交通运输研究
交通運輸研究
교통운수연구
Transportation Standardization
2015年
4期
88-94
,共7页
物流配送%车辆路径问题%遗传算法%交叉算子%收敛性
物流配送%車輛路徑問題%遺傳算法%交扠算子%收斂性
물류배송%차량로경문제%유전산법%교차산자%수렴성
logistics distribution%vehicle routing problem%genetic algorithm%crossover operator%convergence
为了研究物流中心的服务效率和车辆的合理调度方案,以汽车载重量作为影响车辆路线安排的主要因素,以经典的车载容量约束条件下的车辆路径问题为原型建立数学模型,通过求解该数学模型的最优解来获得车辆最优路径。由初始状态随机生成的可行解作为初始的车辆路径方案,通过改进的遗传算法不断地调整染色体的交叉和变异概率进行优化,最终得到物流中心车辆安排的合理方案。通过多次求解算例,都能够得到满意的车辆路径方案,不仅验证了该数学模型的有效性和实践性,而且也验证了改进后遗传算法的收敛性和鲁棒性,同时得到了改进遗传算法交叉和变异概率的调整范围。该模型和算法不仅可以提高物流中心的服务效率,而且可以为物流中心的车辆调度方案提供支持和帮助。
為瞭研究物流中心的服務效率和車輛的閤理調度方案,以汽車載重量作為影響車輛路線安排的主要因素,以經典的車載容量約束條件下的車輛路徑問題為原型建立數學模型,通過求解該數學模型的最優解來穫得車輛最優路徑。由初始狀態隨機生成的可行解作為初始的車輛路徑方案,通過改進的遺傳算法不斷地調整染色體的交扠和變異概率進行優化,最終得到物流中心車輛安排的閤理方案。通過多次求解算例,都能夠得到滿意的車輛路徑方案,不僅驗證瞭該數學模型的有效性和實踐性,而且也驗證瞭改進後遺傳算法的收斂性和魯棒性,同時得到瞭改進遺傳算法交扠和變異概率的調整範圍。該模型和算法不僅可以提高物流中心的服務效率,而且可以為物流中心的車輛調度方案提供支持和幫助。
위료연구물류중심적복무효솔화차량적합리조도방안,이기차재중량작위영향차량로선안배적주요인소,이경전적차재용량약속조건하적차량로경문제위원형건립수학모형,통과구해해수학모형적최우해래획득차량최우로경。유초시상태수궤생성적가행해작위초시적차량로경방안,통과개진적유전산법불단지조정염색체적교차화변이개솔진행우화,최종득도물류중심차량안배적합리방안。통과다차구해산례,도능구득도만의적차량로경방안,불부험증료해수학모형적유효성화실천성,이차야험증료개진후유전산법적수렴성화로봉성,동시득도료개진유전산법교차화변이개솔적조정범위。해모형화산법불부가이제고물류중심적복무효솔,이차가이위물류중심적차량조도방안제공지지화방조。
In order to research the service efficiency and reasonable scheduling scheme of vehicles of a logistics center, the vehicle load was taken as the main factor which influenced the vehicle routing ar?rangement, the classical capacitated vehicle routing problem was used as the prototype to establish the mathematical model, and the optimal path was obtained by solving the optimal solution of the mathemati?cal model. The initial feasible solution generated randomly served as the initial vehicle routing plan, a reasonable vehicle arrangement plan of logistics center was found finally by constantly adjusting chromo?somal crossover and mutation probability of the improved genetic algorithm to optimize the solution. It verifies the effectiveness and practicality of the proposed model, the convergence and robustness of the improved genetic algorithm by solving a numerical example for many times. Meanwhile the adjusting range of crossover and mutation probability of the improved genetic algorithm are obtained. The model and algorithm not only improves the service efficiency of logistics center, but also provide support and help for the vehicle scheduling scheme of logistics center.