本文選題：物流配送　切入點(diǎn)：同時(shí)取送貨　出處：《浙江工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著現(xiàn)代物流產(chǎn)業(yè)的高速發(fā)展與人們對(duì)溫室效應(yīng)問題的高度關(guān)注,考慮碳排放量和客戶的多需求已經(jīng)成為車輛路徑問題研究的熱點(diǎn),通過合理設(shè)計(jì)車輛配送路線,在節(jié)約企業(yè)成本的同時(shí)降低二氧化碳的排放對(duì)于增加企業(yè)競爭力和抑制全球變暖有著重要的現(xiàn)實(shí)意義。本文針對(duì)同時(shí)取送貨低碳車輛調(diào)度中幾類典型問題,在分析其理論與實(shí)踐背景的基礎(chǔ)上,建立了多車型、基于多配送中心的多目標(biāo)以及動(dòng)態(tài)網(wǎng)絡(luò)多目標(biāo)同時(shí)取送貨低碳VRP模型,并研究了量子進(jìn)化算法對(duì)上述模型的求解,本文具體的研究工作如下:1.首先綜述了課題的研究背景和意義,通過調(diào)研國內(nèi)外同時(shí)取送貨VRP和低碳VRP來研究同時(shí)取送貨低碳VRP,針對(duì)目前研究中還存在的很多問題,提出了課題的主要研究內(nèi)容。2.根據(jù)車型種類,車輛總數(shù),客戶的取送貨需求,建立了以碳排放量最小為目標(biāo)的多車型同時(shí)取送貨低碳VRP的模型。模型中使用了考慮車型、距離和車輛載重量的碳排放計(jì)算方法。模型采用量子進(jìn)化算法進(jìn)行求解,最后通過算例測試,進(jìn)行了相應(yīng)的比較,實(shí)驗(yàn)結(jié)果表明了模型的正確性和算法的有效性。3.針對(duì)多配送中心問題,提出了將多個(gè)配送中心抽象成一個(gè)配送中心體系的策略,建立了多配送中心同時(shí)取送貨低碳VRP的多目標(biāo)優(yōu)化模型,模型以碳排放量最小和總路徑最短為目標(biāo)進(jìn)行優(yōu)化。針對(duì)該模型提出了多目標(biāo)QEA求解Pareto解。最后通過算例測試,實(shí)驗(yàn)結(jié)果表明了模型的正確性和本章提出的多目標(biāo)QEA求解多目標(biāo)問題的有效性。4.結(jié)合客戶的取送貨以及時(shí)間窗要求,根據(jù)旅行速度依賴函數(shù),建立了以總旅行時(shí)間最小和總碳排放量最低為目標(biāo)的動(dòng)態(tài)網(wǎng)絡(luò)同時(shí)取送貨多目標(biāo)優(yōu)化模型。針對(duì)該模型提出了多目標(biāo)協(xié)同QEA進(jìn)行求解。最后通過算例的參數(shù)分析與算法比較,實(shí)驗(yàn)結(jié)果表明了模型的正確性和本文所提出多目標(biāo)協(xié)同QEA對(duì)所求問題的有效性和求解的高效性。5.結(jié)合上述理論研究的基礎(chǔ)上搭建了同時(shí)取送貨低碳車輛調(diào)度仿真平臺(tái),該平臺(tái)集成了上述同時(shí)取送貨低碳調(diào)度模型以及所提的量子進(jìn)化算法,驗(yàn)證了所提算法的有效性。
[Abstract]:With the rapid development of modern logistics industry and people's high attention to Greenhouse Effect, considering the carbon emissions and customers' multi-needs has become a hot spot in the research of vehicle routing problem, through the rational design of vehicle distribution routes. Reducing carbon dioxide emissions while saving the cost of enterprises has important practical significance to increase the competitiveness of enterprises and curb global warming. This paper aims at several typical problems in the simultaneous delivery of low-carbon vehicle scheduling. Based on the analysis of its theoretical and practical background, a multi-vehicle model, a multi-objective model based on multi-distribution center and a multi-objective and multi-objective dynamic network model are established, and the solution of these models by quantum evolutionary algorithm (QEA) is studied. The specific research work of this paper is as follows: 1. Firstly, the research background and significance of the subject are summarized. Through the investigation and research, both domestic and foreign VRP and low carbon VRP are used to study the simultaneous delivery of low carbon VRP. There are still many problems in the current research. 2. According to the type of vehicle, the total number of vehicles and the customer's demand for receiving and delivering goods, a model of multi-vehicle model with minimum carbon emission and low carbon VRP is established. In the model, the model is used to consider the type of vehicle. The model is solved by quantum evolutionary algorithm (QEA). The experimental results show the correctness of the model and the validity of the algorithm. 3. Aiming at the problem of multiple distribution centers, a strategy of abstracting multiple distribution centers into a distribution center system is proposed. A multi-objective optimization model for multi-distribution centers is established, in which the minimum carbon emission and the shortest total path are taken as the objectives of multi-objective optimization model. A multi-objective QEA solution for the model is proposed. Finally, an example is given to test the solution. The experimental results show the correctness of the model and the effectiveness of the multi-objective QEA proposed in this chapter. 4. According to the travel speed dependent function, combined with customer delivery and time window requirements, A dynamic network with minimum total travel time and minimum total carbon emission as the target is established, and the multi-objective optimization model of delivery is proposed. Finally, a multi-objective cooperative QEA is proposed to solve the model. Finally, the parameter analysis and algorithm comparison are carried out through an example. The experimental results show the correctness of the model and the effectiveness of the multi-objective cooperative QEA proposed in this paper to solve the problem. 5. Based on the above theoretical research, a simulation platform for simultaneous delivery and low-carbon vehicle scheduling is built. The platform integrates the proposed low carbon scheduling model and the proposed quantum evolutionary algorithm to verify the effectiveness of the proposed algorithm.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U492.22

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1 李文;物流配送同時(shí)取送貨低碳車輛調(diào)度模型及其QEA研究[D];浙江工業(yè)大學(xué);2015年

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