Decision making models embedded into a web-based tool for assessing pest infestation risk

Current practices in agricultural management involve the application of rules and techniques to ensure high quality and environmentally friendly production. Based on their experience, agricultural technicians and farmers make critical decisions affecting crop growth while considering several interwoven agricultural, technological, environmental, legal and economic factors. In this context, decision support systems and the knowledge models that support them, enable the incorporation of valuable experience into software systems providing support to agricultural technicians to make rapid and effective decisions for efficient crop growth. Pest control is an important issue in agricultural management due to crop yield reductions caused by pests and it involves expert knowledge. This paper presents a formalisation of the pest control problem and the workflow followed by agricultural technicians and farmers in integrated pest management, the crop production strategy that combines different practices for growing healthy crops whilst minimising pesticide use. A generic decision schema for estimating infestation risk of a given pest on a given crop is defined and it acts as a metamodel for the maintenance and extension of the knowledge embedded in a pest management decision support system which is also presented. This software tool has been implemented by integrating a rule-based tool into web-based architecture. Evaluation from validity and usability perspectives concluded that both agricultural technicians and farmers considered it a useful tool in pest control, particularly for training new technicians and inexperienced farmers.

Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support

As climate change continues to impact socio-ecological systems, tools that assist conservation managers to understand vulnerability and target adaptations are essential. Quantitative assessments of vulnerability are rare because available frameworks are complex and lack guidance for dealing with data limitations and integrating across scales and disciplines. This paper describes a semi-quantitative method for assessing vulnerability to climate change that integrates socio-ecological factors to address management objectives and support decision-making. The method applies a framework first adopted by the Intergovernmental Panel on Climate Change and uses a structured 10-step process. The scores for each framework element are normalized and multiplied to produce a vulnerability score and then the assessed components are ranked from high to low vulnerability. Sensitivity analyses determine which indicators most influence the analysis and the resultant decision-making process so data quality for these indicators can be reviewed to increase robustness. Prioritisation of components for conservation considers other economic, social and cultural values with vulnerability rankings to target actions that reduce vulnerability to climate change by decreasing exposure or sensitivity and/or increasing adaptive capacity. This framework provides practical decision-support and has been applied to marine ecosystems and fisheries, with two case applications provided as examples: (1) food security in Pacific Island nations under climate-driven fish declines, and (2) fisheries in the Gulf of Carpentaria, northern Australia. The step-wise process outlined here is broadly applicable and can be undertaken with minimal resources using existing data, thereby having great potential to inform adaptive natural resource management in diverse locations.

Agent based simulation as a novel decision support tool for retail managers

Intelligent agents offer a new and exciting way of understanding the world of work. We apply agent-based simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Our multi-disciplinary research team draws upon expertise from work psychologists and computer scientists. Our research so far has led us to conduct case study work with a top ten UK retailer. Based on our case study experience and data we are developing a simulator that can be used to investigate the impact of management practices (e.g. training, empowerment, teamwork) on customer satisfaction and retail productivity.

Building an Improved Community Support System

This research will serve to evaluate the current processes and procedures in place for the Community Case Management training, data entry, and quality assurance at the Department of Juvenile Justice. The goal of the research is to identify and establish a framework for a universal unit within the agency that will enhance the effectiveness of Case Management by consolidating and streamlining information to reduce conflicting standards to create a stronger support unit, and to facilitate learning and understanding for the staff.

Contribution margin model applied to the marketing planning of a container liner carrier

The implementation of confidential contracts between a container liner carrier and its customers, because of the Ocean Shipping Reform Act (OSRA) 1998, demands a revision in the methodology applied in the carrier's planning of marketing and sales. The marketing and sales planning process should be more scientific and with a better use of operational research tools considering the selection of the customers under contracts, the duration of the contracts, the freight, and the container imbalances of these contracts are basic factors for the carrier's yield. This work aims to develop a decision support system based on a linear programming model to generate the business plan for a container liner carrier, maximizing the contribution margin of its freight.

PERFORMANCE OF THE MODEL CALLED ACQUANETXL IN THE ALLOCATION OF WATER IN COMPLEX WATER RESOURCE SYSTEMS

This article presents a tool for the allocation analysis of complex systems of water resources, called AcquaNetXL, developed in the form of spreadsheet in which a model of linear optimization and another nonlinear were incorporated. The AcquaNetXL keeps the concepts and attributes of a decision support system. In other words, it straightens out the communication between the user and the computer, facilitates the understanding and the formulation of the problem, the interpretation of the results and it also gives a support in the process of decision making, turning it into a clear and organized process. The performance of the algorithms used for solving the problems of water allocation was satisfactory especially for the linear model.

Planning and operation of large-scale water distribution systems with preemptive priorities

This paper describes the development of an optimization model for the management and operation of a large-scale, multireservoir water supply distribution system with preemptive priorities. The model considers multiobjectives and hedging rules. During periods of drought, when water supply is insufficient to meet the planned demand, appropriate rationing factors are applied to reduce water supply. In this paper, a water distribution system is formulated as a network and solved by the GAMS modeling system for mathematical programming and optimization. A user-friendly interface is developed to facilitate the manipulation of data and to generate graphs and tables for decision makers. The optimization model and its interface form a decision support system (DSS), which can be used to configure a water distribution system to facilitate capacity expansion and reliability studies. Several examples are presented to demonstrate the utility and versatility of the developed DSS under different supply and demand scenarios, including applications to one of the largest water supply systems in the world, the Sao Paulo Metropolitan Area Water Supply Distribution System in Brazil.

Parameterization and Evaluation of Predictions of DSSAT/CANEGRO for Brazilian Sugarcane

The DSSAT/CANEGRO model was parameterized and its predictions evaluated using data from five sugarcane (Sacchetrum spp.) experiments conducted in southern Brazil. The data used are from two of the most important Brazilian cultivars. Some parameters whose values were either directly measured or considered to be well known were not adjusted. Ten of the 20 parameters were optimized using a Generalized Likelihood Uncertainty Estimation (GLUE) algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index (LA!), stalk and aerial dry mass, sucrose content, and soil water content, using bias, root mean squared error (RMSE), modeling efficiency (Eff), correlation coefficient, and agreement index. The Decision Support System for Agrotechnology Transfer (DSSAT)/CANEGRO model simulated the sugarcane crop in southern Brazil well, using the parameterization reported here. The soil water content predictions were better for rainfed (mean RMSE = 0.122mm) than for irrigated treatment (mean RMSE = 0.214mm). Predictions were best for aerial dry mass (Eff = 0.850), followed by stalk dry mass (Eff = 0.765) and then sucrose mass (Eff = 0.170). Number of green leaves showed the worst fit (Eff = -2.300). The cross-validation technique permits using multiple datasets that would have limited use if used independently because of the heterogeneity of measures and measurement strategies.

Shopping for apparel: how can kiosk systems help?

When shopping for apparel, many consumers seek advice from friends and family or store personnel. In-store kiosk systems might serve as an alternative decision support system. In the present study we address the key question of how such kiosk systems are evaluated by consumers. We conducted three focus group discussions with regular apparel shoppers aged between 23 and 39 years. In sum, qualitative information from 15 participants was subject to a qualitative content analysis with the aim of gaining a more comprehensive understanding of how apparel shoppers experience the shopping process. Getting a more in-depth understanding of the needs and wishes associated with the apparel shopping process gives a basis for evaluating the potential acceptance of electronic decision support systems in apparel shopping. Although our study is exploratory in nature, we are able to draw an initial picture of how kiosk systems could be used in apparel shopping.

Modelo de gestão municipal baseado na utilização estratégica de recursos da tecnologia da informação para a gestão governamental: formatação do modelo e avaliação em um município

São desafios constantes da gestão efetiva dos municípios a estruturação e disponibilização de informações confiáveis, oportunas e personalizadas para apoiar as decisões da administração pública municipal e para elaborar e controlar o planejamento estratégico municipal alinhado aos anseios dos cidadãos. A adaptação de modelos de gestão da iniciativa privada para o ambiente público é uma alternativa para enfrentar esses desafios. Este artigo propõe e avalia um modelo para a gestão governamental. O modelo é baseado na utilização estratégica da tecnologia da informação, que proporcione ao gestor público monitoração e controle da execução estratégica, informações executivas para a tomada de decisão, gestão dos relacionamentos com os cidadãos e o domínio sobre os processos da gestão municipal. A metodologia da pesquisa enfatizou o estudo de caso no município de Curitiba, utilizando um protocolo de pesquisa elaborado a partir da pesquisa bibliográfica exploratória. A seguir, são analisados diferenças, similaridades e resultados da aplicação de elementos que compõem o modelo proposto no município estudado. A conclusão evidencia que a utilização e adaptação do modelo proposto nas gestões municipais podem contribuir significativamente na evolução de seus modelos de gestão.

Sistema de apoio à decisão para a classificação de risco em projectos de tecnologias de informação

O trabalho que a seguir se apresenta tem como objectivo descrever a criação de um modelo que sirva de suporte a um sistema de apoio à decisão sobre o risco inerente à execução de projectos na área das Tecnologias de Informação (TI) recorrendo a técnicas de mineração de dados. Durante o ciclo de vida de um projecto, existem inúmeros factores que contribuem para o seu sucesso ou insucesso. A responsabilidade de monitorizar, antever e mitigar esses factores recai sobre o Gestor de Projecto. A gestão de projectos é uma tarefa difícil e dispendiosa, consome muitos recursos, depende de numerosas variáveis e, muitas vezes, até da própria experiência do Gestor de Projecto. Ao ser confrontado com as previsões de duração e de esforço para a execução de uma determinada tarefa, o Gestor de Projecto, exceptuando a sua percepção e intuição pessoal, não tem um modo objectivo de medir a plausibilidade dos valores que lhe são apresentados pelo eventual executor da tarefa. As referidas previsões são fundamentais para a organização, pois sobre elas são tomadas as decisões de planeamento global estratégico corporativo, de execução, de adiamento, de cancelamento, de adjudicação, de renegociação de âmbito, de adjudicação externa, entre outros. Esta propensão para o desvio, quando detectada numa fase inicial, pode ajudar a gerir melhor o risco associado à Gestão de Projectos. O sucesso de cada projecto terminado foi qualificado tendo em conta a ponderação de três factores: o desvio ao orçamentado, o desvio ao planeado e o desvio ao especificado. Analisando os projectos decorridos, e correlacionando alguns dos seus atributos com o seu grau de sucesso o modelo classifica, qualitativamente, um novo projecto quanto ao seu risco. Neste contexto o risco representa o grau de afastamento do projecto ao sucesso. Recorrendo a algoritmos de mineração de dados, tais como, árvores de classificação e redes neuronais, descreve-se o desenvolvimento de um modelo que suporta um sistema de apoio à decisão baseado na classificação de novos projectos. Os modelos são o resultado de um extensivo conjunto de testes de validação onde se procuram e refinam os indicadores que melhor caracterizam os atributos de um projecto e que mais influenciam o risco. Como suporte tecnológico para o desenvolvimento e teste foi utilizada a ferramenta Weka 3. Uma boa utilização do modelo proposto possibilitará a criação de planos de contingência mais detalhados e uma gestão mais próxima para projectos que apresentem uma maior propensão para o risco. Assim, o resultado final pretende constituir mais uma ferramenta à disposição do Gestor de Projecto.

A simulation tool for demand response programs implementation

The design and development of simulation models and tools for Demand Response (DR) programs are becoming more and more important for adequately taking the maximum advantages of DR programs use. Moreover, a more active consumers’ participation in DR programs can help improving the system reliability and decrease or defer the required investments. DemSi, a DR simulator, designed and implemented by the authors of this paper, allows studying DR actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. DemSi considers the players involved in DR actions, and the results can be analyzed from each specific player point of view.

Meta-heuristics self-configuration for scheduling

Scheduling resolution requires the intervention of highly skilled human problemsolvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. This paper addresses the resolution of complex scheduling problems using cooperative negotiation. A Multi-Agent Autonomic and Meta-heuristics based framework with self-configuring capabilities is proposed.

Swarm intelligence for scheduling: a review

Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduces some of the theoretical foundations, the biological motivation and fundamental aspects of swarm intelligence based optimization techniques such Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Artificial Bees Colony (ABC) algorithms for scheduling optimization.