An intelligent decision support approach for reviewer assignment in R&D project selection

In the process of Research and Development (R&D) project selection, experts play an important role because their opinions are the foundation on which to judge the potential value of a project. How to assign the most appropriate experts to review project proposals might greatly affect the quality of project selection, which in turn could affect the return on investment of the funding organization. However, in many funding organizations, current approaches to assigning reviewers are still based on simply matching the discipline area of the reviewers with that of the proposal, which could result in poor quality of project selection and poor future financial return. Additionally, these approaches might make it difficult to balance resources and resolve conflicts of interests between reviewers and applicants. Therefore, to overcome these problems, there is an urgent need for a systematic approach to support and automate the reviewer assignment process. This research aims at proposing an intelligent decision support approach for reviewer assignment and developing an Assignment Decision Support System (ADSS). In this approach, heuristic knowledge of expert assignment and techniques of operations research are integrated. The approach uses decision models to determine the best solution of reviewer assignment that maximizes the total expertise level of the reviewers assigned to proposals. It also balances the distribution of proposals at different grades and solves conflicts of interests between reviewers and applicants. Its application in the National Natural Science Foundation of China (NSFC) and the computational results of its effectiveness and efficiency are also described.     

Development of a quick credibility scoring decision support system using fuzzy TOPSIS

In this study, a quick credibility scoring decision support system is developed for the banks to determine the credibility of manufacturing firms in Turkey. The proposed decision support system is expected to be used by the banks when they want to determine whether an applicant firm is worth a detailed credit check or not. Using such a quick credit scoring decision model reduces the banks’ workload. The proposed credit scoring model is based on the financial ratios and fuzzy TOPSIS approach. It obtains two separate scores which reflect the attractiveness of manufacturing industries within the overall economy and manufacturing firms’ performance with respect to its competitors belonging to the same industry. These two scores are then used to determine the credibility of applicant manufacturing firms. The developed decision support system is tested with various real cases and satisfactory results are obtained. An application is also provided in the paper for illustrative purposes.     

Development of a fuzzy decision support framework for complex multi‐attribute decision problems: A case study for the selection of skilful basketball players

The selection of skilful players is a complicated process due to the problem criteria consisting of both qualitative and quantitative attributes as well as vague linguistic terms. This study seeks to develop a decision support framework for the selection of candidates eligible to become basketball players through the use of a fuzzy multi‐attribute decision making (MADM) algorithm. The proposed model is based on fuzzy analytic hierarchy process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The model was employed in the Youth and Sports Center of Mugla, Turkey, with the participation of seven junior basketball players aged between 7 and 14. In the present study, physical fitness measurement values and observation values of technical skills were utilized. FAHP was used to determine the weights of the criteria and the observation values of technical skills by decision makers. Physical fitness measurement values were converted to fuzzy values by using a fuzzy set approach. Subsequently, the overall ranking of the candidate players was determined by the TOPSIS method. Results were compared with human experts’ opinions. It is observed that the developed model is more reliable to be used in decision making. The model architecture and experimental results along with illustrative examples are further demonstrated in the study.     

A decision support system for material and manufacturing process selection

The material and manufacturing process selection problem is a multi-attribute decision-making problem. These decisions are made during the preliminary design stages in an environment characterized by imprecise and uncertain requirements, parameters, and relationships. Material and process selection decisions must occur before design for manufacturing can begin. This paper describes a prototype material and manufacturing process selection system called MAMPS that integrates a formal multi-attribute decision model with a relational database. The decision model enables the representation of the designer's preferences over the decision factors. A compatibility rating between the product profile requirements and the alternatives stored in the database for each decision criteria is generated using possibility theory. The vector of compatibility ratings are aggregated into a single rating of that alternative's compatibility. A ranked set of compatible material and manufacturing process alternatives is output by the system. This approach has advantages over existing systems that either do not have a decision module or are not integrated with a database.     

A decision support system for the planning of the workload on a grain terminal

Ships, loaded with agricultural products, are handled by GEM at three different terminals in the port of Rotterdam. Each terminal consists of several berths and has both floating equipment and shore equipment. This paper describes the terminal system, the planning process and a menu driven computer planning model of the system. The planning model is split up into a first phase, in which berths are allocated, and a second phase, in which unloading equipment is assigned. The user of the model has the opportunity to manipulate several penalties and assign preferences to berths.     

Industrial cost modelling and multi-objective optimisation for decision support in production systems development

Recent developments in cost modelling, simulation-based multi-objective optimisation, and post-optimality analysis have enabled the integration of costing data and cost estimation into a new methodology for supporting economically sound decision-making in manufacturing enterprises. Within this methodology, the combination of production engineering and financial data with multi-objective optimisation and post-optimality analysis has been proven to provide the essential information to facilitate knowledge-driven decision-making in real-world production systems development. The focus of this paper is to present the incremental cost modelling technique specifically designed for the integration with discrete-event simulation models and multi-objective optimisation within this methodology. A complete example, using the simulation model and data modified from a previous real-world case study, is provided in this paper to illustrate how the methodology and cost modelling are applied for the optimal investment decision support.     

An autonomous decision support system for manganese forecasting in subtropical water reservoirs

Manganese monitoring and removal is essential for water utilities in order to avoid supplying discoloured water to consumers. Traditional manganese monitoring in water reservoirs consists of costly and time-consuming manual lake samplings and laboratory analysis. However, vertical profiling systems can automatically collect and remotely transfer a range of physical parameters that affect the manganese cycle. In this study, a manganese prediction model was developed, based on the profiler's historical data and weather forecasts. The model effectively forecasted seven-day ahead manganese concentrations in the epilimnion of Advancetown Lake (Queensland, Australia). The manganese forecasting model was then operationalised into an automatically updated decision support system with a user-friendly graphical interface that is easily accessible and interpretable by water treatment plant operators. The developed tool resulted in a reduction in traditional expensive monitoring while ensuring proactive water treatment management.     

A peer IF-TOPSIS based decision support system for packaging machine selection

Selecting the appropriate manufacturing machine is a very important and complex problem for firms which usually have to deal with both qualitative and quantitative criteria and involve different decision makers whose knowledge is often vague and imprecise.This paper proposes a peer-based modification to intuitionistic fuzzy multi-criteria group decision making with TOPSIS method (peer IF-TOPSIS) and applies it to a packaging machine selection problem. Intuitionistic fuzzy weighted averaging (IFWA) operator has been selected both to obtain the group opinion on the relevance of the single decision makers and to aggregate individual opinions of decision makers for rating the importance of criteria and alternatives.A case study illustrates the application of the modified IF-TOPSIS method in order to select a Vertical Form Fill and Seal (VFFS) for Double Square Bottom Bag (DSBB) machine in food packaging.     

A knowledge based decision support system for computer performance management

Computer systems managers make decisions about hardware and software selection, performance evaluation, capacity planning, and other resource variables on the basis of factual data, accounting data, subjective judgements, and assumptions about the resource consumption of the jobs being run. The importance of computer resource planning calls for effective support methods. A Knowledge-Based DSS (KBDSS) will be able to assist managers in making these policy decisions by utilizing knowledge of the existing configuration and its capabilities, the organizational computing environment, available external resources, and their suppliers. Combining procedural and declarative methods, such a KBDSS may provide early warning of possible bottlenecks, forecast growth of hardware usage, and employ knowledge based inferencing to suggest suitable remedial actions to the systems manager. This paper presents a KBDSS for supporting computer resource planning decisions using a procedural/declarative framework, and illustrates the system's usage aspects.     

Development of a decision support system for service delivery

Most traditional service delivery models were developed to solve single objective problems. While the disaster recovery task usually needed the consideration of multiple objectives (e.g. the total waiting time, the total weighted time of travelling, the fairness of resource distribution). Therefore, the traditional models can't completely support the disaster recovery task. In the real world, the assignments of service delivery are always performed by the vehicle dispatchers or truck drivers based on their experiences. However, the intuitive assignment methods are lacking a mathematic basis. They may be efficient but not necessarily effective. In order to provide an efficient and effective decision support system, this study has focused on the general expression of performances for service delivery and modifies the traditional delivery models by rule-inference techniques. The objective of this paper is to describe how a decision support system has been developed to achieve the performance requirement in emergency service delivery tasks, while traditional routing algorithms are modified and software techniques are utilized under a PC-based environment. Furthermore, some directions for future improvement are proposed.     

New horizons at the core of information systems: decision support system advances

Here, we argue that decision support systems (DSSs) research is a core area of the information systems (IS) discipline, being one of six major expansions that have occurred in the IS field. Interestingly, DSS research is often blended with some other expansions experienced by the IS field: namely, organizational computing, electronic commerce/business, and pervasive computing. The DSS core of IS research continues to grow along ever-widening horizons. Diverse exemplars of such DSS advances are found in the papers of this special issue.     

A decision support system for the selection of computer-integrated manufacturing technologies

The adoption of computer-integrated manufacturing (CIM) offers manufacturing organizations many tangible and intangible benefits which enable them to produce products of high quality at low costs. However, the selection and evaluation of CIM is a complex process as it involves the consideration of many parameters to ensure that the selected technology meets the requirements of individual companies. This paper describes the development of a quantitative/qualitative decision support system for the evaluation of CIM which takes into consideration the objectives and operating characteristics of a company, thus ensuring that the selected technology matches the individual needs of that company. The methodology used in the decision support system is based on a combination of the analytical hierarchy process (AHP) and database technology. The AHP provides a means to consider both the tangible and the intangible benefits of CIM while databases are used to store the knowledge about the various benefits that CIM may offer. The system has been implemented in EXCEL, which fully automates the evaluation process. A case study is also presented to illustrate the capability of the proposed decision support system.     

The functions of a decision support system

The growing literature on decision support systems outlines their principal characteristics and presents case studies of successful systems. This paper reviews the literature, describes six functions that a decision support system may perform for a manager or staff analyst, and introduces a new technique, functional mapping, for representing these systems.     

Development of a decision support system for neuro-electrostimulation: Diagnosing disorders of the cardiovascular system and evaluation of the treatment efficiency

The study describes a preliminary stage of the decision support system development for physician performing neuro-electrostimulation of neck neural formations for patients suffering from cardiovascular system disorders. The arterial hypertension was used as the clinical model of the disorders. The study consisted of two steps: diagnosing of the arterial hypertension and an evaluation of the treatment efficiency during the neuro-electrostimulation application. For the diagnosing part, a clinical study was conducted involving heart rate variability signals recorded while performing tilt-test functional load. Heart rate variability signal is an indirect mean of accessing autonomic nervous system functioning. Disturbances of the autonomic nervous system are essential in pathology of arterial hypertension. Performance of different machine learning techniques and feature selection strategies in task of binary classification (healthy volunteers and patients suffering from arterial hypertension) were compared. The genetic programming feature selection and quadratic discriminant analysis classifier reached the highest classification accuracy. Best feature combinations were used to evaluate treatment efficiency. Predictions based on the selected heart rate variability features have a high level of agreement with the arterial pressure dynamics. The results indicate the potential of the proposed decision support system.     

Development of a decision support system for robot selection

With the availability of more different robot types and models along with their separate specifications, selecting the most appropriate robot is becoming more difficult and complicated for companies. Furthermore, a common set of robot selection criteria is not available for the decision makers. In this study, a two-phase robot selection decision support system, namely ROBSEL, is developed to help the decision makers in their robot selection decisions. In development of ROBSEL, an independent set of criteria is obtained first and arranged in the Fuzzy Analytical Hierarchy Process (FAHP) decision hierarchy. In the first elimination phase of the decision support system, the user obtains the feasible set of robots by providing limited values for the 15 requirements. ROBSEL, then, uses FAHP decision hierarchy to rank the feasible robots in the second phase. ROBSEL is illustrated and tested and several critical issues in its practical usage are explored in the paper. The applications of ROBSEL show that ROBSEL is a useful, practical and easy to use robot selection tool and improves robot selection decisions in the companies.     

Model management system for IRT-based test construction decision support system

Decision support system (DSS) has become widespread for some specific domains in recent years. However, DSS for IRT-based (item response theory) test construction has not yet been developed. This domain basically imposes a semi-structured or unstructured decision and, therefore, involves a very complex modeling process. This study develops a model management system (MMS) architecture to assist a non-expert user in manipulating test construction process efficiently and effectively. This architecture consists of four components: problem analysis, model type selection, model formulation and solver. The model type selection subsystem is further organized into three levels of hierarchy, i.e., environment, structure and parameter. A prototype is presented to demonstrate the feasibility of this architecture. The results indicate that this approach can be applied for providing an integrated, flexible and user-friendly DSS environment for producing better quality of results in less solution time.     

An integrated fuzzy multi criteria group decision making approach for ERP system selection

This paper aims to ease group decision-making by using an integration of fuzzy AHP (analytic hierarchy process) and fuzzy TOPSIS (technique for order preference by similarity to ideal solution) and its application to software selection of an electronic firm. Firstly, priority values of criteria in software selection problem have been determined by using fuzzy extension of AHP method. Fuzzy extension of AHP is suggested in this paper because of little computation time and much simpler than other fuzzy AHP procedures. Then, the result of the fuzzy TOPSIS model can be employed to define the most appropriate alternative with regard to this firm's goals in uncertain environment. Fuzzy numbers are presented in all phases in order to overcome any vagueness in decision making process. The final decision depends on the degree of importance of each decision maker so that wrong degree of importance causes the mistaken result. The researchers generally determine the degrees of importance of each decision maker according to special characteristics of each decision maker as subjectivity. In order to overcome this subjectivity in this paper, the judgments of decision makers are degraded to unique decision by using an attribute based aggregation technique. There is no study about software selection using integrated fuzzy AHP-fuzzy TOPSIS approach with group decision-making based on an attribute based aggregation technique. The results of the proposed approach and the other approaches are compared. Results indicate that our methodology allows decreasing the uncertainty and the information loss in group decision making and thus, ensures a robust solution to the firm.     

A simulation-based decision support system for a multi-echelon inventory problem with service level constraints

In this paper, we present a simulation-based decision support system for solving the multi-echelon constrained inventory problem. The goal is to determine the optimal setting of stocking levels to minimize the total inventory investment costs while satisfying the expected response time targets for each field depot. We derive new decision support algorithms to be applied in different scenarios, including small-sample and large-sample cases. The first case requires that the set of alternative solutions is known at the beginning of the experiment, and the number of evaluated solutions may depend on the simulation budget (i.e., the time available to solve the problem). In the second case, the alternative solutions are generated sequentially during the searching process, and we may terminate the algorithm when the specified sampling budget is exhausted. Empirical studies are conducted to compare the performance of the proposed algorithms with other conventional optimization approaches.     

Decision support for team staffing: An automated relational recommendation approach

Selecting individuals for teams is only rarely supported by IS. Existing systems only consider whether a person has the required technical skills and abilities for a job. Another important aspect is neglected — the match between the person and the team members in terms of interpersonal compatibility. We present a decision support system based on a relational recommendation approach for providing an automated pre-selection of candidates that fit best with future team members. The relational recommender contributes to theory by proposing an IS-supported relational approach to team staffing and to practice by offering time and cost savings for HR professionals.     

A dynamic decision support system to predict the value of customer for new product development

In recent years, firms have focused on how to enter markets and meet customer requirements by improving product attributes and processes to boost their market share and profits. Consequently, market-driven product design and development has become a popular topic in the literature. However, past research neither covers all of the major influencing factors that together drive customers to make purchase decisions, nor connects these various influencing factors to customer purchasing behavior. Past studies further fail to take the time value of money and customer value into consideration. This study proposes a decision support system to (a) predict customer purchasing behavior given certain product, customer, and marketing influencing factors, and (b) estimate the net customer lifetime value from customer purchasing behavior toward a specific product. This will not only enable decision-makers to compare alternatives and select competitive products to launch on the market, but will also improve the understanding of customer behavior toward particular products for the formulation of effective marketing strategies that increase customer loyalty and generate greater profits in the long term. Decision-makers can also make use of the system to build up confidence in new product development in terms of idea generation and product improvement. The application of the proposed system is illustrated and confirmed to be sensible and convincing through a case study.