Job-Shop scheduling optimization using genetic algorithm
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Inclou dades d'ús des de 2022
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hdl:2117/180650
Realitzat a/ambGuo li Taiwan ke ji da xue
Tipus de documentProjecte Final de Màster Oficial
Data2019-09-06
Condicions d'accésAccés restringit per decisió de l'autor
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Abstract
The objective of this sNational Taiwan Univesity of Sciencde and Technologytudy is to maximize the utilization of a machine, by minimizing the makespan, and minimize the lateness that take place in a job-shop scheduling problem. The problem is characterized, on the one hand, because there is a single machine that requires setup times between the execution of different kind of products. The existence of these setup times considerably increases the production time of the machine, reason for being necessary trying to avoid them by finding a sequence with the lowest setup times as possible. On the other hand, there is a list of orders whose due dates determine the presence of delays in the deliveries. For a problem like this, reaching the optimal solution requires an excessively long process. Therefore, the use of heuristic is needed, which, despite not guaranteeing finding the optimal solution, they are able to find a satisfactory solution in an acceptable period of time. The heuristic method that has been chosen is the Genetic Algorithm. It is an evolutionary programming technique, common in solving optimization problems, whose process is based on the ideas of natural evolution. The algorithm has been coded in the software MATLAB and has been compared with an initial solution created with the Earlies Due Date rule. The computational results obtained show that the proposed algorithm manages to explore the search space efficiently, shortening the resolution times of the problem
MatèriesComputer scheduling, Genetic algorithms, Planificació de tasques (Informàtica), Algorismes genètics
TitulacióMÀSTER UNIVERSITARI EN ENGINYERIA INDUSTRIAL (Pla 2014)
Col·leccions
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master-thesis-david-mateo.pdf | 1,298Mb | Accés restringit |