Simulation based approach for solving Unequal Area Facility Layout Problems in Stochastic condition by Genetic Algorithm

TL;DR

This paper presents a simulation-based genetic algorithm approach to optimize unequal area facility layouts under stochastic conditions, effectively handling dynamic changes and uncertainties in department placements.

Contribution

It introduces a novel integration of slicing tree representation, simulation modeling, and genetic algorithms for dynamic facility layout problems with stochastic flows.

Findings

Successfully solves benchmark cases from literature.

Demonstrates improved layout quality and adaptability.

Shows efficiency of the proposed method in complex scenarios.

Abstract

In this study we introduce a new method to solve the Dynamics Facility Layout Problems (DFLPs). To represent each layout, we use the slicing tree method integrated with our proposed heuristic to obtain promising initial solutions. Then, we consider the case of adding new departments into the current layout with stochastic flows. We use simulation to model the complexity of stochastic nature of the problem. To improve the quality of the initial solution a genetic algorithm is joined with the simulation module. Finally, to demonstrate the performance of our method, we solve several cases existing in the literature to show the efficiency of our algorithm.