Random keys genetic algorithm with adaptive penalty function for optimization of constrained facility layout problems

Abstract

This paper presents an extended formulation of the unequal area facilities block layout problem which explicitly considers uncertainty in material handling costs by use of expected value and standard deviations of product forecasts. This formulation is solved using a random keys genetic algorithm (RKGA) to circumvent the need for repair operators after crossover and mutation. Because this problem can be highly constrained depending on the maximum allowable aspect ratios of the facility departments, an adaptive penalty function is used to guide the search to feasible, but not suboptimal, regions. The RKGA is shown to be a robust optimizer which allows a user to make an explicit characterization of the cost and uncertainty trade-offs involved in a particular block layout problem.