A Prototype of Multi-Objective Group Decision Support System with a Group AggregationMethod base

This study develops a framework of integrating multi-objective decision support systems (MODSS), expert systems (ES) and group decision support systems (GDSS) effectively to deal with multi-objective decisionmaking problems in a group under knowledge-based intelligent guide. The three dimensions, MODSS, ES, GDSS, are combined to overcome the limitations of each basic system and maximally enhance the competence of the integrated system. As part of this study, this paper proposes a two-level multi-objective based group decision systems framework and five group aggregation methods. A group subsystem is then developed to include the five aggregation methods in a method base. This makes the exploration of group satisfactory solution more flexible and effective

A multi-agent crop production decision support system for technology transfer

The purpose of this research was to study agricultural crop production 'decision support systems' as a means of transferring agricultural technology from research labs and plots to producers, extension specialists, agriculture service agencies, and scientists, on the Western Canadian Prairies. A 'decision support system' is a computer program that analyses problems spanning several knowledge or problem areas producing results that aid the management decision-making process. The primary objective was to develop a computer application program that would fulfill the farm manager's decision support needs and be "open" to future enhancements. This interdisciplinary study has a strong agricultural presence in the application context of the resultant computerized agricultural decision support system, with agronomics being the foundation on which the system was built, and computer science being the toolbox used to build it. Farm Smart 2000 is the resultant decision support system, providing "single-window" access to three different tiers of decision support utilizing the Internet, ' expert systems' and integrated multiple heterogeneous 'reusable agents' in a cooperative problem-solving environment. An ' expert system' is a computer program that solves complicated problems, within a specific knowledge or problem area, that would otherwise require human expertise. Expert systems integrated with each other within a decision support system are called 'agents. Reusable agents' are modular computer programs (e.g. expert systems) which can be used in more than one computer application with little or no modification. Farm Smart 2000 provides support for most management aspects of crop production including variety selection, crop rotations, weed management, disease management, residue management, harvesting, soil conservation, and economics, for the crops of wheat, canola, barley, peas, and flax. Tier-3, the most sophisticated level of Farm Smart 2000, is the focus of this dissertation and utilizes multiple reusable agents, integrating them such that they cooperate together to solve complex interrelated crop production problems. A Global Control Expert achieves the required communication and coordination among the agents resulting in an "open system", enabling Farm Smart 2000 to extend its problem-solving capabilities by integrating additional agents and knowledge, without system re-engineering, thereby remaining an ongoing technology transfer vehicle

Expert Systems for Integrated Development: A Case Study of Shanxi Province, The People's Republic of China

The research and development project described in this status report is a collaborative project between IIASA and the Tate Science and Technology Commission of the People's Republic of China (SSTCC). The project objective is to build a computer-based information and decision support system, using expert systems technology, for regional development planning in Shanxi, a coal-rich province in northwestern China. Building on IIASA's experience in applied systems analysis, the project develops and implements a new generation of computer-based tools, integrating classical approaches of operations research and applied systems analysis with new developments in computer technology and artificial intelligence (AI) into an integrated hybrid system, designed for direct practical application. To provide the required information, several databases, simulation and optimization models, and decision support tools have been integrated. This information is presented in a form directly useful to planners and decision makers. The system is therefore structured along concepts of expert systems technology, includes several AI components, and features an easy-to-use color graphics user interface. The study is being carried out with intensive collaboration between IIASA, and Chinese academic, industrial, and governmental institutions, especially the regional government of Shanxi Province. The report describes the status of the project after one year of research, summarizing the problem area, the design principles of the software and the current status of prototype implementations

Modeling knowledge bases for automated decision making systems - a literature review

Developing automated decision making systems means dealing with knowledge in every possible manner. One of the most important points of developing artificial intelligent systems is developing a precise knowledge base with integrating self-learning mechanisms. Moreover using knowledge in expert systems or decision support systems it is necessary to document knowledge and make it visible for managing it. Main goal of this work is finding a suitable solution for modeling knowledge bases in automated decision making systems concerning both illustrating specific knowledge and learning mechanisms. There are a lot of different terms describing this kind of research, such as knowledge modeling, knowledge engineering or ontology engineering. For that reason this paper provides a comparison of the technical terms in this domain by illustrating similarities, specifics and how they are used in literature

Python Library for Consumer Decision Support System with Automatic Identification of Preferences

The development of information systems (IS) has increased in the e-commerce field. The need for continuous improvement of decision support systems implies the integration of multiple methodologies such as expert knowledge, data mining, big data, artificial intelligence, and multicriteria decision analysis (MCDA) methods. Artificial intelligence algorithms have proven their effectiveness as an engine for data-driven information systems. MCDA methods demonstrated usefulness in domains dealing with multiple dimensions. One of the most critical points of any MCDA procedure is criteria weighting using subjective or objective methods. However, both approaches have several limitations when there is a need to map the preferences of unavailable experts. EVO-SPOTIS library integrating a stochastic evolutionary algorithm with the MCDA method, introduced in this paper, attempts to address this problem. In this approach, the Differential Evolution (DE) algorithm is used to identify decision-makers’ preferences based on datasets evaluated by experts in the past. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) method is used to compute the DE objective function’s values and perform the final evaluation of alternatives using the identified weights. Results confirm the high potential of the library for identification preferences and modeling customer behavior

Utilisation des outils numériques d'aide à la décision pour la gestion de l'eau

Le succès d'une gestion des écosystèmes naturels requiert une connaissance approfondie des différents processus qui interviennent et de leurs échelles de temps et d'espace particulières. Pour cette raison, les décideurs ont besoin d'analyser une vaste gamme de données et d'informations géographiques. Les modèles mathématiques, les systèmes d'informations géographi-ques et les systèmes experts sont capables de produire cette analyse, mais seule une minorité de gestionnaires les utilise actuellement. Cet article identifie quelques unes des raisons à l'origine de l'hésitation des gestionnaires à adopter de tels outils d'aide à la décision pour la gestion des ressources naturelles et propose une structure qui pourrait faciliter leur utilisation pour le processus de prise de décision. Cet exercice est réalisé à l'intérieur du contexte de la gestion intégrée par bassin. Une revue des systèmes d'aide à la décision est également présentée.Many methods of integrated or watershed management exist which account for the necessary biophysical and socio-economic factors at the watershed level. Some of these approaches are ecosystem oriented while others are socio-economically oriented. Whatever the definition, water management at the watershed level needs to account for a plenitude of variables related to the air, water, soil, biology, and economy. The successful management of natural ecosystems requires a thorough understanding of their characteristic time and spatial scales. Because of this, decision makers need to analyze a wide range of data and geographic information. Mathematical models, geographic information systems and expert systems are capable of performing this analysis, but only a minority of managers are currently using them. This paper identifies some of the reasons why ecosystem managers have been slow to adopt such decision support tools in natural resources management and proposes a framework to facilitate their use in the decision making process. This is done in an integrated watershed management context. A review of related decision support systems is also presented.Four types of decision-support tools are introduced : mathematical models, expert-systems, geographical information systems (GIS) and decision support systems (DSS). Mathematical models have long been used for simulation, prediction, and forecasting, however, they are often task specific and were rarely developed for management uses. GIS are more and more commonly being used for decision support as they become more affordable and user-friendly and are very well-suited for managing resources at a spatial scale. There exist many kinds of software ranging from a simple viewer used for cartographic purposes to complex GIS oriented toward spatial analysis and modelling. Expert systems are also interesting for decision support when specific goals are being considered. Finally, DSS are perhaps the digital tools most applicable to management purposes, often integrating one or more models, a GIS or expert system functionalities. There are two types of DSS : 1. Environmental Information Systems (EIS), and 2. Integrated Modelling Systems (IMS) EIS can be very user- friendly, relying heavily upon GIS and statistical functions.IMS also use GIS capabilities, but integrates several mathematical models as well. The level of integration between models varies considerably and the complexity of IMS are generally high.Two questions underlie the operational use of digital technologies for decision support. The first is whether or not such technology should be used at all, while the second is why such tools take time to be adopted by government and management agencies. The use of digital technologies is often required when the problem is complex and where there are a wide range of factors involved with different spatial and temporal scales. Three major constraints towards the implementation of decision support tools can be pinpointed :1. technology, 2. data, and 3. working organization. Technological constraints include cost, lack of user friendliness, and hardware problems, among other factors. Data constraints are mostly related to availability, cost, heterogeneity and volume. Finally, organization constraints pertain mostly to the manager's perception of the tool and the structural integration of the tool within the decision process.This paper proposes a 4-step approach to optimize the use of decision-support tools. The first step requires that managers and decision-makers clearly define their project, goals and budget, as well as, decide whether to use an integrated watershed management approach or a more discrete approach. This leads directly to the second step, which consists of choosing the most appropriate digital support tool. This requires communication between managers and scientists, and at this point, data gathering and integration should begin. The third phase consists of the development of a new tool or adaptation of an existing one within the context of the agency's management structure. The final step is the operational use of the decision support tool by the agency, following an initial trial period. The successful use of a decision support tool for management purposes depends on proper planning that accounts for all factors related to management needs, budget, data, ease of use, and organization integration

Event Monitoring System to Classify Unexpected Events for Production Planning

[EN] Production planning prepares companies to a future production scenario. The decision process followed to obtain the production plan considers real data and estimated data of this future scenario. However, these plans can be affected by unexpected events that alter the planned scenario and in consequence, the production planning. This is especially critical when the production planning is ongoing. Thus providing information about these events can be critical to reconsider the production planning. We herein propose an event monitoring system to identify events and to classify them into different impact levels. The information obtained from this system helps to build a risk matrix, which determines the significance of the risk from the impact level and the likelihood. 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