本文關(guān)鍵詞： 區(qū)間規(guī)劃 不確定性 混合遺傳算法 軸輻式網(wǎng)絡(luò)　出處：《大連海事大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：在集裝箱軸輻式班輪網(wǎng)絡(luò)設(shè)計(jì)中,一個(gè)重要參數(shù)是各港口間的集裝箱運(yùn)輸需求。當(dāng)需求發(fā)生變化時(shí),優(yōu)化設(shè)計(jì)的網(wǎng)絡(luò)隨之發(fā)生變化。優(yōu)化設(shè)計(jì)的最終航線(xiàn)網(wǎng)絡(luò)結(jié)構(gòu)一旦決策就很難改變,以確定需求為模型構(gòu)建參數(shù)的航線(xiàn)網(wǎng)絡(luò)優(yōu)化不能隨市場(chǎng)運(yùn)輸需求的變化,使決策者承擔(dān)相當(dāng)大的風(fēng)險(xiǎn)。因此,需要考慮不確定性條件下的軸輻式網(wǎng)絡(luò)優(yōu)化。 對(duì)于不確定班輪問(wèn)題的處理,主要有隨機(jī)規(guī)劃、模糊規(guī)劃、情景集魯棒優(yōu)化等方法。其中,隨機(jī)規(guī)劃方法需要不確定變量的概率分布,模糊規(guī)劃需要隸屬度函數(shù)、情景集法需要情景概率分布。港口的復(fù)雜性使實(shí)際的不確定變量分布難以獲取。因此,以上方法有其相應(yīng)的局限性。 在此條件下,本文嘗試引入?yún)^(qū)間集合形式約束集裝箱運(yùn)輸需求參數(shù),以運(yùn)輸成本和中轉(zhuǎn)成本總成本為目標(biāo)函數(shù),建立混合整數(shù)線(xiàn)性區(qū)間規(guī)劃,聯(lián)合優(yōu)化樞紐港選址、支線(xiàn)港配置、干線(xiàn)航線(xiàn)三個(gè)問(wèn)題。引入風(fēng)險(xiǎn)因子,將含有區(qū)間形式的目標(biāo)函數(shù)轉(zhuǎn)化確定性函數(shù),從而進(jìn)行優(yōu)化求解。 本文建立的模型為NP-hard問(wèn)題。模型的復(fù)雜性決定了問(wèn)題求解的難度,鑒于此,提出利用GA(遺傳算法)和AC(蟻群算法)相結(jié)合的混合遺傳算法求解。其中,樞紐港選址利用遺傳算法,支線(xiàn)港配置利用最短路徑法,干線(xiàn)航線(xiàn)優(yōu)化采用蟻群算法。但整個(gè)算法仍以遺傳算法為框架,選取目標(biāo)函數(shù)值的倒數(shù)作為適應(yīng)度函數(shù)；選取最優(yōu)個(gè)體和輪盤(pán)賭相結(jié)合的算子作為選擇算子；采用單點(diǎn)交叉作為交叉算子；采用基因互換作為變異算子。 最后利用算例驗(yàn)證模型的可行性和算法的有效性。同時(shí)也說(shuō)明利用區(qū)間規(guī)劃處理需求不確定性問(wèn)題,不僅能體現(xiàn)數(shù)據(jù)非精確性,同時(shí)求解結(jié)果能包含不確定性信息并且在一定程度上反應(yīng)集裝箱需求信息,因此能夠使決策者更為詳細(xì)地了解風(fēng)險(xiǎn)狀態(tài)與后果。
[Abstract]:In the design of container axis-spoke liner network, an important parameter is the container transportation demand between ports. The final route network structure of optimization design is difficult to change once the decision is made, and the route network optimization based on the model construction parameters can not change with the market transportation demand. Therefore, it is necessary to consider the radial network optimization under uncertain conditions. For the treatment of uncertain liner problems, there are mainly stochastic programming, fuzzy programming, scenario set robust optimization and other methods, in which the stochastic programming method needs the probability distribution of uncertain variables. Fuzzy programming needs membership function, scenario set method needs scenario probability distribution, and the complexity of port makes it difficult to obtain the actual uncertain variable distribution. Therefore, the above method has its corresponding limitations. Under this condition, this paper attempts to introduce interval set form to constrain container transportation demand parameters, take the total cost of transportation and transit cost as the objective function, and establish mixed integer linear interval programming. By introducing risk factors, the objective function with interval form can be transformed into deterministic function, which can be solved optimally by jointly optimizing the location of hub port, the configuration of branch port and the route of trunk line. The model established in this paper is a NP-hard problem. The complexity of the model determines the difficulty of solving the problem. A hybrid genetic algorithm based on GA (genetic algorithm) and AC (Ant Colony algorithm) is proposed, in which the location of hub port is based on genetic algorithm, and the shortest path method is used in the configuration of branch port. Ant colony algorithm is used in trunk route optimization, but the whole algorithm is still based on genetic algorithm, and the reciprocal of objective function is selected as fitness function. The operator which combines the optimal individual and roulette is chosen as the selection operator. Single point crossover is used as crossover operator. Gene exchange was used as mutation operator. Finally, the feasibility of the model and the validity of the algorithm are verified by an example. At the same time, the use of interval planning to deal with demand uncertainty can not only reflect the inaccuracy of the data. At the same time, the solution results can contain uncertainty information and reflect container demand information to a certain extent, so that the decision makers can understand the risk state and consequences in more detail.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：U695.22

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