The manufacturing processes of a chip resistor are very similar to a flowshop scheduling problem only with minor details which can be modeled using some extra constraints; while permutation flowshop scheduling problems (PFSPs) have attracted much attention in the research works. Many approaches like genetic algorithms were dedicated to solve PFSPs effectively and efficiently. In this paper, a novel approach is presented by embedding artificial chromosomes into the genetic algorithm to further improve the solution quality and to accelerate the convergence rate. The artificial chromosome generation mechanism first analyzes the job and position association existed in previous chromosomes and records the information in an association matrix. An association matrix is generated according to the job and position distribution from top 50% chromosomes. Artificial chromosomes are determined by performing a roulette wheel selection according to the marginal probability distribution of each position. Two types of PFSPs are considered for evaluation. One is a three-machine flowshop in the printing operation of a real-world chip resistor factory and the other is the standard benchmark problems retrieved from OR-Library. The result indicates that the proposed method is able to improve the solution quality significantly and accelerate the convergence process.
Relation:
Expert Systems with Applications 36(3), p.7135-7141
