsets and the objects they contain are investigated. The implementation of the developed approach is discussed and illustrated by various examples.     

A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems

With the rapid growth of data sets nowadays, the object sets in an information system may evolve in time when new information arrives. In order to deal with the missing data and incomplete information in real decision problems, this paper presents a matrix based incremental approach in dynamic incomplete information systems. Three matrices (support matrix, accuracy matrix and coverage matrix) under four different extended relations (tolerance relation, similarity relation, limited tolerance relation and characteristic relation), are introduced to incomplete information systems for inducing knowledge dynamically. An illustration shows the procedure of the proposed method for knowledge updating. Extensive experimental evaluations on nine UCI datasets and a big dataset with millions of records validate the feasibility of our proposed approach.     

A model of interim information sharing under incomplete information

We propose a two-person game-theoretical model to study information sharing decisions at an interim stage when information is incomplete. The two agents have pieces of private information about the state of nature, and that information is improved by combining the pieces. Agents are both senders and receivers of information. There is an institutional arrangement that fixes a transfer of wealth from an agent who lies about her private information. In our model, we show that (1) there is a positive relation between information revelation and the amount of the transfers, and (2) information revelation has a collective action structure, in particular, the incentives of an agent to reveal decrease with respect to the amount of information disclosed by the other.     

An efficient decision making approach in incomplete soft set

Zou et al. (2008) [21] presented weighted-average of all possible choice values approach of soft sets under incomplete information system in decision making. However, the approach is hard to understand and involves a great amount of computation. In order to simplify the approach, we present the simplified probability to directly instead of the incomplete information, and demonstrate the equivalence between the weighted-average of all possible choice values approach and the simplified probability approach. Finally, comparison results show that the proposed approach involves relatively less computation and is easier to implement and understand as compared with the weighted-average of all possible choice values approach.     

Multi-criteria semantic dominance: A linguistic decision aiding technique based on incomplete preference information

A linguistic decision aiding technique for multi-criteria decision is presented. We define a relation between alternatives as multi-criteria semantic dominance (MCSD). It adopts the similar ideal of the stochastic dominance by utilizing the partial information of the decision maker’s preference, which is only ordinal or partially cardinal. The MCSD rules based on three typical types of semanteme functions are introduced and proven. By using these rules, all the alternatives under consideration are divided into two mutually exclusive sets called efficient set and inefficient set. The decision maker who has such a semanteme function will never choose the alternative from the corresponding inefficient set as the optimal one. In such a way, when we analyze the linguistic decision information, the inherent fuzziness of preference can be handled and several controversial operations of the linguistic terms can be avoided. An example is also provided to illustrate the procedure of the proposed method.     

A framework for the value of information in inventory replenishment

Technological advances and changes in supply chain management practices have combined to draw attention to the value of information sharing in inventory replenishment. Academic research has produced seemingly conflicting results due to differences in the type of information that is shared, the supply chain structure, and the selection and parameterization of performance goals. This research provides a framework to help explain apparent differences in the extant literature. Our purpose is to understand what determines the value of information. With this specific view, we establish a set of research questions and suggest directions for future research.     

油田A类物资采购决策支持系统研究

结合大庆油田物资公司在重要供应链管理环节,即采购、需求和库存中所面临的实际问题(物资需求增加、仓储压力增大、采购成本增多)及三者之间的相互作用,基于预测和优化理论,构建了针对油田A类物资的采购优化和库存管理决策支持系统原型,包括：预测模块、优化模块和方案调整评估模块,为相关部门制定合理物资采购方案提供决策支持.进一步,以银浪仓库中的4种A类物资为例,运用该原型系统进行数值模拟. 结果表明,2009年和2010年4种物资的总成本节省率分别为10.35%和8.07%,效益可观. 考虑到油田物资数据结构不完备及优化模型的复杂性,该原型系统在大庆油田大规模推广方面仍需进一步完善.     

Scenario-based portfolio selection of investment projects with incomplete probability and utility information

In the selection of investment projects, it is important to account for exogenous uncertainties (such as macroeconomic developments) which may impact the performance of projects. These uncertainties can be addressed by examining how the projects perform across several scenarios; but it may be difficult to assign well-founded probabilities to such scenarios, or to characterize the decision makers’ risk preferences through a uniquely defined utility function. Motivated by these considerations, we develop a portfolio selection framework which (i) uses set inclusion to capture incomplete information about scenario probabilities and utility functions, (ii) identifies all the non-dominated project portfolios in view of this information, and (iii) offers decision support for rejection and selection of projects. The proposed framework enables interactive decision support processes where the implications of additional probability and utility information or further risk constraints are shown in terms of corresponding decision recommendations.     

The impacts of uncertainties in a real options model under incomplete information

This paper examines the impact with respect to the uncertainty of the underlying state variable, profit uncertainty, on the real options model in a situation of incomplete information. Profit uncertainty has not incorporated into the real options model under incomplete information, in that the underlying state variable is not formulated as the stochastic process (see, e.g., Bernardo, A. E., Chowdhry, E. B., 2002. Resources, real options, and corporate strategy. Journal of Financial Economics, 63, 211–234). We extend the model developed by Bernardo and Chowdhry to formulate the underlying state variable as the stochastic process. We conclude that profit uncertainty has the same type of impact on the real options value and its triggers, both under complete and incomplete information.     

Multiple-attribute decision support system based on fuzzy logic for performance assessment

This paper deals with the problem of assessing the performance of a set of production units, simultaneously considering different kinds of information, yielded by a Data Envelopment Analysis, a qualitative data analysis and an expert assessment. The tool for integrating heterogeneous data is a model that applies fuzzy logic to decision support systems. The results obtained are a holistic performance assessment of each unit of the set and a ranking order of the units.     

A list-based compact representation for large decision tables management

Due to the huge size of the tables we manage when dealing with real decision-making problems under uncertainty, we propose turning them into minimum storage space multidimensional matrices. The process involves searching for the best order of the matrix dimensions, which is a NP-hard problem. Moreover, during the search, the computation of the new storage space that each order requires and copying the table with respect to the new order may be too time consuming or even intractable if we want a process to work in a reasonable time on an ordinary PC. In this paper, we provide efficient heuristics to solve all these problems. The optimal table includes the same knowledge as the original table, but it is compacted, which is very valuable for knowledge retrieval, learning and expert reasoning explanation purposes.     

A web-based decision support system for waste lube oils collection and recycling

This paper presents a web-based decision support system (DSS) that enables schedulers to tackle reverse supply chain management problems interactively. The focus is on the efficient and effective management of waste lube oils collection and recycling operations. The emphasis is given on the systemic dimensions and modular architecture of the proposed DSS. The latter incorporates intra- and inter-city vehicle routing with real-life operational constraints using shortest path and sophisticated hybrid metaheuristic algorithms. It is also integrated with an Enterprise Resource Planning system allowing the utilization of particular functional modules and the combination with other peripheral planning tools. Furthermore, the proposed DSS provides a framework for on-line monitoring and reporting to all stages of the waste collection processes. The system is developed using a web architecture that enables sharing of information and algorithms among multiple sites, along with wireless telecommunication facilities. The application to an industrial environment showed improved productivity and competitiveness, indicating its applicability on realistic reverse logistical planning problems.     

A decision support for identifying crucial knowledge requiring capitalizing operation

Capitalizing on the company’s crucial knowledge is an important operation for company’s success. The first step in knowledge capitalizing is to identify the crucial knowledge for which capitalizing operation is required. The crucial knowledge identification is an ill-structured and multi-criteria problem. In this paper, the crucial knowledge identification problem is structured according to the well-known Simon’s decision making process. Then, a decision support system (DSS), called K-DSS, is developed to support this decision process. The main objectives of this paper are to introduce the crucial knowledge identification decision process, to present the conceptual and functional architectures of K-DSS and to illustrate its utility through a real-world case study conducted in an automobile company.     

Fuzzy goal programming for inventory management: A bacterial foraging approach

An efficient inventory planning approach in today’s global trading regime is necessary not only for increasing the profit margin, but also to maintain system flexibility for achieving higher customer satisfaction. Such an approach should hence be comprised of a prudent inventory policy and clear satisfaction of stakeholder’s goals. Relative significance given to various objectives in a supply chain network varies with product as well as time. In this paper, a model is proposed to fill this void for a single product inventory control of a supply chain consisting of three echelons. A generic modification proposed to the membership functions of the fuzzy goal-programming approach is used to mathematically map the aspiration levels of the decision maker. The bacterial foraging algorithm has been modified with enhancement of the algorithms’ capability to map integer solution spaces and utilised to solve resulting fuzzy multi-objective function. An illustrative example comprehensively covers various decision scenarios and highlights the underlying managerial insights.     

A parametric GP model dealing with incomplete information for group decision-making

We consider a group decision-making problem where preferences given by the experts are articulated into the form of pairwise comparison matrices. In many cases, experts are not able to efficiently provide their preferences on some aspects of the problem because of a large number of alternatives, limited expertise related to some problem domain, unavailable data, etc., resulting in incomplete pairwise comparison matrices. Our goal is to develop a computational method to retrieve a group priority vector of the considered alternatives dealing with incomplete information. For that purpose, we have established an optimization problem in which a similarity function and a parametric compromise function are defined. Associated to this problem, a logarithmic goal programming formulation is considered to provide an effective procedure to compute the solution. Moreover, the parameters involved in the method have a clear meaning in the context of group problems.     

A simple model of corporate international investment under incomplete information and taxes

This paper extends the theory of corporate international investment in Choi (J. Int. Bus. Stud. 20: 145–155, 1989) in an environment where the segmentation of international capital markets for investors or the presence of agency costs provide some independence to corporate decisions. The model shows that the real exchange risk, the competition between firms in different markets and diversification gains affect corporate international investment. By accounting for the role of information as defined in the models of Merton (J. Finance 42: 483–510, 1987), Bellalah (Int. J. Finance Econ. 6: 59–67, 2001a) and Bellalah and Wu (Int. J. Theor. Appl. Finance 5(5): 479–495, 2002), the model embodies different existing explanations based on economic and behavioral variables. We show in a “two-country” firm model that real exchange risk, diversification motives and information costs are important elements in the determination of corporate international investment decisions. The dynamic portfolio model reflects the main results in several theories of foreign direct investment. Our model accounts for the role of information in explaining foreign investments. It provides simple explanations which are useful in explaining the home bias puzzle in international finance. Using the dynamical programming principle method, we provide the general solution for the proportion of firm’s total capital budget. We also use a new method to get explicit solutions in some special cases. This new method can be applied to solve other financial control problems. The simulating results are given to show our conclusion and the influence of some parameters to the optimal solution. The economic results can be seen as a generalization of the model in Solnik (J. Econ. Theory 8: 500–524, 1974).     

Evaluation of firm’s loss due to incomplete information in real investment decision

We investigate the effect of incomplete information in a model where a start-up with a unique idea and technology pioneers a new market but will eventually be expelled from the market by a large firm’s subsequent entry. We evaluate the start-up’s loss due to incomplete information about the large firm’s behavior. We clarify conditions under which the start-up needs more information about the large firm. The proposed method of evaluating the loss due to incomplete information could also be applied to other real options models involving incomplete information.     

Modelling imperfect advance demand information and analysis of optimal inventory policies

We consider an inventory control problem where it is possible to collect some imperfect information on future demand. We refer to such information as imperfect Advance Demand Information (ADI), which may occur in different forms of applications. A simple example is a company that uses sales representatives to market its products, in which case the collection of sales representatives’ information as to the number of customers interested in a product can generate an indication about the future sales of that product, hence it constitutes imperfect ADI. Other applications include internet retailing, Vendor Managed Inventory (VMI) applications and Collaborative Planning, Forecasting, and Replenishment (CPFR) environments. We develop a model that incorporates imperfect ADI with ordering decisions. Under our system settings, we show that the optimal policy is of order-up-to type, where the order level is a function of imperfect ADI. We also provide some characterizations of the optimal solution. We develop an expression for the expected cost benefits of imperfect ADI for the myopic problem. Our analytical and empirical findings reveal the conditions under which imperfect ADI is more valuable.     

A simulation model for emergency evacuation

This paper describes the development of a prototype spatial decision support system for use by emergency planners in developing contingency plans for evacuations from disaster areas. It links together a geographical information system (ARC/INFO) with a specially written object-oriented micro-simulator via a windowing computer operating system. The details of the system are described, its limitations are discussed and potential enhancements are identified.