本文選題：危險(xiǎn)貨物 + 車輛路徑　； 參考：《蘭州交通大學(xué)》2015年碩士論文

【摘要】：危險(xiǎn)貨物運(yùn)輸車輛路徑優(yōu)化是保障危險(xiǎn)貨物安全運(yùn)輸?shù)幕A(chǔ)環(huán)節(jié)之一。由于危險(xiǎn)貨物的危險(xiǎn)性要求危險(xiǎn)貨物運(yùn)輸必須考慮運(yùn)輸風(fēng)險(xiǎn)這一因素,因此危險(xiǎn)貨物運(yùn)輸車輛路徑優(yōu)化問題是一個(gè)多目標(biāo)優(yōu)化問題,具有一定的復(fù)雜性。要在危險(xiǎn)貨物運(yùn)輸網(wǎng)絡(luò)中找到一條或多條風(fēng)險(xiǎn)、時(shí)間或其他屬性值最小的有效路徑,需要知道運(yùn)輸網(wǎng)絡(luò)中各路段上相應(yīng)的數(shù)據(jù)信息。然而,這些重要的已知數(shù)據(jù)中,部分?jǐn)?shù)據(jù)具有不確定性。由于基礎(chǔ)數(shù)據(jù)的不確定,所得到的危險(xiǎn)貨物運(yùn)輸路徑也不確定,有時(shí)候當(dāng)路段上的某一屬性值有細(xì)微變化時(shí),所得到的危險(xiǎn)貨物運(yùn)輸路徑有可能變得不可接受。因此,在不確定環(huán)境下,科學(xué)合理的設(shè)計(jì)危險(xiǎn)貨物運(yùn)輸車輛路徑,綜合考慮運(yùn)輸風(fēng)險(xiǎn)和運(yùn)輸時(shí)間等因素,找到具有較強(qiáng)魯棒性的“穩(wěn)健”的車輛路徑至關(guān)重要。本文以危險(xiǎn)貨物運(yùn)輸車輛路徑問題為研究對(duì)象,通過分析將該問題分為單配送中心危險(xiǎn)貨物單車配送路徑問題、單配送中心危險(xiǎn)貨物多車配送路徑問題、多配送中心危險(xiǎn)貨物單車配送路徑問題、多配送中心危險(xiǎn)貨物多車配送路徑問題等4個(gè)子問題,然后各自建立了魯棒性可調(diào)的不確定環(huán)境下危險(xiǎn)貨物運(yùn)輸車輛路徑多目標(biāo)Bertsimas魯棒優(yōu)化模型。針對(duì)單配送中心危險(xiǎn)貨物單車配送路徑多目標(biāo)魯棒模型,設(shè)計(jì)了一種采用莊家法構(gòu)造非支配個(gè)體,采用聚集距離保持進(jìn)化群體分布性的多目標(biāo)單親遺傳算法進(jìn)行求解;針對(duì)單配送中心危險(xiǎn)貨物多車配送路徑多目標(biāo)魯棒模型,給出了一種改進(jìn)的多目標(biāo)遺傳算法進(jìn)行求解;針對(duì)多配送中心危險(xiǎn)貨物單車配送路徑多目標(biāo)魯棒模型,設(shè)計(jì)了一種“兩階段法”對(duì)模型進(jìn)行求解,先通過第一階段的全局搜索聚類方法找到各配送中心所服務(wù)的客戶需求點(diǎn),然后由第二階段的多目標(biāo)單親遺傳算法對(duì)每個(gè)配送中心進(jìn)行依次求解;針對(duì)多配送中心危險(xiǎn)貨物多車配送路徑多目標(biāo)魯棒模型,設(shè)計(jì)了一種將多個(gè)配送中心和多個(gè)客戶需求點(diǎn)綜合考慮的混合多目標(biāo)遺傳算法進(jìn)行求解。論文針對(duì)每一個(gè)子問題都給出了一個(gè)算例,通過所設(shè)計(jì)的算法對(duì)算例求解,結(jié)果表明本文設(shè)計(jì)的多目標(biāo)遺傳算法能夠找到具有不同魯棒性的危險(xiǎn)貨物運(yùn)輸車輛路徑的Pareto解集,這對(duì)決策者找到一條或多條相對(duì)“穩(wěn)健”的危險(xiǎn)貨物運(yùn)輸路徑具有一定的參考價(jià)值。
[Abstract]:The route optimization of dangerous goods transportation vehicle is one of the basic links to ensure the safe transportation of dangerous goods. Because the dangerous goods must consider the transport risk, the route optimization problem of dangerous goods transportation vehicle is a multi-objective optimization problem, which has certain complexity. In order to find one or more effective paths with minimum risk, time or other attribute values in the transport network of dangerous goods, we need to know the corresponding data information on each section of the transport network. However, some of these important known data are uncertain. Because of the uncertainty of the basic data, the route of dangerous goods transportation is also uncertain. Sometimes, when there is a slight change in the value of a certain attribute on the road, the route of transport of dangerous goods obtained may become unacceptable. Therefore, under the uncertain environment, it is very important to design the vehicle route of dangerous goods transportation scientifically and reasonably, and to find the "robust" vehicle path with strong robustness considering the factors such as transportation risk and transportation time. This paper takes the vehicle routing problem of dangerous goods transportation as the research object, and divides the problem into single distribution center, single distribution center, single distribution center, multi-vehicle distribution route problem of dangerous goods. Four sub-problems, such as the multi-distribution center dangerous goods single cycle distribution path problem, the multi-distribution center dangerous goods multi-vehicle distribution path problem and so on four sub-problems, Then, the multi-objective Bertsimas robust optimization models of vehicle paths for dangerous goods transport in uncertain environments with adjustable robustness are established respectively. Aiming at the multi-objective robust model of single distribution center, a multi-objective single-parent genetic algorithm based on clustering distance to maintain the distribution of evolutionary population is proposed. For the multi-objective robust model of multi-vehicle distribution path of dangerous goods in single distribution center, an improved multi-objective genetic algorithm is presented, and the multi-objective robust model of single-vehicle distribution path of dangerous goods in multi-distribution center is proposed. A "two-stage method" is designed to solve the model. Firstly, the first stage global search clustering method is used to find the customer demand points served by each distribution center. Then the second stage multi-objective single-parent genetic algorithm is used to solve each distribution center in turn. For multi-distribution center, multi-objective robust model of dangerous goods multi-vehicle distribution path is proposed. A hybrid multi-objective genetic algorithm (MIGA) is designed to solve the problem, which takes multiple distribution centers and customer demand points into consideration. In this paper, an example is given for each sub-problem. The results show that the multi-objective genetic algorithm designed in this paper can find the Pareto solution set of vehicle paths with different robustness. It has certain reference value for decision makers to find one or more relatively "robust" transport routes of dangerous goods.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U492.336.3
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本文編號(hào)：1792163