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

Bryan A. Norman, Alice E. Smith

Research output: Contribution to conferencePaperpeer-review

22 Scopus citations

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 optimizor which allows a user to make an explicit characterization of the cost and uncertainty trade-offs involved in a particular block layout problem.

Original languageEnglish
Pages407-411
Number of pages5
StatePublished - 1997
EventProceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97 - Indianapolis, IN, USA
Duration: Apr 13 1997Apr 16 1997

Conference

ConferenceProceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97
CityIndianapolis, IN, USA
Period04/13/9704/16/97

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