Optimizing Labor Allocation in Modular Construction Factory Using Discrete Event Simulation and Genetic Algorithm

Abstract

Modular construction is gaining popularity in the USA due to several advantages over stick-built methods in terms of reduced waste and faster production. Since modular construction factories operate as an assembly line, the number of workers at various workstations dictates the efficiency of the overall production. This paper presents a resource allocation framework combining discrete event simulation (DES) model and genetic algorithm (GA) to facilitate data-driven decision making. The DES model simulates the process of building modular units in the factory, and the GA optimizes the number of the worker at different workstations yielding to minimum makespan. A case study with a real-world modular construction factory showed that optimizing the assignment of available workers can reduce the makespan by up to 15%. This study demonstrates the potential of the proposed method as a practical tool to optimize resource allocation in the uncertain work environments in modular construction factories.