基于GASA算法的成品燃油战时公路配送路径优化

为了实现配送的路径优化,结合战时极其重要的物资--成品燃油,提出了多属性道路网络下成品燃油战时配送的路径优化问题.综合考虑战时配送VRP(vehicle routing problem)的多个评价目标,基于重要性的多目标分层优化思想,分析建立了问题的完全分层优化模型.将进化算法和传统优化技术相结合,构造了模型的两层求解算法,第一层采用遗传算法和模拟退火算法混合的GASA(genetic simulated annealing algorithms)算法,第二层采用枚举法.实验表明,所构造的算法较标准遗传算法更有效.

Optimization of wartime distributing routes of finished fuel oil based on genetic simulated annealing algorithms

In order to obtain optimized distributing routes,with the example of finished fuel oil,one of the extraordinary significant strategic materials, vehicle routing problem of wartime distribution in multi-attribute road networks was presented.Based on the significance and hiberarchy of evaluation objectives,factors influencing optimization were analyzed and a completely stratified programing model established.By combining evolveing algorithm with conventional optimization method,a concrete algorithm for the model was constructed,first adopting GASA(genetic simulated annealing algorithms),and secondly enumeration algorithm.The results of the simulation indicate the GASA has higher efficiency than simple genetic algorithms.