Strategic Site Selection for Biotechnology Driven Commercial Logistics: An Innovative Approach to Optimize Multi-Distribution Centers
Tingni Li
Hainan Vocational University of Science and Technology, Haikou, Hainan, 571126, China
DOI:https://doi.org/10.5912/jcb1554
Abstract:
The process of selecting optimal locations for logistics multi-distribution centers plays a pivotal role in the overall optimization of logistics systems. This paper introduces a novel approach to multi-distribution center site selection, drawing inspiration from the immune response and clone selection mechanisms within biological information systems. We treat the total distance and constraint functions associated with logistics distribution as antigens, while antibodies represent potential solutions to the optimization problem. The iterative acquisition of satisfactory solutions is modeled using a step-by-step clone selection process, generating new antibodies or satisfactory solutions through iteration. Empirical results demonstrate the effectiveness of the immune optimization algorithm-based approach in multi-distribution center site selection, with a remarkable computation time of 47.56 seconds, presenting a 12.5% improvement in speed compared to traditional genetic algorithms. Furthermore, our algorithm exhibits faster convergence and lower time complexity. Unlike genetic algorithms, the immune optimization algorithm demonstrates resilience against local optima, making it a robust choice for solving complex site-selection problems. The biotechnology-based site-selection model introduced here incorporates evolutionary learning mechanisms like those found in biological immune systems, including noise tolerance, teacherless learning, self-organization, and memory. These attributes empower the algorithm to address intricate site-selection challenges effectively. In conclusion, our research presents an innovative and efficient approach to multi-distribution center site selection in logistics, harnessing the power of immune optimization algorithms. This method substantially improves computation time and robustness compared to conventional genetic algorithms. It is a valuable tool for addressing complex optimization problems, particularly in biotechnology-driven commercial logistics.