Development of an Adaptive Genetic Algorithm to Optimize the Problem Of Unequal Facility Location

Abstract

The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the genetic algorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional genetic algorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.

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“…Figures 3 shows that the levosimendan group showed a significantly (p < 0.05) lower MDA level than the CLP group. In earlier studies, Levosimendan can stop cardiomyocytes losing their mitochondrial membrane potential and from opening their transition pore [26]. It may alter ROS release in this manner, and levosimendan's maintenance of mitochondrial function may serve as the starting point for reducing ROS generation and restoration heart energy and cell survival [27].…”

Preface to the Special Issue on Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics

“…1. An Adaptive Genetic Algorithm (AGA) was developed by Budi et al [1] for the unequal facility location problem.…”