Kubota, n. 1994: genetic algorithm with age structure and its application to self-organising...

1994 IEEE Symposium on Emerging Technologies & Factory Automation Genetic Algorithm with Age Structure and Its Application to Self-Organizing Manufacturing System Naoyuki KUBOTA, Toshio FUKUDA, Fumihito ARAI, and Koji SHIMOJIMA Dept. of Micro System Engineering, Nagoya University, I Furo-cho, Chikusa-ku, Nagoya 464-01, JAPAN Abstract - This paper deals with the new genetic algorithm with the age structure. The genetic algorithm has been recently demonstrated its effectiveness in optimization issues, but the genetic algorithm has two major problems: a premature local convergence ard a bias by the genetic drift In order to solve these problems, we propose the genetic algorithm introducing the age structure which is a continuous generation model. The genetic algorithm with age structure is applied to the self- organizing manufacturing system, that is, a process self- organizes to the other process in the flexible manufacturing system envi ronment. The effectiveness of the genetic algorithm with the age structure is demonstrated through numeri cal simulations of the reorganizat ion of the press machining line as an example of the self-organizing manufacturing system. INTRODUCTION In recent years, the intelligent manufacturing system, which has been discussed by many researchers, is required alvanced fl exible processes in a flexible manufacturing system (FMS) environment[l,2]. We have been proposed self- organizing cellular robotic sys tem (CEBOT) which is composed of a number of autonomous robotic units with simple functions[3]- [ 6J. The form of the CEBOT is reconfigured dynamically in order to suit to the environment and tasks. Further, as a manufacturing system held the idea of the CEBOT, we propose the self-organizing manufacturing system (SOMS), that is, each process self-organizes effectively to the other process in the FMS. The optimization of each process in the FMS is ne:eCed in <rder to crerue an ideal manufacturing system environment, ard the FMS environment includes many optimization problems which are ill-defined. There are stochastic search methods for these problems such as the si mulated annealing, the genetic algorithm(GA}, ard so on . The GA[ 7]- [9], which simulates the process of natural evolution, is defired as an optimization method with the set, called a population of individuals. Each individual has a fitness value corresponding with its genotype, ard the next generation of a population is reproduced by selection according to a fitness value of each individual. The simple GA(SGA)[2] as one of the GAs, in general, composes of genetic operators: roulette strategy selection, one-point crossover ard mutat ion. The GA has been demonsrrated its effectiveness in combinatorial optimization issues, scheduling problems ard so on[JOH13]. However, the GA has two major problems of premature local convergence ard the bias by the genetic drift. These problems occurred when genetic diversity in the populat ion is reduced. Then ways to so lve these problems have been proposed following : a fitness scaling, a dynamically conrrol of a mutat ion rate, a parallelization by subpopulations, and so on . Then, in tl'der to solve these problems, we propose the GA introducing the age structure (ASGA) which is a continuous generation model. In the ASGA, a coexistence of parents and offspring in a population is pennitted . As optimization problems to compare in the performances among the GAs. most of the GAs are applied fer a scheduling problem or a traveling salesman problem . The job shop scheduling problem (JSSP) is in general refined as an optimization problem to search for optimal scheduling of required works in orckr to shorten makespan performed in the environment composed of several machines[!]. However, in th is JSSP there are many restrictions about transportation methods ard others. lt is required to consider these restrictions in ceder to apply for the FMS. In su ch a F\1S environment , however, there are lots of flow shop scheduling problem which are defined as a sequencing. The sequence of machining line is effective 'n respect to paths of automated guided vehicles(AGVs) or placemems of belt conveyers, f or the di.rection of AGYs are the same. There are many cases that the sequencing of works is optimized according to the several available machines in the FMS environment. In contrast to those cases, there are few cases that the form of machines is optimized according to the sequeoce of given works. Consequently, "e deal with an application for the latter cases. In this popcr, the ASGA is applied to a press machining line (PML) wh ich is capable of Q-7603-2114-61941$4.00 © 1994 IEEE. 472

Kubota, n. 1994: genetic algorithm with age structure and its application to self-organising...

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