An Adaptive Genetic Algorithm based approach for production reactive scheduling of manufacturing systems

Abstract

The problem for scheduling the manufacturing systems production involves the system modeling task and the application of a technique to solve it. There are several ways used to model the scheduling problem and search strategies have been applied on the models to find a solution. The solutions consider performance parameters like makespan. However, depending on the size and complexity of the system, the response time becomes critical, mostly when it's necessary to reschedule. Researches aim to use Genetic Algorithms as a search method to solve the scheduling problem. This paper proposes the use of Adaptive Genetic Algorithm (AGA) to solve this problem having as performance criteria the minimum makespan and the response time. The probability of crossover and mutation is dynamically adjusted according to the individual's fitness value. The proposed approach is compared with a traditional Genetic Algorithm (GA).

An Adaptive Genetic Algorithm for Production Scheduling on Manufacturing Systems with Simultaneous Use of Machines and AGVs

“…In this paper, is proposed a modified adaptive genetic algorithm (MAGA) based on approach presented in Morandin et al (2008), where the crossover and mutation rates are dynamically adjusted to improve the performance of the algorithm in the search space.…”

“…In Morandin et al (2008) was investigated the use of an adaptive genetic algorithm for production reactive scheduling of manufacturing systems having as performance criteria the minimum makespan and the response-obtaining time. In Morandin et al (2008), the operator probabilities are dynamically set according to the fitness value of the chromosomes.…”

“…Because of this, this paper used adaptive genetic algorithms with dynamic adjustment of crossover and mutation rates. The proposed approach is based on the work proposed by Morandin et al (2008). The steps of adaptive genetic algorithm used and the possible adjustments of genetic operators' rates are presented below:…”

“…In Morandin et al (2008), the operator probabilities are dynamically set according to the fitness value of the chromosomes.…”

“…However, it is reported that the simple GA often suffers from the troubles of premature convergence, difficulty in constructing fitness functions and parameter dependence Yin et al (2004). So far, many researches have used an adaptive genetic algorithm to solve the problem of premature convergence (Morandin et al 2008;Jerald et al 2006;Zhang et al 2006;Yin et al 2004).…”