Enhancing Biotechnological Supply Chain and Facility Planning Using an Improved Ant Colony Algorithm

Abstract

This study explores the application of the ant colony algorithm, a robust path-finding method derived from the foraging behavior of ants, to enhance the efficiency of biotechnological supply chain and facility layouts. The ant colony algorithm simulates how ants leave pheromone trails to signal a path to resources, adapting this concept to optimize logistical pathways and reduce operational inefficiencies in biotechnology settings. The focus is shifted from conventional tourism path planning to sophisticated route optimization in complex biotechnological environments, where precise control over logistics and facility management is crucial. The paper also integrates geographic information systems (GIS) and advanced database technologies to support the spatial and logistical planning required in biotech industries. Techniques such as overlay analysis, buffer analysis, and network analysis are employed to refine the algorithm's applicability to managing vast datasets and intricate network structures typical in biotechnological operations. Additionally, the conceptualization of a Tourism Planning Information System (TPIS) platform is repurposed to illustrate the development of a Biotechnological Planning Information System that aids in visualizing supply routes and optimizing resource distribution based on current economic conditions and facility requirements. Through mathematical modeling and parameter adjustment, this paper demonstrates the potential of the ant colony algorithm to significantly improve the strategic planning of biotechnological facilities and supply chains, enhancing overall operational effectiveness and contributing to more sustainable industry practices.