摘要
针对农业供应链的运输环节中生鲜农产品配送模型存在的速度恒定、碳排放计算方法单一的问题,本研究结合路网时变特征和新的多车型碳排放计算方法,提出了考虑配送距离、多车型碳排放量、货物损耗和车辆固定成本等4个优化目标的生鲜农产品配送路径优化模型;并根据模型特点提出了一种改进的双策略种群协同蚁群算法(Double-Strategies Co-Evolutionary Ant Colony System,DC-ACS)。利用改进蚁群算法对Solomon数据集的C105算例进行了求解,在4个优化目标上分别取得最优解为937.94 km、4961.48元、4081.78元和7500.87元,证明了本研究提出的模型的有效性。在模型有效的基础上,通过试验结果证明,改进蚁群算法比基本蚁群算法在4个优化目标上的配送总成本平均降低幅度超过14%,证明改进蚁群算法更具有优越性。使用改进蚁群算法对集中、随机和混合3种不同分布的大规模算例进行求解,3种分布上分别求得最优总成本为19,939.53、24,095.00和24,397.58元。综上所述,所提模型和算法可以为冷链物流企业的城市配送路径决策提供良好的参考依据,对完善智慧农业供应链的配送路径优化模型和优化方法提供了新的思路,为企业进一步扩大规模提供了参考。
In view of the problems of constant speed and single carbon emission calculation method in the distribution model of fresh agricultural products in the transportation link of agricultural supply chain,combined with the time-varying characteris‐tics of road network and the new multi vehicle carbon emission calculation method,this study put forward the distribution route optimization model of fresh agricultural products with four optimization objectives,which were the distribution distance,multi vehicle carbon emission,goods loss and vehicle fixed cost.In this model,the calculation of fuel consumption and carbon emis‐sion in the model would be affected by many factors,among which the load is the most important factor:Firstly,the average fu‐el consumption per 100 km of different trucks was calculated,then the CO2 emission factors of various trucks were calculated according to the carbon balance principle,and finally the average value of the results of each truck was taken as the carbon emission factor of the vehicle.According to those characteristics of the model,an improved double strategies co-evolutionary ant colony system(DC-ACS)was proposed.In this study,the main method was used to transform the problem into a solvable single objective problem.Then,the ant colony algorithm combined the coevolution mechanism,adaptive pheromone update strategy and local search mechanism were used to improve the solution effect of the algorithm.Finally,an appropriate fitness calculation method and stagnation avoidance strategy were designed to enhance the ability of the algorithm to jump out of local optimization.The C105 example of Solomon dataset was solved by using the improved ant colony algorithm.The optimal solu‐tions on the four optimization objectives were 937.94 km,4961.48 CNY,4081.78 CNY and 7500.87 CNY respectively,which proved the effectiveness of the model proposed in this study.Based on the effectiveness of the model,the experimental results showed that the total distribution cost of the improved ant colony a
作者
刘思远
陈天恩
陈栋
张驰
王聪
LIU Siyuan;CHEN Tian'en;CHEN Dong;ZHANG Chi;WANG Cong(School of Computer,Electronics&Information,Guangxi University,Nanning 530004,China;National Engineering Research Center for Information Technology in Agriculture(NERCITA),Beijing 10097,China)
基金
北京市科技计划课题(Z191100004019007)。
