本文選題：VRPTW　切入點(diǎn)：蟻群算法　出處：《西安科技大學(xué)》2017年碩士論文

【摘要】：隨著經(jīng)濟(jì)的繁榮發(fā)展,企業(yè)對(duì)物流配送的需求量急劇增加,對(duì)物流配送服務(wù)質(zhì)量的要求也越來(lái)越高。對(duì)于物流配送企業(yè)來(lái)說,要保持核心競(jìng)爭(zhēng)力,就要在提高服務(wù)質(zhì)量的同時(shí)降低運(yùn)輸成本。本文首先將物流企業(yè)所面臨的問題歸類于帶時(shí)間窗車輛路徑問題(VRPTW),并建立以固定成本、可變成本、時(shí)間懲罰費(fèi)用最小化為目標(biāo)函數(shù)的數(shù)學(xué)模型,在所有可行方案中保留滿足約束的最優(yōu)方案,該方案既節(jié)省了物流企業(yè)的配送成本,又提高了客戶對(duì)服務(wù)的滿意程度。其次,將遺傳、蟻群兩種算法融合得到改進(jìn)的蟻群混合算法(ACO-GAF)。為了使螞蟻尋優(yōu)結(jié)果更符合VRPTW的需求,在蟻群狀態(tài)轉(zhuǎn)移概率公式中增加容量因素和時(shí)間窗緊度因素;為了使遺傳算法跳出局部最優(yōu),在交叉、變異操作后引入魚群算子繼續(xù)尋優(yōu)。再將遺傳、蟻群尋優(yōu)解群體合并,計(jì)算適應(yīng)度函數(shù)后通過輪盤賭選擇出最優(yōu)個(gè)體,尋優(yōu)完成后更新路徑信息素。在MATLAB平臺(tái)上,使用Solomon數(shù)據(jù)庫(kù)中的RC系列算例,設(shè)置適當(dāng)?shù)膮?shù)值,以路程最短和車輛數(shù)最少為目標(biāo),并將ACO-GAF算法求解各算例的結(jié)果與目前最優(yōu)解進(jìn)行對(duì)比,結(jié)果表明ACO-GAF算法在減少車輛數(shù)上取得了較大的進(jìn)步;另外,將ACO-GAF算法求解結(jié)果與遺傳算法、蟻群算法、魚群算法求解結(jié)果進(jìn)行對(duì)比,ACO-GAF算法在尋優(yōu)效率和尋優(yōu)結(jié)果方面均優(yōu)于各單一算法。最后,將帶時(shí)間窗車輛路徑問題數(shù)學(xué)模型應(yīng)用于實(shí)例中,以某廠商為華潤(rùn)萬(wàn)家超市配送貨物為例,使用ACO-GAF算法求解車輛配送路線方案,求解結(jié)果在總路程、遲到懲罰費(fèi)用方面得到了優(yōu)化,求解結(jié)果表明本文建立的求解帶時(shí)間窗車輛路徑問題模型具有一定的實(shí)用性,求解帶時(shí)間窗車輛路徑問題的ACO-GAF算法有效可行。
[Abstract]:With the development of economy, the demand for logistics and distribution is increasing rapidly, and the requirement for service quality of logistics distribution is becoming higher and higher. For logistics distribution enterprises, it is necessary to maintain the core competitiveness. At the same time, it is necessary to reduce the transportation cost while improving the service quality. Firstly, the problems faced by the logistics enterprises are classified into the vehicle routing problem with time window (VRPTWN), and the fixed cost and variable cost are established. The mathematical model of minimizing the cost of time punishment as the objective function preserves the optimal scheme which satisfies the constraints in all feasible schemes. This scheme not only saves the distribution cost of the logistics enterprise, but also improves the satisfaction degree of the customer to the service. ACO-GAFA, an improved ant colony hybrid algorithm, is fused by genetic and ant colony algorithms. In order to meet the requirements of VRPTW, the capacity factor and the time window tightness factor are added to the ant colony state transition probability formula. In order to make the genetic algorithm jump out of the local optimum, after crossover, mutation operation, the fish swarm operator is introduced to continue the optimization. Then the genetic and ant colony optimization solutions are combined, the fitness function is calculated and the optimal individual is selected by roulette. Update path pheromone after optimization. On MATLAB platform, use RC series of examples in Solomon database, set appropriate parameter value, take the shortest distance and the least number of vehicles as the target, The results of ACO-GAF algorithm are compared with the current optimal solution. The results show that the ACO-GAF algorithm has made great progress in reducing the number of vehicles. In addition, the result of ACO-GAF algorithm is compared with that of genetic algorithm, Ant Colony algorithm (ACA). Compared with the results of fish swarm algorithm, ACO-GAF algorithm is superior to any single algorithm in the efficiency and result of optimization. Finally, the mathematical model of vehicle routing problem with time window is applied to an example. Taking a manufacturer as an example, the ACO-GAF algorithm is used to solve the vehicle distribution route scheme. The result is optimized in the aspects of total distance, late penalty cost, etc. The results show that the model established in this paper is practical and the ACO-GAF algorithm for solving the vehicle routing problem with time window is effective and feasible.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F252;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李秀娟;楊s，

本文編號(hào)：1653375