發(fā)布時(shí)間：2018-07-09 20:43

  本文選題：車(chē)輛時(shí)間窗 + 客戶時(shí)間窗��； 參考：《重慶工商大學(xué)》2015年碩士論文

【摘要】：隨著社會(huì)經(jīng)濟(jì)的快速發(fā)展,許多物流公司往往擁有多個(gè)配送中心(車(chē)場(chǎng))。配送中心之間如何進(jìn)行合理的任務(wù)分配及恰當(dāng)?shù)能?chē)輛行駛路線安排以實(shí)現(xiàn)企業(yè)配送成本的降低和服務(wù)質(zhì)量的提升,就成為企業(yè)非常關(guān)心的問(wèn)題。多車(chē)場(chǎng)車(chē)輛路徑問(wèn)題也是車(chē)輛路徑問(wèn)題領(lǐng)域的研究熱點(diǎn)之一。目前多車(chē)場(chǎng)車(chē)輛路徑問(wèn)題中關(guān)于時(shí)間窗的研究,主要都是從客戶角度出發(fā),旨在提高服務(wù)質(zhì)量的同時(shí)保證成本最低。本文所研究的帶車(chē)輛時(shí)間窗的多車(chē)場(chǎng)車(chē)輛路徑問(wèn)題,主要是從企業(yè)成本控制和優(yōu)化資源配置角度出發(fā),在調(diào)配車(chē)輛時(shí)考慮正在運(yùn)行中的車(chē)輛資源,從而將車(chē)輛時(shí)間窗概念引入到車(chē)輛路徑問(wèn)題中,同時(shí)結(jié)合實(shí)踐運(yùn)作中調(diào)度的情況、特點(diǎn),最終建立帶車(chē)輛時(shí)間窗,帶客戶時(shí)間窗的多車(chē)場(chǎng)車(chē)輛路徑的數(shù)學(xué)模型。本文主要做了以下幾個(gè)方面的工作:1.概述本文研究背景、研究意義及創(chuàng)新點(diǎn),總結(jié)國(guó)內(nèi)外相關(guān)問(wèn)題的研究現(xiàn)狀等內(nèi)容。2.對(duì)帶客戶時(shí)間窗的多車(chē)場(chǎng)車(chē)輛路徑問(wèn)題進(jìn)行研究。在闡述基本車(chē)輛路徑問(wèn)題構(gòu)成要素、分類、模型及算法的基礎(chǔ)上,引入多車(chē)場(chǎng)和客戶時(shí)間窗兩類約束條件,對(duì)帶客戶時(shí)間窗的多車(chē)場(chǎng)車(chē)輛路徑的問(wèn)題(MDVRPTW)進(jìn)行描述分析,進(jìn)而對(duì)其模型及常用的求解算法進(jìn)行歸納總結(jié)。3.構(gòu)建帶車(chē)輛時(shí)間窗的多車(chē)場(chǎng)車(chē)輛路徑問(wèn)題的數(shù)學(xué)模型。在詳細(xì)介紹車(chē)輛時(shí)間窗概念的基礎(chǔ)上,對(duì)帶車(chē)輛時(shí)間窗的多車(chē)場(chǎng)車(chē)輛路徑問(wèn)題進(jìn)行描述及定義,最終構(gòu)建帶車(chē)輛時(shí)間窗、帶客戶時(shí)間窗的多車(chē)場(chǎng)車(chē)輛路徑的數(shù)學(xué)模型。4.求解帶車(chē)輛時(shí)間窗的多車(chē)場(chǎng)車(chē)輛路徑問(wèn)題的算法研究。在詳細(xì)介紹聚類分析、模擬退火算法理論的基礎(chǔ)上,基于“先聚類后路線”的方法,首先通過(guò)k-means聚類算法對(duì)配送區(qū)域進(jìn)行劃分,將多車(chē)場(chǎng)車(chē)輛路徑問(wèn)題轉(zhuǎn)化為單車(chē)場(chǎng)車(chē)輛路徑問(wèn)題,再利用模擬退火算法求解單車(chē)場(chǎng)問(wèn)題,并進(jìn)行算例分析。
[Abstract]:With the rapid development of social economy, many logistics companies often have multiple distribution centers. How to carry out reasonable task assignment and proper vehicle route arrangement among distribution centers in order to reduce the cost of distribution and improve the quality of service has become an issue of great concern to enterprises. Multi-depot vehicle routing problem is also one of the hotspots in the field of vehicle routing problem. At present, the research on time window is mainly from the point of view of customers, aiming at improving the quality of service and ensuring the lowest cost. In this paper, the vehicle routing problem with vehicle time window is studied, mainly from the point of view of enterprise cost control and optimization of resource allocation, considering the running vehicle resources in the allocation of vehicles. Therefore, the concept of vehicle time window is introduced into the vehicle routing problem. At the same time, the mathematical model of multiple vehicle paths with vehicle time window and customer time window is established according to the characteristics of scheduling in practice. This article mainly does the following several aspects of work: 1. This paper summarizes the research background, research significance and innovation, summarizes the domestic and foreign research on related issues. 2. 2. This paper studies the vehicle routing problem of multiple depots with customer time windows. On the basis of expounding the basic vehicle routing problem's constituent elements, classification, model and algorithm, this paper introduces two kinds of constraint conditions, multi-yard and customer time window, to describe and analyze the problem of multi-vehicle path with customer time window (MDVRPTW). Then the model and common algorithms are summarized. 3. The mathematical model of vehicle routing problem with vehicle time window is constructed. On the basis of introducing the concept of vehicle time window in detail, the vehicle routing problem with vehicle time window is described and defined. Finally, the mathematical model of vehicle path with vehicle time window and customer time window is constructed. The algorithm of solving vehicle routing problem with vehicle time window is studied. On the basis of introducing the theory of clustering analysis and simulated annealing algorithm in detail, and based on the method of "clustering first and then route", the distribution area is divided by k-means clustering algorithm. The multi-depot vehicle routing problem is transformed into the single-yard vehicle routing problem, and the simulated annealing algorithm is used to solve the single vehicle parking problem, and an example is given.
【學(xué)位授予單位】：重慶工商大學(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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本文編號(hào)：2110593