Green city logistics path planning and design based on genetic algorithm

Abstract

Effective logistics distribution paths are crucial in enhancing the fundamental competitiveness of an enterprise. This research introduces the genetic algorithm for logistics routing to address pertinent research issues, such as suboptimal scheduling of time-sensitive orders and reverse distribution of goods. It proposes an enhanced scheme integrating the Metropolis criterion. To address the limited local search ability of the genetic algorithm, this study combines the simulated annealing algorithm’s powerful local optimization capability with the genetic algorithm, thereby developing a genetic algorithm with the Metropolis criterion. The proposed method preserves the optimal chromosome in each generation population and accepts inferior chromosomes with a certain probability, thereby enhancing the likelihood of finding an optimal local solution and achieving global optimization. A comparative study is conducted with the Ant Colony Optimization, Artificial Bee Colony, and Particle Swarm Optimization algorithms, and empirical findings demonstrate that the proposed genetic algorithm effectively achieves excellent results over these algorithms.

Evolution Model and Driving Mechanism of Urban Logistics Land: Evidence from the Yangtze River Delta

“…Guo [40] and Fahimnia [41] proposed methods for planning underground logistics and industrial logistics systems, respectively. With technological innovation, more innovative methods and models are applied to urban logistics spatial planning [42], such as the spatial design of a logistics network system and distribution paths based on hybrid heuristic algorithms [43] and genetic algorithms [44], and the planning and control model of intelligent logistics facilities, spaces, and parks based on the logistics 4.0 framework [45].…”