生鲜食品冷链物流配送路径优化

Optimization of Fresh Food Cold Chain Logistics Distribution Route

由于模拟退火算法在求解优化路径的时候容易陷入局部最优,因此通过对传统的模拟退火路径优化算法进行改进来提高算法的求解效果。以冷链配送过程中的总成本为优化目标,以车辆载重以及客户要求为约束条件建立模型,分析温度和时间对各项成本的影响,分别讨论运输过程和卸货过程的制冷成本和货损成本,并用指数函数表示生鲜食品的腐败规律。通过调整模拟退火算法中Metropolis准则的接受概率并分别对不同接受概率的算法进行实验仿真,结果表明,接受概率对模拟退火算法在生鲜食品冷链物流配送路径优化问题上的求解效果产生影响,减小接受概率,可以提高算法的局部求解精度和收敛速度,降低平均收敛代数;提高接受概率,能够扩大搜索范围,降低算法陷入局部最优的概率。

When searching for the optimal path via the simulated annealing algorithm(SAA),it is easy to get trapped in local optimums,so we focus on improving the algorithm’s efficiency by optimizing the traditional SAA.Taking the total cost in the cold chain transportation as the target and the vehicle load and customer requirements as the constraints,we establish a model to analyze the impact of temperature and time on each cost.We also figure out the cost of cooling and damage loss during the process of delivering and unloading the goods and describe the rate of food spoilage as an exponential function.By adjusting the acceptance probability of the Metropolis criterion in the simulated annealing algorithm and experimentally simulating the algorithms with different acceptance probabilities,it is concluded that the acceptance probability can affect the solving efficiency of SAA when working on the optimization problem of fresh food cold chain logistics distribution path.If reducing the acceptance probability,we can improve the accuracy of local solution,the convergence speed of the algorithm and also reduce the average convergence algebra.If increasing the acceptance probability,we can expand the search range and reduce the probability that the algorithm falls into local optimum.