Assembly sequence planning using hybrid binary particle swarm optimization

Assembly Sequence Planning (ASP) is known as a large-scale, timeconsuming combinatorial problem. Therefore time is the main factor in production planning. Recently, ASP in production planning had been studied widely especially to minimize the time and consequently reduce the cost. The first objective of this research is to formulate and analyse a mathematical model of the ASP problem. The second objective is to minimize the time of the ASP problem and hence reduce the product cost. A case study of a product consists of 19 components have been used in this research, and the fitness function of the problem had been calculated using Binary Particle Swarm Optimization (BPSO), and hybrid algorithm of BPSO and Differential Evolution (DE). The novel algorithm of BPSODE has been assessed with performance-evaluated criteria (performance measure). The algorithm has been validated using 8 comprehensive benchmark problems from the literature. The results show that the BPSO algorithm has an improved performance and can reduce further the time of assembly of the 19 parts of the ASP compared to the Simulated Annealing and Genetic Algorithm. The novel hybrid BPSODE algorithm shows a superior performance when assessed via performance-evaluated criteria compared to BPSO. The BPSODE algorithm also demonstrated a good generation of the recorded optimal value for the 8 standard benchmark problems

Optimization of robotic assembly sequence

The assembly process is combination of several products into a single product. The assembly process affects manufacturing processes very great extent because it is very time consuming and expensive process. The cost of assembly can reach up to 30% of the manufacturing cost. Instability and direction change in assembly process increases the cost of assembly thus the total cost of product is increased very great extent. The production rate decreases with increase in time in assembly process, so the correct assembly sequence is needed to reduce the time and cost of assembly. For the given product assembly model, the sequences and paths of parts is determined by assembly sequence planning (ASP) to obtain the assembly with minimum costs and shortest time. Industries are taking interest in automated assembly system; robotic assembly system comes under category of this assembly system which uses robots for performing the required assembly tasks. This system is one of the most flexible assembly systems to assemble various parts into desired assembly. Robotic assembly systems can handle a wide range of styles and products, so that same product can be assembled different ways, and to recover from errors. Robotic assembly has the ability to switch to different products and styles because robotic assembly is programmable assembly and it has advantage of greater process capability. Robotic assembly is faster, more efficient and precise than any conventional process. It is very important to determine the feasible,stable and optimal assembly sequence for an assembly system. An assembly sequence plan is a high level plan for constructing a product from its component parts. It specifies which sets of parts form subassemblies, the order in which parts and subassemblies are to be inserted into each subassembly,are to be performed. The aim of the present work is to determine stable, feasible and optimal robotic assembly sequence which follows the assembly constraints and reduces the assembly cost.An important feature of this developing process is epresented by the need to automatically determine the assembly plan by recognizing the optimum sequence iv of operations based upon cost and accuracy. Products with large number of parts have several alternative feasible sequences among which optimal assembly sequence is generated. Traditional methods often generate combinatorial explosions of alternatives, with intolerable computational times. A new methodology has been developed to find out the best robotic assembly sequence among the feasible robotic sequences. The feasible robotic assembly sequences have been generated based on the assembly constraints and later, Artificial Immune System (AIS) and particle swarm optimization with mutation operation has been applied to generate feasible and optimal assembly sequences and result is compared with the previous technique. In AIS Clonal selection and Affinity maturation have been implemented to determine the optimal assembly sequence. During the implementation, each assembly sequence and its energy value have been considered as antibody and the antibody affinity respectively. In PSO, each part of the assembled product is considered as the particle (bird) and mutation operation is performed for selected assembly sequence in each iteration to update the position and velocity of each particle. To generate optimal assembly sequence, a fitness function is generated, which is based on the energy function associated with assembly sequence. The sequence which is having the best fitness value followed by all assembly constraints is treated as the optimal robotic assembly sequence. Present research work has been divided into six chapters. The introduction of the topic and the related matters including the objectives of the work are presented in Chapter 1.The literature reviews on different issues of the topic in Chapter 2. In Chapter 3 Steps of assembly sequence generation,assembly constraints, instability is presented Chapter 4 presents generation of stable assembly sequences using Novel immune approach method and Particle swarm optimization with mutation operation for the generation of robotic assembly sequence. In Chapter 5, Result and discussion obtained from different methods are presented. Finally, Chapter 6 presents the conclusion and future work

Traveling Salesman Problem

The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance

Técnicas de soft-computing para el desarrollo de redes de acceso móvil con control de polución electromagnética

Este trabajo de Tesis Doctoral estudia el problema del despliegue de redes móviles (Mobile Network Deployment Problem o MNDP), orientado a la localización de estaciones base en una red de telecomunicación GSM. Tradicionalmente, este problema de optimización consiste en hallar una solución tal que, con el mínimo coste económico de la red, asegure un grado de servicio mínimo en la zona. Así la función de evaluación maneja dos variables: el coste y el grado de cobertura de la red en el área de estudio. Una de las aportaciones de este trabajo es la incorporación de una nueva variable a dicha función: la radiación electromagnética sobre el terreno en el que opera la red. Existen numerosos estudios que abordan el problema a partir del coste y el grado de servicio, sin embargo no hemos encontrado investigaciones que persigan minimizar la cantidad de radiación emitida por las estaciones base. La sociedad actual mantiene cierta aversión a la radiación que emiten los equipos de telefonía móvil. De este sentimiento surge la idea de incorporar el parámetro de polución electromagnética al problema de optimización MNDP. El problema se aborda mediante métodos metaheurísticos de optimización: un algoritmo evolutivo tradicional, y un novedoso algoritmo recientemente publicado, el Coral Reefs Optimization (CRO). Este último es un algoritmo bio-inspirado que se basa en la simulación de los procesos que de los arrecifes de coral. Los resultados obtenidos de la aplicación de ambas metodologías al problema MNDP han sido comparados con otros tres algoritmos metaheurísticos con la misma función de evaluación. Estos son: el algoritmo Particle Swarm Optimization, el Harmony Search y un algoritmo tipo greedy. Los experimentos realizados sitúan, de manera ampliamente diferenciada, el algoritmo CRO como el más apropiado para resolver el problema MNDP

Técnicas de soft-computing para el desarrollo de redes de acceso móvil con control de polución electromagnética

Este trabajo de Tesis Doctoral estudia el problema del despliegue de redes móviles (Mobile Network Deployment Problem o MNDP), orientado a la localización de estaciones base en una red de telecomunicación GSM. Tradicionalmente, este problema de optimización consiste en hallar una solución tal que, con el mínimo coste económico de la red, asegure un grado de servicio mínimo en la zona. Así la función de evaluación maneja dos variables: el coste y el grado de cobertura de la red en el área de estudio. Una de las aportaciones de este trabajo es la incorporación de una nueva variable a dicha función: la radiación electromagnética sobre el terreno en el que opera la red. Existen numerosos estudios que abordan el problema a partir del coste y el grado de servicio, sin embargo no hemos encontrado investigaciones que persigan minimizar la cantidad de radiación emitida por las estaciones base. La sociedad actual mantiene cierta aversión a la radiación que emiten los equipos de telefonía móvil. De este sentimiento surge la idea de incorporar el parámetro de polución electromagnética al problema de optimización MNDP. El problema se aborda mediante métodos metaheurísticos de optimización: un algoritmo evolutivo tradicional, y un novedoso algoritmo recientemente publicado, el Coral Reefs Optimization (CRO). Este último es un algoritmo bio-inspirado que se basa en la simulación de los procesos que de los arrecifes de coral. Los resultados obtenidos de la aplicación de ambas metodologías al problema MNDP han sido comparados con otros tres algoritmos metaheurísticos con la misma función de evaluación. Estos son: el algoritmo Particle Swarm Optimization, el Harmony Search y un algoritmo tipo greedy. Los experimentos realizados sitúan, de manera ampliamente diferenciada, el algoritmo CRO como el más apropiado para resolver el problema MNDP