發(fā)布時(shí)間：2018-01-04 11:33

  本文關(guān)鍵詞：基于文化基因算法的動(dòng)態(tài)車輛路徑問題研究　出處：《河南大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 不確定環(huán)境 動(dòng)態(tài)車輛路徑問題 文化基因算法 客戶滿意度

【摘要】：隨著現(xiàn)代物流的發(fā)展,物流配送問題逐步發(fā)展成為制約企業(yè)發(fā)展的瓶頸,優(yōu)化物流配送中的車輛路徑問題成為了研究的重點(diǎn)�，F(xiàn)代物流是以滿足客戶的需求為目標(biāo),把生產(chǎn)、運(yùn)輸、銷售等市場(chǎng)情況統(tǒng)一考慮的一種戰(zhàn)略措施,其實(shí)質(zhì)是企業(yè)策劃者通過合理規(guī)劃使人力、物力、財(cái)力在有效的時(shí)間內(nèi)高效的利用。如何實(shí)現(xiàn)物流的系統(tǒng)化、現(xiàn)代化、合理化對(duì)于社會(huì)發(fā)展至關(guān)重要。車輛路徑問題(Vehicle Routing Problem,VRP)在物流配送中扮演很重要的角色,一直是業(yè)界人士研究的熱點(diǎn)問題,然而現(xiàn)代社會(huì)各行各業(yè)的發(fā)展,物品交換頻繁,物類繁多復(fù)雜,路面狹窄、道路交通混亂、城市擁擠、車輛拋錨、交通事故等一系列的不可預(yù)料的問題,導(dǎo)致車輛行駛的時(shí)間過長(zhǎng),物品未能及時(shí)的送達(dá)客戶,客戶服務(wù)的滿意度由此降低。本文將研究不確定環(huán)境下動(dòng)態(tài)車輛路徑問題,分析動(dòng)態(tài)的車輛路徑和靜態(tài)的車輛路徑之間的關(guān)系;以車輛行駛的時(shí)間最短和最大化客戶滿意度為目標(biāo)函數(shù),根據(jù)問題影響因素,建立數(shù)學(xué)模型;在設(shè)計(jì)的文化基因算法的基礎(chǔ)上求解動(dòng)態(tài)車輛路徑問題,并介紹算法的主要方法及實(shí)現(xiàn)步驟。對(duì)不確定環(huán)境下動(dòng)態(tài)車輛路徑問題進(jìn)行詳細(xì)的分析,運(yùn)用文化基因算法進(jìn)行求解,文化基因算法是一種全局搜索和局部搜索的啟發(fā)式搜索相結(jié)合的一種算法,本文采用遺傳算法和禁忌搜索策略相結(jié)合求解此問題。本文運(yùn)用文化基因算法來求解動(dòng)態(tài)車輛路徑優(yōu)化問題,根據(jù)問題設(shè)計(jì)了文化基因算法的實(shí)現(xiàn)步驟,包括染色體編碼方式、初始化、設(shè)置適應(yīng)度函數(shù)、進(jìn)化操作、局部搜索的具體步驟、算法終止條件等;進(jìn)化操作中選擇、交叉、變異操作進(jìn)行全局搜索,運(yùn)用禁忌搜索算法作為局部搜索策略,對(duì)局部搜索的具體步驟進(jìn)行分析及介紹,遺傳算法和禁忌搜索算法相結(jié)合求解動(dòng)態(tài)車輛路徑問題,得到了問題的最優(yōu)解,有效處理新訂單的增加和老訂單的取消或修改等不確定事件,及時(shí)有效的改變車輛路調(diào)度方案。
[Abstract]:With the development of modern logistics, logistics distribution has gradually become a bottleneck restricting the development of enterprises. The optimization of vehicle routing in logistics distribution has become the focus of the research. Modern logistics is a strategic measure to meet the needs of customers and consider the market conditions such as production, transportation, sales and so on. Its essence is how to realize the systematization and modernization of logistics by the enterprise planner making efficient use of manpower, material resources and financial resources in an effective time through rational planning. Rationalization is very important for social development. Vehicle Routing problem (VRP) plays an important role in logistics distribution. It has always been a hot topic in the industry. However, with the development of various industries in modern society, the exchange of goods is frequent, the kinds of things are complicated, the road surface is narrow, the road traffic is chaotic, the city is crowded, the vehicles break down. A series of unexpected problems, such as traffic accidents, lead to the excessive travel time of vehicles and the failure of goods to reach customers in time. This paper will study the dynamic vehicle routing problem in uncertain environment and analyze the relationship between dynamic vehicle routing and static vehicle routing. Taking the shortest travel time and maximum customer satisfaction as objective function, the mathematical model is established according to the influencing factors of the problem. Based on the design of the cultural genetic algorithm, the dynamic vehicle routing problem is solved, and the main methods and implementation steps of the algorithm are introduced. The dynamic vehicle routing problem in uncertain environment is analyzed in detail. Cultural gene algorithm is used to solve the problem. Cultural gene algorithm is a combination of global search and local search heuristic search algorithm. In this paper, genetic algorithm and Tabu search strategy are used to solve this problem. In this paper, the cultural gene algorithm is used to solve the dynamic vehicle routing optimization problem, and the implementation steps of the cultural gene algorithm are designed according to the problem. Including chromosome coding, initialization, setting fitness function, evolutionary operation, local search specific steps, algorithm termination conditions, and so on; The selection, crossover and mutation operations in evolutionary operations are used for global search. Tabu search algorithm is used as a local search strategy to analyze and introduce the specific steps of local search. Genetic algorithm and Tabu search algorithm are combined to solve the dynamic vehicle routing problem, the optimal solution is obtained, and the uncertain events such as the increase of new orders and the cancellation or modification of old orders are effectively handled. Timely and effective change of vehicle routing plan.
【學(xué)位授予單位】：河南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP18

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