Construcción de un sistema de información y de ayuda a la decisión mediante lógica difusa para el cultivo del olivar en Andalucía

In Southern Spain, olive (Olea europaea L.) growing is an important part of the economy, especially in the provinces of Jaén, Córdoba and Granada. This work proposes the first stages of an Information and Decision-Support System (IDSS) for providing different types of users (farmers, agricultural engineers, public services, etc.) with information on olive growing and the environment, and also assisting in decision-making. The main purposes of the project reported in this paper are to process uncertain or imprecise data, such as those concerning the environment or crops, and combine user data with other scientific-experimental data. The possibility of storing agricultural and ecological information in fuzzy relational databases, vital to the development of an IDSS is described. The information will be processed using knowledge extraction tools (fuzzy data-mining) that will allow rules on expert knowledge for assessing suitability of land to be developed and making thematic maps with the aid of Geographic Information Systems. Flexible querying will allow the users to collect information interactively from databases, while user information is constantly added. Flexible querying of databases, land suitability and thematic maps may be used to help in decisionmaking.El cultivo del olivo (Olea europaea L.) tiene una enorme importancia económica en la zona sur de España y concretamente en las provincias de Jaén, Córdoba y Granada. En este trabajo se propone la construcción de un sistema de información y ayuda a la toma de decisión (IDSS) que permita en el futuro a distintos tipos de usuarios (agricultores, agrónomos, administraciones públicas, etc.) obtener y manejar información sobre el cultivo de olivar y el soporte ambiental del mismo, así como ayudar en la toma de decisiones. Los principales objetivos desarrollados en este trabajo son el tratamiento de datos inciertos e imprecisos, como es el caso de la información ambiental y sobre cultivos, y la fusión de datos sobre cultivo y otros de carácter científico-experimental. Se describe la posibilidad de almacenar la información de carácter agronómico y ecológico en bases de datos relacionales, que es vital para el desarrollo de un IDSS. La información será procesada a través de herramientas de extracción de conocimiento (minería de datos difusa) y permitirá sobre la base del conocimiento experto el desarrollo de reglas para la clasificación de aptitud del terreno y para la obtención de mapas temáticos con la ayuda de Sistemas de Información Geográfica. La consulta flexible permitirá a los distintos usuarios la consulta interactiva de toda la información almacenada en las bases de datos, así como una implementación constante de las mismas. La consulta flexible de bases de datos, la idoneidad de los terrenos y los mapas temáticos pueden ser de gran utilidad en la toma de decisiones.This work is part of the research projects 1FD97-0244-CO3-2 (financed with FEDER funds) and CGL2004-02282BTE (Spanish Ministry of Education and Science)

REDES NEURAIS PARA PREVISÃO DA PRODUÇÃO INDUSTRIAL DE DIFERENTES SEGMENTOS

Este trabalho tem como objetivo propor um modelo de rede neural para previsão de séries de produção de onze segmentos industriais brasileiros. Primeiramente, estudou-se diferentes tipos de redes que vêm sendo implementadas na literatura nos últimos anos, como Perceptron, Redes Lineares, Perceptron Multi-Camadas, Redes BAM e ART, Rede Probabilística, Hopfield, Kohonen, TDNN (Time delay neural network), Rede de Elman e Jordan, além dos algoritmos Backpropagation e Levenberg-Marquadt. Estudando o comportamento dessas séries de produção e as principais características de cada tipo de rede, concluímos que a rede Perceptron Multi-Camadas com atraso no tempo (TDNN) é a melhor para o cálculo e análise da previsão da produção dos onze segmentos escolhidos do setor industrial. A rede neural foi então aplicada considerando duas diferentes estratégias de modelo estrutural. Concluímos que o modelo de rede neural proposto foi eficaz na previsão de séries de produção de segmentos industriais

Forecasting model selection through out-of-sample rolling horizon weighted errors

Demand forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In this paper, a new automatic selection procedure of time series forecasting models is proposed. The selection criterion has been tested using the set of monthly time series of the M3 Competition and two basic forecasting models obtaining interesting results. This selection criterion has been implemented in a forecasting expert system and applied to a real case, a firm that produces steel products for construction, which automatically performs monthly forecasts on tens of thousands of time series. As result, the firm has increased the level of success in its demand forecasts. © 2011 Elsevier Ltd. All rights reserved.Poler Escoto, R.; Mula, J. (2011). Forecasting model selection through out-of-sample rolling horizon weighted errors. Expert Systems with Applications. 38(12):14778-14785. doi:10.1016/j.eswa.2011.05.072S1477814785381

The Research for Demand Response Function based on POS data

We knew many approaches to maximize the sales in the prior books and research. But realistic research to analyze retail market systematically is rare. Because of difficulty of obtaining sales data and figure the elements realistic. So this research aims to identify different factors in the Grocery retail market that influences manufacturer. Grocery POS data was collected and then we developed an effect assessment model to analyze demand response in the different channels. Further more, we propose the demand response function and alternative management method for different goods including the revealed factors to improve efficiency in real retail market.제 1 장 서론 1 1.1 연구의 배경 2 1.2 연구의 목표 5 1.3 연구의 구성 9 1.4 연구의 흐름도 10 제 2 장 선행 연구 고찰 11 2.1 기존 문헌 고찰 11 2.2 선행 연구 고찰 11 2.3 소매업의 구조 및 현황 16 제 3 장 본론 23 3.1 데이터 23 3.2 연구 모델 정립 25 3.3 분석 결과 31 제 4 장 결론 36 4.1 결론 36 4.2 연구의 한계와 향후 방향 39 참고문헌 4

Using neural networks for sales forecast in retail industry

Neste trabalho, explora-se o uso de redes neurais na previsão de vendas no varejo. Com essa técnica foram elaboradas previsões a partir de dados históricos de vendas de produtos de uma empresa do varejo. Foram gerados modelos com o uso de dados de venda de cada um dos produtos da base de dados, semana a semana. Os resultados sugerem que a modelagem por meio de redes neurais artificiais pode ser considerada adequada para a previsão de demanda de produtos no nível individual (produto a produto). Os modelos obtidos com o uso da metodologia proposta podem prever as vendas de produtos no curto prazo com maior precisão do que as técnicas naïve não-ajustada e de regressão linear, mais freqüentemente utilizadas.En este trabajo, se investiga el uso de redes neurales en la previsión de ventas al por menor. Con esa técnica se elaboraron previsiones a partir de datos históricos de ventas de productos de una empresa minorista. Se generaron modelos con el uso de datos de venta de cada uno de los productos de la base de datos, semana a semana. Los resultados sugieren que el modelado por medio de redes neurales artificiales puede ser considerado adecuado para la previsión de demanda de productos considerados individualmente (producto por producto). Los modelos obtenidos con el uso de la metodología propuesta pueden prever las ventas de productos a corto plazo con mayor precisión que las técnicas naïve no ajustada y de regresión lineal, más frecuentemente utilizadas.This paper explores the use of artificial neural networks in sales forecasts in retailing industry. Historical data is used to make forecasts of product sales in the retailing industry. Weekly sales data from individual products were used to generate models. Results sugest that models based upon neural networks can be adequate to individual product sales forecast. Models obtained with the proposed methodology are able to predict product sales in the short term more accurately then non adjusted naïve techniques and linear regression, more frequently used

Forecasting seat sales in passenger airlines: introducing the round-trip model

This thesis aims to improve sales forecasting in the context of passenger airlines. We study two important issues that could potentially improve forecasting accuracy: day-to-day price change rather than price itself, and linking flights that are likely to be considered as pairs for a round trip by passengers; we refer to the latter as the Round-Trip Model (RTM). We find that price change is a significant variable regardless of days remaining to flight in the last three weeks to flight departure, which opens the possibility of planning for revenue maximizing price change patterns. We also find that the RTM can improve the precision of the forecasting models, and provide an improved pricing strategy for planners. In the study of the effect of price change on sales, analysis of variance is applied; finite regression mixture models were tested to identify linked traffic in the two directions and the linked flights on a route in reverse directions; adaptive neuro-fuzzy inference system (ANFIS) is applied to develop comparative models for studying sales effect between price and price change, and one-way versus round-trip models. The price change model demonstrated more robust results with comparable estimation errors, and the concept model for the round-trip with only one linked flight reduced estimation error by 5%. This empirical study is performed on a database with 22,900 flights which was obtained from a major North American passenger airline

A web-based collaborative decision making system for construction project teams using fuzzy logic

In the construction industry, the adoption of concurrent engineering principles requires the development of effective enabling IT tools. Such tools need to address specific areas of need in the implementation of concurrent engineering in construction. Collaborative decision-making is an important area in this regard. A review of existing works has shown that none of the existing approaches to collaborative decision-making adequately addresses the needs of distributed construction project teams. The review also reveals that fuzzy logic offers great potential for application to collaborative decision-making. This thesis describes a Web-based collaborative decision-making system for construction project teams using fuzzy logic. Fuzzy logic is applied to tackle uncertainties and imprecision during the decision-making process. The prototype system is designed as Web-based to cope with the difficulty in the case where project team members are geographically distributed and physical meetings are inconvenient/or expensive. The prototype was developed into a Web-based software using Java and allows a virtual meeting to be held within a construction project team via a client-server system. The prototype system also supports objectivity in group decision-making and the approach encapsulated in the prototype system can be used for generic decision-making scenarios. The system implementation revealed that collaborative decision-making within a virtual construction project team can be significantly enhanced by the use of a fuzzybased approach. A generic scenario and a construction scenario were used to evaluate the system and the evaluation confirmed that the system does proffer many benefits in facilitating collaborative decision-making in construction. It is concluded that the prototype decision-making system represents a unique and innovative approach to collaborative decision-making in construction project teams. It not only contributes to the implementation of concurrent engineering in construction, but also it represents a substantial advance over existing approaches

An analysis of neural networks and time series techniques for demand forecasting

This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method

An analysis of neural networks and time series techniques for demand forecasting

This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method