Optimization of Logistics Distribution Centers Based on Economic Efficiency and Sustainability: Data Support from the Hohhot–Baotou–Ordos–Ulanqab Urban Agglomeration

Abstract

This study proposes a nonlinear 0-1 mixed-integer programming model for optimizing the location of logistics distribution centers within the Hohhot–Baotou–Ordos–Ulanqab urban agglomeration, integrating transportation costs, carbon emissions, and operational coefficients. The optimization problem is solved using a genetic algorithm (GA), whose robustness is systematically validated through comparative analyses with linear programming (LP) and alternative heuristic optimization methods including simulated annealing (SA) and particle swarm optimization (PSO). Comprehensive sensitivity analyses are conducted on critical parameters—including transportation costs, demand fluctuations, carbon pricing mechanisms, the logistics center capacity, land use impact, and water resource constraints—to evaluate the model’s adaptability under diverse operational scenarios. The research methodology incorporates environmental impact factors, including carbon emission costs, land resource utilization, and water resource management, thereby extending traditional optimization frameworks to address region-specific ecological sensitivity concerns. The empirical results demonstrate that the optimized location configuration significantly reduces logistics operational costs while simultaneously enhancing both the economic efficiency and environmental sustainability, thus fostering regional economic coordination. This study makes several key contributions: (1) developing an integrated decision-making framework that balances economic efficiency and environmental sustainability; (2) systematically incorporating environmental impact factors into the optimization model; (3) establishing calibration methods specifically tailored for ecologically sensitive regions; and (4) demonstrating the potential for the synergistic optimization of economic and environmental objectives through strategic logistics network planning.