A HYBRID EVOLUTIONARY ALGORITHM BASED ON GENETIC ALGORITHM AND SIMULATED ANNEALING FOR FACILITY LAYOUT

摘要

The Simple Genetic Algorithm (SGA) has intrinsic drawbacks that much time is wasted on coding and decoding when using this algorithm. Additionally, SGA lacks the hill-climbing ability to back out when the calculation falls into a regional pit such that it is only capable of searching for regional extreme values. The objective of this research is to propose a hybrid algorithm by combining the genetic algorithm and the simulated annealing to overcome the problems encountered by using this simple genetic algorithm. The new algorithm is complemented with Space Filling Curve (SFC) to find the optimum solution for discrete facility layouts. It also considers the calculated minimum Total Layout Cost (TLC) for layouts of various departments with unequal areas. TLC is the target function of multiple factors; it considers Shape Ratio Factor (SRF) and Area Utilization Factor (AUF) in addition to the factor cost of material flow. The literature published previously on discrete layouts seldom covers the problems on the irregular shape of various departments and the area utilization. Hence, this research targets problems of discrete layouts with unequal areas to overcome problems on the order of laying out departments, balancing the geometric shape of department and utilizing area. Additionally, the study uses the space filling curve method to avoid partitioning discrete departments thus effectively dealing with the problems that have not been solved in literature.