Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Otimização Adaptativa Baseada em Simulação para a programação da produção em sistemas flow shop: um estudo comparativo

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de ProduçãoO planejamento e controle da produção é uma atividade essencial para todos os sistemas produtivos, uma vez que seu desempenho afeta diretamente critérios importantes como custo, qualidade, tempo e disponibilidade, fatores fundamentais para a competitividade empresarial. O problema de programação do flow shop é um dos problemas mais comuns envolvendo o planejamento de sistemas produtivos, sendo altamente aplicado no mundo real. A solução para este problema não pode ser encontrada facilmente devido à sua complexidade, o que impossibilita a utilização de métodos que retornem resultados ótimos a um baixo tempo de execução computacional. Assim, a Otimização Adaptativa Baseada em Simulação é uma ferramenta com potencial aplicação pois considera a estocasticidade do problema, buscando soluções que estejam alinhadas com o cenário real por meio de uma simulação e otimizando as variáveis desejadas. O objetivo geral do presente trabalho é avaliar comparativamente diferentes modelos de otimização adaptativa baseada em simulação para sistemas de produção flow shop. Foram considerados dois métodos para comparação, ambos aplicando um algoritmo genético integrado com uma simulação de eventos discretos. O primeiro método busca encontrar a melhor regra de sequenciamento para cada máquina do sistema produtivo, enquanto o segundo busca encontrar diretamente o melhor sequenciamento de ordens de produção sem uma regra pré-determinada. Os resultados mostram que um sequenciamento direto de ordens de produção retorna resultados melhores que as regras de despacho, tanto o makespan quanto o tempo de execução.Production planning and control is an essential activity for all production systems, since its performance directly affects important criteria such as cost, quality, time and availability, factors that are fundamental for business competitiveness. The flow shop scheduling problem is one of the most common problems involving the planning of production systems, being highly applied in the real world. The solution to this problem cannot be easily found due to its complexity, which makes it impossible to use methods that return optimal results at a low computational execution time. Thus, Adaptive Simulation-Based Optimization is a tool with potential application as it considers the stochasticity of the problem, seeking solutions that are aligned with the real scenario through a simulation and optimizing the desired variables. The general objective of the present work is to comparatively evaluate different adaptive simulation-based optimization models for flow shop production systems. Two methods for comparison were considered, which apply a genetic algorithm integrated with a simulation of discrete events. The first method seeks to find the best dispatching rule for each machine in the production system, while the second seeks to directly find the best sequencing of production orders without a pre-determined rule. The results show that the rule-free sequencing of production orders returns better results than dispatch rules, both makespan and execution tim

Flow Shop Scheduling for Energy Efficient Manufacturing

A large number of new peaking power plants with their associated auxiliary equipment are installed to meet the growing peak demand every year. However, 10% utility capacity is used for only 1%~2% of the hours in a year. Thus, to meet the demand and supply balance through increasing the infrastructure investments only on the supply side is not economical. Alternatively, demand-side management might cut the cost of maintaining this balance via offering consumers incentives to manage their consumption in response to the price signals. Time-varying electricity rate is a demand-side management scheme. Under the time-varying electricity rate, the electricity price is high during the peak demand periods, while it is low during the off-peak times. Thus, consumers might get the cost benefits through shifting power usages from the high price periods to the low price periods, which leading to reduce the peak power of the grid. The current research works on the price-based demand-side management are primarily focusing on residential and commercial users through optimizing the “shiftable” appliance schedules. A few research works have been done focusing manufacturing facilities. However, residential, commercial and industrial sectors each occupies about one-third of the total electricity consumption. Thus, this thesis investigates the flow shop scheduling problems that reduce electricity costs under time-varying electricity rate. A time-indexed integer programming is proposed to identify the manufacturing schedules that minimize the electricity cost for a single factory with flow shops under time-of-use (TOU) rate. The result shows that a 6.9% of electricity cost reduction can be reached by shifting power usage from on-peak period to other periods. However, in the case when a group of factories served by one utility, each factory shifting power usage from on-peak period to off-peak hours independently, which might change the time of peak demand periods. Thus, a TOU pricing combined with inclining block rate (IBR) is proposed to avoid this issue. Two optimization problems are studied to demonstrate this approach. Each factory optimizes manufacturing schedule to minimize its electricity cost: (1) under TOU pricing, and (2) under TOU-IBR pricing. The results show that the electricity cost of each factory is minimized, but the total electricity cost at the 2nd hour is 6.25% beyond the threshold under TOU pricing. It also shows that factories collaborate with each other to minimize the electricity cost, and meanwhile, the power demand at each hour is not larger than the thresholds under TOU-IBR pricing. In contrast to TOU rate, the electricity price cannot be determined in ahead under real-time price (RTP), since it is dependent on the total energy consumption of the grid. Thus, the interactions between electricity market and the manufacturing schedules bring additional challenges. To address this issue, the time-indexed integer programming is developed to identify the manufacturing schedule that has the minimal electricity cost of a factory under the RTP. This approach is demonstrated using a manufacturing facility with flow shops operating during different time periods in a microgrid which also served residential and commercial buildings. The results show that electricity cost reduction can be achieved by 6.3%, 10.8%, and 24.8% for these three time periods, respectively. The total cost saving of manufacturing facility is 15.1% over this 24-hour period. The results also show that although residential and commercial users are under “business-as-usual” situation, their electricity costs can also be changed due to the power demand changing in the manufacturing facilities. Furthermore, multi-manufacturing factories served by one utility are investigated. The manufacturing schedules of a group of manufacturing facilities with flow shops subject to the RTP are optimized to minimize their electricity cost. This problem can be formulated as a centralized optimization problem. Alternatively, this optimization problem can be decomposed into several pieces. A heuristic approach is proposed to optimize the sub-optimization problems in parallel. The result shows that both the individual and total electricity cost of factories are minimized and meanwhile the computation time is reduced compared with the centralized algorithm

Desarrollo de una aplicación computacional bajo algoritmos genéticos para la secuenciación de trabajos/Órdenes de celda de manufactura HAS 200 de la Universidad Libre Seccional Bogotá.

El proyecto tiene como fin fomentar la investigación en secuenciación y programación de la producción en la Universidad Libre Seccional Bogotá, mejorando el uso del laboratorio de la Celda de Manufactura HAS200, para esto se desarrolló una aplicación computacional que facilita la apreciación de resultados y la toma de decisiones en la diferentes situaciones que se presentan a lo largo del proceso, es así como se decide realizar la investigación de algoritmos genéticos para implementarlos en el desarrollo del aplicativo, teniendo en cuenta que la enseñanza en cuanto a secuenciación solamente abarca las reglas básicas hasta dos máquinas, consecuentemente se realizaron pruebas del sistema actual que tiene la celda, como la toma de tiempos de los diferentes productos, identificando así las falencias que este tiene, finalmente se logra realizar el aplicativo Sekvens que muestra detalladamente el procedimiento de secuenciación de órdenes, identificando tiempos totales y la mejor secuencia encontrada.The project aims to increase research in sequencing and production planning at the Universidad Libre Seccional Bogota, improving the use of laboratory HAS200 Manufacturing Cell, for this we propose a computational application that facilitates the assessment of results and taking decisions in the different situations that arise throughout the process, and decided to perform research of genetic algorithms to implement in developing the application, taking into account that teaching about sequencing only covers the basic rules to two machines were tested consequently the current system that has the cell, such as taking time for different products, thus identifying the weaknesses it has, finally manages to make the application Sekvens showing in detail the process of sequencing orders identifying total times and the best sequence found, so students and teachers will find a learning method that provides added value to the teaching of future industrial engineers

A hybrid genetic algorithm for route optimization in the bale collecting problem

The bale collecting problem (BCP) appears after harvest operations in grain and other crops. Its solution defines the sequence of collecting bales which lie scattered over the field. Current technology on navigation-aid systems or auto-steering for agricultural vehicles and machines, is able to provide accurate data to make a reliable bale collecting planning. This paper presents a hybrid genetic algorithm (HGA) approach to address the BCP pursuing resource optimization such as minimizing non-productive time, fuel consumption, or distance travelled. The algorithmic route generation provides the basis for a navigation tool dedicated to loaders and bale wagons. The approach is experimentally tested on a set of instances similar to those found in real situations. In particular, comparative results show an average improving of a 16% from those obtained by previous heuristics.This work was supported in part by the Spanish Government (research project AGL2010-15334).Gracia Calandin, CP.; Diezma Iglesias, B.; Barreiro Elorza, P. (2013). A hybrid genetic algorithm for route optimization in the bale collecting problem. Spanish Journal of Agricultural Research. 11(3):603-614. https://doi.org/10.5424/sjar/2013113-3635S603614113Amiama, C., Bueno, J., Álvarez, C. J., & Pereira, J. M. (2008). Design and field test of an automatic data acquisition system in a self-propelled forage harvester. Computers and Electronics in Agriculture, 61(2), 192-200. doi:10.1016/j.compag.2007.11.006Baker, B. M., & Ayechew, M. A. (2003). A genetic algorithm for the vehicle routing problem. Computers & Operations Research, 30(5), 787-800. doi:10.1016/s0305-0548(02)00051-5Baykasolu, A., Oumlzbakr, L., & Tapk, P. (2007). 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Makespan Minimization in Re-entrant Permutation Flow Shops

Re-entrant permutation flow shop problems occur in practical applications such as wafer manufacturing, paint shops, mold and die processes and textile industry. A re-entrant material flow means that the production jobs need to visit at least one working station multiple times. A comprehensive review gives an overview of the literature on re-entrant scheduling. The influence of missing operations received just little attention so far and splitting the jobs into sublots was not examined in re-entrant permutation flow shops before. The computational complexity of makespan minimization in re-entrant permutation flow shop problems requires heuristic solution approaches for large problem sizes. The problem provides promising structural properties for the application of a variable neighborhood search because of the repeated processing of jobs on several machines. Furthermore the different characteristics of lot streaming and their impact on the makespan of a schedule are examined in this thesis and the heuristic solution methods are adjusted to manage the problem’s extension

Sessenta anos de Shop Scheduling : uma revisão sistemática da literatura

Orientador : Prof. Dr. Cassius Tadeu ScarpinDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa: Curitiba, 09/02/2017Inclui referências : f. 449-492Resumo: Desde o seminal artigo de Johnson em 1954, a Programação da Produção em Shop Scheduling tem se tornado uma área relevante dentro da Pesquisa Operacional e, atualmente, duzentos trabalhos tangentes à temática são publicados anualmente. Dentre os artigos aqui citados tem-se aqueles que se dedicam à apresentação e síntese do estado da arte desse assunto, intitulados artigos de revisão. Quando tais artigos são elaborados a partir de um conjunto objetivo de critérios, relativos à categorização dos artigos selecionados, tem-se a Revisão Sistemática da Literatura (RSL). O presente trabalho realiza uma RSL em Shop Scheduling, a partir da análise de cada ambiente fabril que o compõe. Fez-se o escrutínio de 560 artigos, à luz de um conjunto de métricas, que constitui a estrutura basilar da proposta de nova taxonomia do Shop Scheduling, complementar à notação de Graham, objetivo fulcral do presente trabalho. Além disso, utilizou-se uma representação em redes dos resultados obtidos em algumas das métricas empregadas, como a característica dos itens, algo outrora inaudito em estudos de revisão desse assunto. Ademais, outro ponto relevante desse estudo repousa na identificação de campos pouco explorados, de modo a colaborar com a pesquisa futura neste tomo. Palavras-chave: Shop Scheduling. Revisão Sistemática da Literatura. Taxonomia. Representação em Redes.Abstract: Since Johnson's seminal article in 1954, Shop Scheduling in Production Scheduling has become a relevant area within Operational Research, and currently hundreds of tangential works on the subject are published annually. Among the articles cited here are those dedicated to the presentation and synthesis of the state of the art of this subject, which are entitled review articles. When these articles are elaborated from an objective set of criteria, regarding the categorization of the selected articles, we have the Systematic Review of Literature (SLR). The present work performs a SLR in Shop Scheduling, based on the analysis of each manufacturing environment that composes it. There were 560 articles scrutinized based on a set of metrics, which is the basic structure of the proposed new Taxonomy of Shop Scheduling, complementary to Graham's notation, the main objective of this work. In addition to that a network representation of the results was obtained in some of the metrics used, such as the job characteristics, something previously unheard of in review studies of this subject. Moreover, another relevant point of this study lies in the identification of less explored fields in order to collaborate with future research in this matter. Keywords: Shop Scheduling. Systematic Literature Review. Taxonomy. Network Representation