An Optimized Metaheuristic Swarm Framework for the Multi-Objective Layout Design of Factory Logistics

International Journal of Computer Science and Technology | Vol. 6, No. 10, October 2004 | pp. 73–80

DOI: 10.46882/2004/IJCST/000063

Article Type: Research Article

Title: An Optimized Metaheuristic Swarm Framework for the Multi-Objective Layout Design of Factory Logistics

Names of Authors: Mateo Silva¹, Lucia Rossi²

Authors’ Affiliations: ¹Department of Industrial Engineering, University of Chile, Santiago, Chile; ²Department of Computer Science, University of Bologna, Italy

Abstract: Designing physical spatial layouts for modern production plants is a complex optimization task that directly affects internal material transport overhead, assembly throughput, and overall operational safety. This paper introduces an optimized multi-objective Ant Colony Optimization (ACO) metaheuristic tailored for structural facility layout problems. The AHM algorithm relies on an adaptive pheromone update mechanism that switches exploration patterns depending on layout congestion indexes to prevent local stagnation. We benchmarked our framework using standard industrial layout instances alongside real automated guided vehicle tracking logs. The proposed ACO model secured an overall 14.8% reduction in material handling costs and minimized idle transit delay parameters by 9.2% when compared to standard Tabu Search strategies. Computationally, the routine compiles optimal spatial configurations in under 3.5 s, establishing an agile system for factory floor reconfiguration.

Keywords: Facility Layout Problem, Ant Colony Optimization, Factory Logistics, Metaheuristics, Space Allocation

Manuscript Timeline: Received: July 20, 2004; Revised: August 30, 2004; Accepted: September 24, 2004; Published: October 18, 2004

Citation: Silva, M., & Rossi, L. (2004). An Optimized Metaheuristic Swarm Framework for the Multi-Objective Layout Design of Factory Logistics. International Journal of Computer Science and Technology, 6(10), 73–80.

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