Programación de la producción para la empresa Tridimensionales M&S S.A.S

Production scheduling is one of the most important activities in manufacturing companies due to its influence in customer service level. The company TRIDIMENSIONALES M&S SAS (Footwear Soles maker) consists in a Hybrid Flow Shop production environment which presents a high proportion of tardy jobs, being of 31.73%, 38.24%, and 46.43% in 2015, 2016, and 2017 respectively. The main possible causes are: current Line balancing (78.95%), nonexistence of a preventive maintenance program (PM) for machines and molds, and the nonexistence of scheduling methodology. Therefore, this project proposes two solutions simultaneously (one for maintenance and one for scheduling) that are applied in two different scenarios (the actual line balancing and a proposed line balancing). For the PM of both machines and molds, the methodologies Reliability Centered Maintenance and Failure Mode Effects Analysis are proposed. The implementation of proposed PM within 15 days in three different machines resulted in the reduction of the percentage of the fails number, maintenance time, and total costs of corrective maintenance in 61.51%, 62.52%, and 63.56% respectively. For solving the HFS scheduling problem a mathematical model was proposed firstly, and a Tabu Search (TS) metaheuristic was developed to solve real instances, considering the NP-hardness of the problem. The TS was parametrized, and its effectiveness was measured by comparing its results with the mathematical model for small instances of 5 and 10 jobs. In all of them the TS reached the optimal solution. Then, TS was implemented for actual line balancing (78.95%) and proposed line balancing (98.7%) scenarios and the results were compared with the actual manual scheduling procedure, obtaining two main conclusions. Firstly, with overlapping confidence intervals there is not statistically significant difference between percentage of tardy jobs obtained by each one of the Line balancing levels and therefore it would not be justified the hiring of an additional operator at a $20,678,374 annual cost. Secondly, with a significance of 5% and p-value<0.001 there is statistically significant difference between the percentage of tardy jobs between actual manual scheduling in comparison with proposed TS (with actual balancing line), where TS presents a reduction of 42.34%, 62.51%, 52.49%, 71.07%, 67.37%, and 71.93% in percentage of tardy jobs for February, March, June, July, August, and September instances respectively, and a global reduction of 61.34%.Ingeniero (a) IndustrialPregrad

New Coding/Decoding Techniques for Wireless Communication Systems

Wireless communication encompasses cellular telephony systems (mobile communication), wireless sensor networks, satellite communication systems and many other applications. Studies relevant to wireless communication deal with maintaining reliable and efficient exchange of information between the transmitter and receiver over a wireless channel. The most practical approach to facilitate reliable communication is using channel coding. In this dissertation we propose novel coding and decoding approaches for practical wireless systems. These approaches include variable-rate convolutional encoder, modified turbo decoder for local content in Single-Frequency Networks, and blind encoder parameter estimation for turbo codes. On the other hand, energy efficiency is major performance issue in wireless sensor networks. In this dissertation, we propose a novel hexagonal-tessellation based clustering and cluster-head selection scheme to maximize the lifetime of a wireless sensor network. For each proposed approach, the system performance evaluation is also provided. In this dissertation the reliability performance is expressed in terms of bit-error-rate (BER), and the energy efficiency is expressed in terms of network lifetime

Proposta de um algorítmo híbrido baseado em evolução diferencial para os problemas de p-medianas e de máxima cobertura

Orientadora : Profa. dra. Luzia Vidal de SouzaOrientador : Prof. Dr. Luiz Fernando NunesTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 29/08/2014Inclui referênciasÁrea de concentração: Programação MatemáticaResumo: O estudo dos problemas de localização de instalações se relaciona diretamente com problemas organizacionais da sociedade, como por exemplo, a localização de escolas, postos de saúde, etc. Na sua forma geral, os problemas de P-Medianas e Máxima Cobertura são NP-hard e nas suas resoluções são utilizados métodos heurísticos. Os algoritmos de Evolução Diferencial (ED) são poderosos algoritmos de otimização evolucionária, propostos inicialmente, para problemas em espaços contínuos. Recentemente, têm sido propostas adaptações ao seu mecanismo de mutação diferencial para aplicação em problemas combinatórios. Este trabalho apresenta um novo algoritmo híbrido, utilizando algoritmos Evolução Diferencial e Busca Tabu, para a abordagem de problemas de P-Medianas e Máxima Cobertura. Introduz-se no operador de mutação diferencial de um algoritmo de Evolução Diferencial, o algoritmo Busca Tabu, com adaptações, a fim de que o mesmo possa ser aplicado para resolver problemas em um espaço de busca discreto. Testes computacionais foram realizados, com instâncias disponíveis na literatura, e comparados com outras meta-heurísticas e soluções ótimas obtidas com um modelo matemático. Os resultados encontrados sugerem que a técnica proposta é promissora e apropriada para a resolução dos problemas abordados, pois obteve-se na maioria dos testes soluções iguais ou melhores que alguns métodos presentes na literatura em tempos computacionais aceitáveis. Palavras Chave: Otimização Combinatória, Algoritmos Heurísticos, Localização de Instalações.The study of facility location problems is directly related to organizational problems of society, such as the location of schools, health centers , etc. . In its general form, the problem of P-Medians and Maximum Coverage is NP-hard, and heuristic methods are used to solve them. The Differential Evolution (DE) algorithms are powerful evolutionary optimization algorithms, originally proposed for problems in continuous spaces. Recently, it has been proposed adjustments that can be made to the mechanism of differential mutation for its application to combinational problems. This paper presents a new hybrid algorithm, using Differential Evolution Algorithms and Tabu Search, to address problems of P-Medians and Maximum Coverage. Be introduced to the operator of a differential mutation algorithm Differential Evolution, the Tabu Search algorithm with adaptations, so that it can be applied to solve problems in a discrete search space. Computational tests were performed, with instances available in the literature, and compared with other meta-heuristics and optimal solutions obtained from a mathematical model. The results suggest that the proposed technique is promising and appropriate for the resolution of the problems addressed, as was obtained in most testing solutions equal or better than some methods from the literature in acceptable computational time. Keywords : Combinatorial Optimization, Heuristic Algorithms, Location of Facilities

Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network

In Wireless Sensor Network (WSN), high transmission time occurs when search agent focuses on the same sensor nodes, while local optima problem happens when agent gets trapped in a blind alley during searching. Swarm intelligence algorithms have been applied in solving these problems including the Ant Colony System (ACS) which is one of the ant colony optimization variants. However, ACS suffers from local optima and stagnation problems in medium and large sized environments due to an ineffective exploration mechanism. This research proposes a hybridization of Enhanced ACS and Tabu Search (EACS(TS)) algorithm for packet routing in WSN. The EACS(TS) selects sensor nodes with high pheromone values which are calculated based on the residual energy and current pheromone value of each sensor node. Local optima is prevented by marking the node that has no potential neighbour node as a Tabu node and storing it in the Tabu list. Local pheromone update is performed to encourage exploration to other potential sensor nodes while global pheromone update is applied to encourage the exploitation of optimal sensor nodes. Experiments were performed in a simulated WSN environment supported by a Routing Modelling Application Simulation Environment (RMASE) framework to evaluate the performance of EACS(TS). A total of 6 datasets were deployed to evaluate the effectiveness of the proposed algorithm. Results showed that EACS(TS) outperformed in terms of success rate, packet loss, latency, and energy efficiency when compared with single swarm intelligence routing algorithms which are Energy-Efficient Ant-Based Routing (EEABR), BeeSensor and Termite-hill. Better performances were also achieved for success rate, throughput, and latency when compared to other hybrid routing algorithms such as Fish Swarm Ant Colony Optimization (FSACO), Cuckoo Search-based Clustering Algorithm (ICSCA), and BeeSensor-C. The outcome of this research contributes an optimized routing algorithm for WSN. This will lead to a better quality of service and minimum energy utilization