【摘要】：隨著現(xiàn)在電子商務(wù)的飛速發(fā)展,物流運(yùn)輸行業(yè)逐漸成為當(dāng)今社會(huì)的熱門(mén)行業(yè),如何快速、高效、安全和便捷地運(yùn)送貨物已經(jīng)成為現(xiàn)階段各大企業(yè)關(guān)注的焦點(diǎn)。車(chē)輛路徑問(wèn)題是物流運(yùn)輸行業(yè)中十分重要的問(wèn)題,自從被提出以來(lái),就在運(yùn)籌學(xué)與組合優(yōu)化等多個(gè)領(lǐng)域受到了研究者的關(guān)注。本文研究的內(nèi)容是多目標(biāo)帶時(shí)間窗的車(chē)輛路徑問(wèn)題(Vehicle Routing Problem with Time Windows,VRPTW)。先對(duì)VRPTW問(wèn)題采用多目標(biāo)模型,同時(shí)最小化車(chē)輛數(shù)目和行駛距離這兩個(gè)目標(biāo),通過(guò)改進(jìn)的遺傳算法求解該問(wèn)題模型。該改進(jìn)遺傳算法首先采用隨機(jī)法和前相插入法產(chǎn)生初始解,然后運(yùn)用最佳個(gè)體選擇和輪盤(pán)賭法對(duì)群體中的優(yōu)良個(gè)體進(jìn)行選擇操作,改進(jìn)交叉操作使個(gè)體的性能達(dá)到更優(yōu),改進(jìn)變異操作促進(jìn)種群的多樣性。接著,再進(jìn)行局部搜索操作,在鄰域內(nèi)搜尋到更好的解,結(jié)合改進(jìn)非支配排序遺傳算法(Non-dominated Sorting Genetic Algorithm,NSGA-II),將新的種群與上一代種群組合進(jìn)行非支配排序,尋找Pareto最優(yōu)解集。最后,本文改進(jìn)的遺傳算法通過(guò)VC++6.0進(jìn)行編程,在Solomon Benchmark測(cè)試集上進(jìn)行了實(shí)驗(yàn),并將其中一部分測(cè)試集的結(jié)果與其他文獻(xiàn)的結(jié)果進(jìn)行了比較,實(shí)驗(yàn)結(jié)果表明了改進(jìn)遺傳算法具有較好的性能。
[Abstract]:With the rapid development of electronic commerce, logistics transportation industry has gradually become a hot industry. How to transport goods quickly, efficiently, safely and conveniently has become the focus of attention of large enterprises at this stage. Vehicle routing problem (VRP) is an important problem in logistics and transportation industry. Since it was proposed, it has attracted the attention of researchers in many fields, such as operational research and combinatorial optimization. This paper focuses on the vehicle routing problem with time windows (Vehicle Routing Problem with Time Windows Windows VRPTW). First, the multi-objective model is adopted to solve the VRPTW problem, and the two objectives of minimizing the number of vehicles and the driving distance are minimized, and the improved genetic algorithm is used to solve the model. The improved genetic algorithm first uses random method and antecedent insertion method to produce initial solution, then uses the best individual selection and roulette method to select the superior individual in the population, and improves the crossover operation to make the individual's performance better. Improved mutation operation promotes population diversity. Then, a local search operation is carried out, and a better solution is found in the neighborhood. Combined with the improved Non-dominated Sorting Genetic algorithm (NSGA-II), the new population is combined with the previous generation population to search for the optimal solution set of Pareto. Finally, the improved genetic algorithm is programmed by VC 6.0, and the experiment is carried out on the Solomon Benchmark test set, and the results of some of the test sets are compared with the results of other documents. Experimental results show that the improved genetic algorithm has better performance.
【學(xué)位授予單位】：廣東財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U116

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本文編號(hào)：2176101