Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

Sistema de Ayuda a la Toma de Decisiones para la gestión de Incidencias

[ES] Este artículo aborda los DSS, sistemas de ayuda a la toma de decisiones, cuando estas deben tomarse por incidencias en la empresa. La incidencia no es más que un evento no programado por la empresa, que puede repercutirnos negativamente en la empresa. Con lo que tenemos que crear métodos para actuar en su resolución de forma rápida y eficaz. Lo principal es definir los tipos de decisión por su forma de resolución, tanto por estar guiadas por las políticas y las normas de la empresa, como por quien toma las decisiones, creando una jerarquía en la toma de decisiones en la empresa, y dando importancia a una adecuada recogida de información y un tratamiento efectivo de la misma por la organización.[EN] This article talks about how the DSS (Decision Support Systems), working in support of decision making when these incidents are given by the company. The incidence is an event not expected by the company, which may have a negative impact in the company. So, it is necessary developing methods to act on its resolution quickly and efficiently. The main thing is to define the types of decisions by way of resolution, both being guided by the policies and rules of the company, as the decision maker, creating a hierarchy in decision-making in the company, and giving importance a good collection of information and the most effective treatment of this for use in the company.Esta investigación se ha llevado a cabo en el marco del proyecto financiado por el Vicerrectorado de Investigación de la Universitat Politècnica de València titulado “Sistema de ayuda a la toma de decisiones ante decisiones no programadas en la planificación jerárquica de la producción (ADENPRO-PJP)” Ref. SP20120703.Valero, R.; Boza García, A.; Vicens Salort, E. (2013). Sistema de Ayuda a la Toma de Decisiones para la gestión de Incidencias. En Industrial Engineering and Complexity Management. Grupo INSISOC. 1043-1049. http://hdl.handle.net/10251/78108S1043104

The Socio-Economic Impacts of the Porong Mud Volcano on the Shrimp Fisheries Sector in Sidoarjo District, East Java Province, Indonesia

This thesis analyses the socioeconomic impact of the Sidoarjo mud volcano on shrimp fisheries’ production. It adapts the sustainable livelihoods framework in order to investigate how local livelihood assets are utilised to maintain and adapt livelihood strategies in response to river pollution in the context of a local and globalised aquaculture industry

Categorization of disaster decision support needs for the development of an integrated model for DMDSS

The wide variety of disasters and the large number of activities involved have resulted in the demand for separate Decision Support System (DSS) models to manage different requirements. The modular approach to model management is to provide a framework in which to focus multidisciplinary research and model integration. A broader view of our approach is to provide the flexibility to organize and adapt a tailored DSS model (or existing modular subroutines) according to the dynamic needs of a disaster. For this purpose, the existing modular subroutines of DSS models are selected and integrated to produce a dynamic integrated model focussed on a given disaster scenario. In order to facilitate the effective integration of these subroutines, it is necessary to select the appropriate modular subroutine beforehand. Therefore, subroutine selection is an important preliminary step towards model integration in developing Disaster Management Decision Support Systems (DMDSS). The ability to identify a modular subroutine for a problem is an important feature before performing model integration. Generally, decision support needs are combined, and encapsulate different requirements of decision-making in the disaster management area. Categorization of decision support needs can provide the basis for such model selection to facilitate effective and efficient decision-making in disaster management. Therefore, our focus in this paper is on developing a methodology to help identify subroutines from existing DSS models developed for disaster management on the basis of needs categorization. The problem of the formulation and execution of such modular subroutines are not addressed here. Since the focus is on the selection of the modular subroutines from the existing DMDSS models on basis of a proposed needs classification scheme

CATEGORIZATION OF DISASTER DECISION SUPPORT NEEDS FOR THE DEVELOPMENT OF AN INTEGRATED MODEL FOR DMDSS

The wide variety of disasters and the large number of activities involved have resulted in the demand for separate Decision Support System (DSS) models to manage different requirements. The modular approach to model management is to provide a framework in which to focus multidisciplinary research and model integration. A broader view of our approach is to provide the flexibility to organize and adapt a tailored DSS model (or existing modular subroutines) according to the dynamic needs of a disaster. For this purpose, the existing modular subroutines of DSS models are selected and integrated to produce a dynamic integrated model focussed on a given disaster scenario. In order to facilitate the effective integration of these subroutines, it is necessary to select the appropriate modular subroutine beforehand. Therefore, subroutine selection is an important preliminary step towards model integration in developing Disaster Management Decision Support Systems (DMDSS). The ability to identify a modular subroutine for a problem is an important feature before performing model integration. Generally, decision support needs are combined, and encapsulate different requirements of decision-making in the disaster management area. Categorization of decision support needs can provide the basis for such model selection to facilitate effective and efficient decision-making in disaster management. Therefore, our focus in this paper is on developing a methodology to help identify subroutines from existing DSS models developed for disaster management on the basis of needs categorization. The problem of the formulation and execution of such modular subroutines are not addressed here. Since the focus is on the selection of the modular subroutines from the existing DMDSS models on basis of a proposed needs classification scheme.Decision support systems, disaster management systems, model selection, model management

CATEGORIZATION OF DISASTER DECISION SUPPORT NEEDS FOR THE DEVELOPMENT OF AN INTEGRATED MODEL FOR DMDSS

The wide variety of disasters and the large number of activities involved have resulted in the demand for separate Decision Support System (DSS) models to manage different requirements. The modular approach to model management is to provide a framework in which to focus multidisciplinary research and model integration. A broader view of our approach is to provide the flexibility to organize and adapt a tailored DSS model (or existing modular subroutines) according to the dynamic needs of a disaster. For this purpose, the existing modular subroutines of DSS models are selected and integrated to produce a dynamic integrated model focussed on a given disaster scenario. In order to facilitate the effective integration of these subroutines, it is necessary to select the appropriate modular subroutine beforehand. Therefore, subroutine selection is an important preliminary step towards model integration in developing Disaster Management Decision Support Systems (DMDSS). The ability to identify a modular subroutine for a problem is an important feature before performing model integration. Generally, decision support needs are combined, and encapsulate different requirements of decision-making in the disaster management area. Categorization of decision support needs can provide the basis for such model selection to facilitate effective and efficient decision-making in disaster management. Therefore, our focus in this paper is on developing a methodology to help identify subroutines from existing DSS models developed for disaster management on the basis of needs categorization. The problem of the formulation and execution of such modular subroutines are not addressed here. Since the focus is on the selection of the modular subroutines from the existing DMDSS models on basis of a proposed needs classification scheme.Decision support systems, disaster management systems, model selection, model management