Mejoras de las Heurísticas Greedy empleadas en el secuenciamiento de los Sistemas de Producción Multi-modelo y Multi-nivel

Abstract

The Car Sequencing Problem consists in maintaining a certain order in the vehicles as they pass through the assembly line. Sequences have to be built according to each vehicle's options, each one requiring different resources and production time, with the objective of avoiding to exceed the maximum human and facility potential. In this paper, we use a Greedy heuristic, the Goal-chasing method developed by Toyota, to solve the Car Sequencing Problem. The concept of different weights for each option is introduced to improve the ordering of the sequence. Nelder-Mead method of nonlinear optimization is applied to obtain the weights of options that minimize the cost of the resulting sequence. The results obtained for a initial sequence are expanded to a set of 30 representative sequences of the automotive industry and they are contrasted with some of the classical benchmarks from the literature. Finally, real data is used to validate the proposal.