本文選題：車輛路徑問題　切入點(diǎn)：多車型　出處：《西南交通大學(xué)》2014年碩士論文

【摘要】：隨著全球氣候變暖,減少能耗、控制碳排放變得日益重要。物流作為能源消耗量較大的行業(yè),應(yīng)肩負(fù)起節(jié)能減排的責(zé)任,倡導(dǎo)低碳物流,改變物流粗放低效率的運(yùn)作模式。配送是物流的一個(gè)重要環(huán)節(jié),其運(yùn)輸過程中存在著返程或起程空駛、交叉運(yùn)輸、迂回運(yùn)輸、重復(fù)運(yùn)輸?shù)炔缓侠憩F(xiàn)象,燃油的消耗量也就無(wú)法忽略不計(jì),這就需要對(duì)配送路線進(jìn)行合理優(yōu)化,并且優(yōu)化過程中應(yīng)更多考慮加入減少能耗作為目標(biāo)之一。而在實(shí)際配送中,車輛類型不止一種,不同車型還具有不同的裝載能力、不同的固定成本、不同的行駛距離等特點(diǎn),多車型的車輛路徑問題更具有現(xiàn)實(shí)意義。因此考慮能耗的多車型車輛路徑問題成為本文研究的重點(diǎn),也更具有理論和實(shí)踐上的指導(dǎo)意義。 本文首先總結(jié)歸納了國(guó)內(nèi)外多車型車輛路徑問題研究現(xiàn)狀,并重點(diǎn)分析了多車型車輛路徑問題的構(gòu)成要素、數(shù)學(xué)模型和求解算法,尤其是對(duì)求解多車型問題的算法進(jìn)行了分析,遺傳算法因其強(qiáng)魯棒性、全局收斂、易于操作以及較少應(yīng)用于多車型求解中而成為本文選擇的算法。然后結(jié)合低碳物流節(jié)能減排的思想,將與車型相關(guān)的能耗成本和固定成本之和作為優(yōu)化目標(biāo),建立起多車型低耗車輛路徑問題(Fleet Size and Mix Vehicle Routing Problem with Energy Minimizing, FSMVRPEM),并設(shè)計(jì)改進(jìn)遺傳算法進(jìn)行求解,在基準(zhǔn)測(cè)試上驗(yàn)證算法的可行性和有效性,并取得了較好的效果。最后將多車型低耗車輛路徑問題應(yīng)用于快速消費(fèi)品的配送中心,由于其零售網(wǎng)點(diǎn)分布密集、需求量小但穩(wěn)定的特點(diǎn),當(dāng)前物流中心采用固定路線的配送方案。針對(duì)于此本文從整體出發(fā),選取市區(qū)作為優(yōu)化范圍,結(jié)果不僅減少了車輛使用,還提高了服務(wù)水平,是一種較為合理的優(yōu)化方案。因模型數(shù)據(jù)獲取的方便性以及求解算法的可行性和有效性,是車輛調(diào)度安排和線路優(yōu)化較好的決策工具。 對(duì)于算法中實(shí)現(xiàn)車型選擇的直觀性和高效性還需要進(jìn)一步探討,能否將多車型低耗車型路徑問題模型應(yīng)用于實(shí)際中求解大規(guī)模的顧客點(diǎn)的問題還需要進(jìn)一步的研究。
[Abstract]:With global warming, reducing energy consumption, carbon emissions control has become increasingly important.Logistics, as an industry with large energy consumption, should shoulder the responsibility of energy saving and emission reduction, advocate low-carbon logistics, and change the operation mode of extensive and inefficient logistics.Distribution is an important part of logistics. In the process of transportation, there are unreasonable phenomena such as return or departure empty driving, cross transportation, roundabout transportation, repeated transportation, etc., so the consumption of fuel can not be ignored.Therefore, it is necessary to optimize the distribution route reasonably, and more consideration should be given to reducing energy consumption in the process of optimization.But in the actual distribution, there is more than one type of vehicle, different models also have different loading capacity, different fixed cost, different driving distance and so on.Therefore, the multi-vehicle routing problem considering energy consumption has become the focus of this paper, and also has theoretical and practical significance.Firstly, this paper summarizes the research status of multi-vehicle routing problem at home and abroad, and focuses on the analysis of the components, mathematical model and algorithm of multi-vehicle vehicle routing problem, especially the algorithm to solve multi-model vehicle routing problem.Genetic algorithm (GA) has been chosen in this paper because of its strong robustness, global convergence, ease of operation and less application in multi-vehicle solution.Then combined with the idea of low carbon logistics energy saving and emission reduction, the sum of energy consumption cost and fixed cost related to vehicle type is taken as the optimization goal.The Fleet Size and Mix Vehicle Routing Problem with Energy optimization problem is established and improved genetic algorithm is designed to solve the problem. The feasibility and effectiveness of the algorithm are verified in the benchmark test, and good results are obtained.Finally, the multi-model low-consumption vehicle routing problem is applied to the distribution center of fast moving consumer goods. Due to the characteristics of dense distribution of retail outlets and small but stable demand, the current logistics center adopts a fixed route distribution scheme.In view of this, this paper chooses the urban area as the optimization range from the whole, the result not only reduces the vehicle use, but also improves the service level, is one kind of more reasonable optimization plan.Because of the convenience of model data acquisition and the feasibility and effectiveness of the algorithm, it is a good decision tool for vehicle scheduling and route optimization.The realization of visualization and efficiency of vehicle selection in the algorithm needs to be further discussed. Whether the multi-model low-consumption vehicle path problem model can be applied to the practical problem of solving large-scale customer points still needs further study.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U116

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