| dc.description.abstract | This work proposes the development of a metaheuristics based computation tool, to solve the permutation flow shop scheduling problem (PFSSP) in the electronic manufacturing operating in High-mix, Low-volume enviroment. To operate in HMLV enviroment is demanded a large number of setup changes to comply the flexibility required. This elevated number of successive setup changes to produce little batches have negative impacts on the operation costs. One way for to obtain advantages handling a large product mix is to explore the similar features between this products. Through a proper scheduling we can reduce the total downtime to setup changes, and consequently reduces the process time (makespan). The literature brings many success examples in the production scheduling optimization as a way to obtain competitive advantages. But, the complexity and the computational effort demanded to solve this problems, sometimes, turns the practical application unfeasible in the factories routine. In this contexto emerges the metaheuristics as an option to viability this type of application. Among the mataheuristics approaches, outstands the hybrid approaches that combine local search strategies with evolutionary algorithms as a way to obtain good and fast solutions for the scheduling problems, although the optimality is not been guaranted. The tool proposed combine the metaheuristics
Genetic Algorithm and Tabu Search to optimize the flow shop scheduling in the shortest possible time to allow the practical application in industry. The tool was evaluate based on quality metrics like makespan and mean setup time. The Hybrid Algorithm has been evaluated using instances of the literature and instances arising from a real case. The results of the tests indicate a superiority of the hybrid approach over canonical approaches of the Genetic algorithm and Tabu Search. The results obtained in the evaluation of real instances indicate an applicability of the tool in real environments, obtaining good results in the optimization of textit setup times, also for the sequencing of large products. The Hybrid Algorithm has been evaluated using instances of the literature and instances arising from a real case. The tests results indicate a superiority of the hybrid approach over canonical approaches of the Genetic algorithm and Tabu Search. The results obtained in the evaluation of real instances indicate an applicability of the tool in real environments, obtaining good results in the setup time optimization, also for the sequencing of large products. | en |
