大型城市地下物流网络优化布局的模拟植物生长算法

Optimization layout of underground logistics network in big cities with plant growth simulation algorithm

交通拥堵问题的加剧使传统物流网络在我国大型城市已达到极限,未来地面物流系统将逐步向地下不同层次里转移并释放出城市地上空间.本文以斯坦纳最小树(SMT)为理论模型,建立了符合我国大型城市不断扩展这一特点的树状地下物流网络布局模型.由于SMT为NP-完全问题,因此算法的寻优能力是研究的关键.本文所采用的模拟植物生长算法(PGSA)是以植物向光性理论为启发式准则的智能算法,该算法是利用人工植物在给定物流节点集解空间中的生长过程得到城市地下物流网络的最优布局.通过对国际公布的STEINLIB实例数据计算并与蚁群算法和模拟退火算法进行比较,表明模拟植物生长算法具有较强的精确性、稳定性和全局搜索能力.

With the aggravation of traffic jam traditional logistics network has reached high-point in big cities in China, so the logistics system on the ground will gradually transfer to underground on various layers in the future so as to release the ground space in cities. Based on SMT theory, this paper establishes underground tree logistics network layout model. Because SMT is NP-complete problem, the algorithm optimization capability is the key of research. Plant growth simulation algorithm （PGSA） in this paper is an intelligence optimization algorithm, which takes plant phototropism growth pattern as its heuristic criterion. Through artificial plant growth process in solution space of given logistics node set, we can get the optimal layout of underground logistics network in cities. Through the calculation of STEINLIB lab data announced internationally, PGSA is demonstrated with better accuracy, stability and global searching ability, by comparing the solutions of ant algorithm and simulated annealing algorithm.