Abstract
An improved genetic algorithm for the dynamic layout problem is developed and tested in this research. Our genetic algorithm differs from the existing implementation in three ways: first, we adopt a different crossover operator, second, we use mutation, and third, we use a new generational replacement strategy to help increase population diversity. A computational study shows that the proposed GA is quite effective. Scope and purpose Most organizations today are operating in a dynamic and market-driven environment. To stay competitive, their facilities must be adaptive to market fluctuations. The dynamic layout problem devises a multi-period layout plan based on these demand fluctuations. In this paper, we propose an improved implementation of an existing genetic algorithm for solving the dynamic layout problem.
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