Abstract:

This paper presents a teaching experience focused on introducing combinatorial optimization techniques through the optimal plant layout problem. The activity was implemented in an industrial engineering course using a classical facility layout assignment problem, where machines are allocated on a grid to minimize material handling costs based on flow intensity and rectilinear distances.

Instead of relying on simulated annealing or genetic algorithms, the proposed instructional approach adopts Tabu Search due to its memory-based structure and conceptual transparency in explaining search dynamics and mechanisms for escaping local optima. The teaching practice includes the mathematical formulation of the objective function, comparison with exhaustive search results for small instances, and analysis of algorithm performance under different parameter settings.

The learning experience is supported by two-dimensional layout representations, flow matrices, and cost tables, enabling students to visualize the evolution of the objective function and interpret configuration changes at each iteration. Additionally, future developments are discussed, including the potential integration of interactive multimedia tools such as dynamic simulations or 3D visualization environments to enhance spatial understanding.

Results from classroom implementation suggest that incorporating combinatorial metaheuristics within an applied framework improves students’ conceptual understanding of optimization processes and strengthens their analytical skills in production system design.

Keywords:

Facility Layout Problem, Heuristics, Tabu Search.