Dynamic unequal area facility layout design under stochastic material flow, re-arrangement cost, and change period

Abstract

In this paper, we present a novel approach to stochastic dynamic unequal area facility layout design while considering uncertainty in material flow, rearrangement costs, and change periods. Our method accounts for dynamic changes by adding new workcenters at stochastic time periods, material flows, and rearrangement costs. By leveraging slicing tree representations, a hybrid genetic algorithm, and a simulation-based evaluation framework, our approach effectively balances material handling efficiency and rearrangement costs, ensuring resilient layouts under stochastic scenarios. Our approach explicitly incorporates probabilistic modeling of material flows, dynamic cost functions, and stochastic event-driven layout adjustments in a unified framework. Computational experiments validate the effectiveness of our method, demonstrating significant improvements in cost optimization and adaptability. Our study reveals that deterministic solutions may not remain optimal under stochastic conditions, emphasizing the need for a range of material flow scenarios in the deterministic model. The results highlight the practical implications of our approach, enabling industries to reduce disruptions and operational costs while maintaining efficiency.