Model-Based Decision Support Methodology with Environmental Applications

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

Distributed decision support and organizational connectivity: A case study

Product development decisions represent challenging processes with various stakeholders from different disciplines and with different perspectives involved. Decisions can be highly complex due to the high amount of influencing factors that have to be considered simultaneously. Among other methods and tools, decision support systems (DSS) were developed to support decision-makers in handling complex decisions. One main disadvantage of DSS is the little adaptation to and integration of decision-maker needs and characteristics. Thus, current trends focus on the development of human- and decision-maker-centered DSS. This paper aims in providing a multilevel stepwise procedure for the modeling of decision-makers in order to enable a more effective decision support. The decision-maker modeling procedure is based on findings from literature research and results from research projects performed in cooperation with industry partners. The procedure serves as a guideline for generating diverse decision-maker models for different applications and for integrating human cognitive thinking procedures within the design and functionality of decision support methodologies and systems. The procedure represents an important design element of the human-centered decision support methodology currently being developed at the Institute of Product Development of the Technische Universität München (Germany).

This paper aims to introduce the Decision Support System (DSS), which is an area informatics approach to area studies, and the research network complex based on DSS's application in Thailand. In particular, the paper presents and discusses current activities of the DSS research network at the national level in Thailand rather than being a theoretical and analytical study of the DSS mechanism or its cases. DSS has wide applications in Thailand, extending to various fields such as agricultural and natural resource management, economic and social management, historical and cultural preservation, and so on. DSS generally refers to a support system embedded into a computer system for providing intellectual resources with the appropriate numerical model necessary for decision making in production activities. Agricultural and natural resources have been the foundation of social and economic development in Thailand since the country's first national plan in the1960s. Decision making to maintain agricultural productivity as well as to protect natural resources requires well-integrated data sets. The Thailand Research Fund(TRF) established a DSS research and development network (TRF-DSS) in 2002 to help various research teams in Thailand develop DSS tools and components for addressing agricultural and natural resource management issues. Using a "systems"approach, the DSS framework allows researchers and users to identify and integrate key components as well as to define databases and model-base management systems. This paper focuses on the TRF-DSS research and development network, which consists of 12 universities, two line agencies in Thailand, and a line agency in Cambodia. During 2002–10, a total of 59 research projects were granted a budget allocation of 140.1 million baht, and a budget of 15.4 million baht was allocated to support activities of the network. In addition, 10 projects were funded to carry out post-project activities, with a total budget of 1.5 million baht. More than 20 DSS tools were developed and implemented by various users, ranging from policy makers to provincial and local government agencies engaged in short- and long-term planning and management. Most DSS tools were designed to allow the integration of biophysical and socioeconomic data as well as the decision support modules for alternatives evaluation and analysis. These approaches support the choices of dynamic simulation models as well as multi-criteria analyses for modelbase software development. They also allow users to evaluate various alternatives in agricultural and natural resource management. Networking is a powerful platform for DSS research and development and maybe applied to other types of research and development agenda in Thailand, such as area study projects. DSS tools contribute to an understanding of sustainability of production systems against a background of climate change, poverty reduction, and food security.

The increasing frequency of disasters highlights the critical need for efficient and adaptable humanitarian logistics systems.To enhance decision-making in these complex environments, it is essential to incorporate advanced mathematical models through the development of Decision Support Systems (DSS).This study presents the development of a DSS prototype aimed at optimizing the location of temporary Points of Distribution (PODs) following a large-scale earthquake scenario in Alto Selva Alegre, Arequipa, Peru.The system distinguishes between two types of PODs: fixed PODs, which remain operational throughout all periods and serve as stable infrastructure during both the response and recovery phases, and temporary PODs, which are activated only during the initial response to maximize proximity to the affected population.The DSS is composed of four core components-a database, an optimization model, a user interface, and a communication layer-and is structured into five interactive tabs. The Data tab presents input information; Optimization tab enables model execution and parameter adjustments based on decision-maker knowledge, resource availability, or operational priorities; Solution visualizes results by period; andComparative allows users to analyze, and contrast saved configurations.Results show that small changes in input parameters-such as the number of modules or active PODs-can lead to significant improvements in coverage and travel distance, with only modest increases in cost.The DSS also adapts well to operational constraints, providing feasible solutions even when some sites are unavailable.By combining mathematical modelling with interactive usability, it supports quick scenario comparison and informed decision-making, offering a practical tool for humanitarian logistics planning.

Changing systems for supporting farmers' decisions: problems, paradigms, and prospects

Tobacco (Nicotiana tabacum) production plays a crucial role in the agricultural economy of several regions around the world, especially in developing countries, such as Brazil. However, the lack of well-defined terminology and semantic models harms the development of decision support systems. Conceptualizing the tobacco production domain is challenging due to its ambiguous terminology and the complexity involved in considering environmental, soil, disease, and pest management factors. We present an Ontology of Tobacco Production (OnTop), designed to assist tobacco production in optimizing crop management practices and placing environmental factors within a formal framework. To the best of available knowledge, this study is the first to formalize the tobacco production lifecycle integrated with soil and climate data. It includes symbolic and description logic for reasoning and automation. This paper presents the proposal for the core set (main classes) and the application of OnTop to enrich a Large Language Model (LLM) to provide an ontology-based decision support prompt. OnTop allows actionable recommendations based on environmental, agronomic, and productivity data. The main contributions of this paper are: i) a domain ontology that formalizes knowledge on data-driven tobacco production; and ii) a queryable framework (enriched LLM), which allows experts to obtain complex agronomic answers. OnTop offers an extensible framework for decision making in tobacco farming and paves the way for further innovations in ontologies and LLM-based decision support systems for the agricultural domain.

A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.

We describe a loosely integrated set of teaching modules that allow students to explore decision support systems (DSS) topics ranging from data warehousing to model based decision support. The modules are used as part of a spreadsheet based modeling approach to teaching DSS. We describe one particular module in detail in which students are forced to deal with a muddy data set as a prelude to analysis, modeling and decision support system development.

Model-based Decision Support Systems (DSSs) employ various types of models, such as statistical, optimization, simulation, or rule-based. Models are used to assess and analyze the given decision situation, and on this basis advise the decision-maker. Generally, the DSS development process involves three steps: (1) model development, (2) implementing the model(s) in a DSS, and (3) using the DSS. In this chapter, we focus on two model development approaches: Data Mining and Expert Modeling. We advocate for combing the two in order to get better models and better DSSs in general. We illustrate some points and potential pitfalls using an example of the PD_manager DSS, which is aimed at supporting medication change decisions in the management of Parkinson’s disease. Based on the experience from PD_manager and some other DSS development projects, we propose the so-called 5C requirements for better DSS models: correctness, completeness, consistency, comprehensibility, and convenience. Finally, we summarize the lessons learned and give advice to DSS developers and researchers.

World experience shows that intelligent information systems for decision support are an integral part of modern society functioning. The basis of many tasks that lies in the development of decision support systems is the presentation of knowledge of a particular subject field. Each decision-support system, depending on the application field, has its own characteristics that characterize the main objectives of this subject area, among which, for example, the presentation of knowledge of multi-level administration systems for decision support purposes. In modern conditions, mathematical modeling is the most effective for the formal representation of knowledge in decision support systems which can be used to represent declarative knowledge of land relations. Therefore, urgent scientific practical problem is relevant now that lies in the representing contradictory knowledge in multilevel administration systems. The scientific and practical task envisages the transformation of declarative knowledge of the subject field in the form of mathematical and informational models using elements of set theory. The task envisages the transformation of declarative knowledge in the form of mathematical models with using set theory. The research used methods: analysis, set theory, mathematical modeling, corpus linguistics, ontological engineering. The results of the study are important for organizing activities in a variety of industries using a large body of documents and laws. The purpose of this work is to develop models of knowledge representation on the use of set theory in ontology. The object of research is the process of decision support in multilevel systems. Subject of research is the mathematical models of knowledge representation in multilevel administration systems using set theory. Keywords: formalization, theory, predicate logic, corpus linguistics, ontology, set theory, model.

This paper examines how an object called a decision support system (DSS) is a system from the point of view of GST (general systems theory) and how GST is related with the development of DSS in a positive way. Since a DSS is a complicated system, it cannot be described by one model. We will proceed the consideration in the following different levels: functional level, structural level, process level and physical level. We will show that on each level GST provides a useful framework to deal with a DSS and the general formulation by GST directs what is to be done in the development of DSS. On the progress of the development we have found GST can help us with conceptualizing a DSS which is most important in finding a correct research direction. The presented models are the basic framework of our current system, a product of GST.

The growth in the number of cloud computing users has led to the availability of a variety of cloud based services provided by different vendors. This has made the task of selecting suitable set of services quite difficult. There has been a lot of research towards the development of suitable decision support system (DSS) to assist users in making an optimal selection of cloud services. However, existing decision support systems cannot address two crucial issues: firstly, the involvement of both business and technical perspectives in decision making simultaneously and, secondly, the multiple-clouds services based selection using single DSS. In this paper, we tackle these issues in the light of solving the problem of cloud service discovery. In particular, we present the following novel contributions: Firstly, we present critical analysis of the state-of-the-art in decision support systems. Based on our analysis, we identify critical shortcomings in the existent tools and develop the set of requirements which should be met by a potential DSS. Secondly, we present a new holistic framework for the development of DSS which allows a pragmatic description of user requirements. Additionally, the data gathering and analysis is studied as an integral part of the proposedDSS and therefore, we present concrete algorithms to assess the data for an optimal service discovery. Thirdly, we assess our framework for applicability to cloud service selection using an industrial case study. We also demonstrate the implementation and performance of our proposed framework using a prototype which serves as a proof of concept. Overall, this paper provides novel and holistic framework for development of a multiple cloud service discovery based decision support system.

Integrated pest management (IPM) prioritizes non-chemical alternatives for managing pests and/or diseases, reserving chemicals as last solution, when economically and environmentally justified. The holistic consideration of the agroecosystem makes decision-making in crop protection more complex, as it requires selecting the most appropriate action at the right time. Decision support systems (DSS) for IPM help manage the risks of pests and diseases affecting crops by predicting potential threats and suggesting suitable control measures. However, their adoption remains modest among farmers, despite demonstrated benefits. An anonymous online survey was conducted among farmers, advisors, and other agricultural stakeholders to investigate factors influencing DSS uptake, or lack thereof. The survey distinguished between DSS users and non-users, enabling targeted questions on different aspects of DSS use and adoption. In total, 78 respondents were classified as DSS users and 29 as DSS non-users . Data were analyzed through clustering, followed by statistical validation of clusters and induction of a classification tree, to identify the most influential features shaping cluster formation among both DSS users and DSS non-users . The analysis identified four clusters in the DSS users’ dataset and two clusters in the DSS non-users’ dataset. For DSS users , DSS output reliability and usefulness were identified as the most influential features. Membership in a farmers’ network and DSS ease of use also contributed to cluster separation. For DSS non-users , DSS availability for different cropping systems was the most important attribute determining cluster separation. The results indicate that farmer involvement in DSS development and calibration may enhance trust and, therefore, adoption. Moreover, the need for user-friendly systems providing simple and clear information was highlighted. Although the lack of DSS tailored to specific cropping systems emerged as the main barrier, limited awareness and concerns about costs were also important obstacles. The applied methodological approach presents a reproducible and robust AI-based framework, built on open-source tools, to analyze stakeholders’ perceptions of IPM DSS and extract knowledge from complex survey data. This study provides valuable insights to facilitate DSS adoption for more sustainable crop protection and underscores the importance of both DSS technical performance and end-user needs.

: Mathematical modeling is becoming a vital tool in engineering design. These models have been used to provide decision support to designers of systems characterized by many design choices. However, for the design of complex systems, these models become too large and cumbersome to be of significant value. As a result, research has progressed in the decomposition of the design problem. A decision support system, serving as a computer-based aid for engineers designing weapon systems, must integrate procedures dealing with designing for producibility and designing for supportability with those dealing with designing for performance cost and schedule (ULCE Implementation Plan, 1987). One of the characteristics of large weapon system development that causes difficulties in achieving this integration is the hierarchical nature of the design process. Typically, a system is designed in terms of subsystems which are required to meet certain specifications. Problems begin to arise when these specifications cannot be met. Consequently, these design processes may be enhanced by a computer-based system that supports the hierarchical nature of design. This research examines the application of mathematical modeling and knowledge-based system techniques to the development of decision support systems to support engineering design.