关于车辆调度外文文献翻译(中英)

1、 关于车辆调度外文文献翻译（中英）目 录1 外文文献译文 2 2 外文文献原文 91基于互联网的连锁企业的物流管理系统N. Prindezis，C.T. Kiranoudis化工学院，国立技术大学，15780 雅典，希腊收到 2003 年 9 月 13 日，在经修订的形式收到的 2003 年 12 月 20 号，接受2004 年1月27日允许上网 二四年十二月十日1 前言连锁企业经营模式是目前和未来相关市场的一种商业模式，涵盖了中小型规模的企业。很明显,对聚焦的对象分组活动提高市场占有率，促进一个可以理解一个成功的商标，从而保证其在这个领域保持一流的水平。若干个合作模式，介绍了主要包括特许经营

2、作为这个综合的过程一部分。当这种网络的引入，以利用商业理念或企业的倡议，并随后扩大市场渗透率的增长，若干管理问题方面出现在整个网络的运作上面。这种网络是组织和较集中的方式对一些普通业务供应链和物流从评估的理想场所、工具、组织管理流程及个别公司的业务需要而进行开发而成，可以以 更集中的方式发展，并且该工具可以提供给每个网络成员促进交易和处理业务相类似的服务。基于 Web 的应用是为开发此类应用一个理想的起点。通常这些系统分配在物流领域提供共同服务的中心仓库，如同商业应用存储在中央服务器和服务的每个组的成员提供。这样一个服务器的原型是描述在以前的工作(Prindezis，Kiranoudis，库里

3、斯，2003 年)。本文展示了一个全面的互联网系统，并在雅典中央副食品批发市场中央 Web 服务器上安装的处理分配 690 公司，包括一个独特的物流和零售连锁企业的整合问题。每个公司的需要是强调和算法开发的范围内，进行统一的网络环境描述。每一个公司解决问题和服务提供的是一个涉及通过混合车队的卡车货物分配，新的启发式解法为就业提供新的见解。通过特征案例的研究，提出说明，并进一步通过一个详细的雅典道路网络的现实分布问题，提出方法的有效性。22 通过异构车队配送该车队管理问题本文提出通过车辆混合车队的使用来分配网络及其客户货物(Tarantilis，Kiranoudis，及阿迪斯，2003，2004

4、)。因此，该系统是为了实现设计，自动生成车辆路线(即车辆应提供哪些客户的顺序)，使用合理，数量，空间和非空间信息和车辆，同时尽量减少成本和总距离行驶的车辆须符合下列限制: , 每辆车有一个预先确定的负载能力，通常不同于所有其它车辆(异质性)组成的舰队。, 不能超过车辆的运载能力。, 单一的汽车用品满足每一个客户的需求。, 预先确定所用车辆的数目。这个问题具有明显的商业价值，已经引起了 OR(运筹学)组织的关注。它的巨大成功可以归功于以下事实。这是一个无论从实用的角度，还是从理论角度来说都是一个很有趣的问题。从实际角度来看，问题所涉及的分布起着一定的分配管理的业 务水平，提高效率的中央计划的作用

5、，产生经济的路线，有助于降低分销成本，同时提供显着的一切有关费用节省(资本，燃料费用，司机工资)。它最重要在实践上，在紧张的理论工作动机和高效率的算法。对于这一问题,学术研究人员和专业协会 OR/ MS，为解决这个问题，引起了许多许多学者发表了关于发展有一定数量的车辆路径信息系统(VRIS)的论文。讨论的问题是一个 NP-hard 优化问题，即在全球范围内优化的问题，只能通过对指数的时间和空间复杂度算法透露有关问题的大小。并运用启发式或启发式技术对付这种类型的问题。因为车队管理问题对启发式算法的开发研究(Tarantilis,Kiranoudis，2001,20022002 年)取得了自认为与

6、在 60 年代初首次提出的算法展现了相当大的进展。其中，禁忌搜索是最重大的成就(拉波特，根德罗，普托文，及 Semet，2000)。目前，最强大的搜索演算法是解决中等规模，甚至大型工程在时间方面的负荷，在极少数情况下，能够计算环境。在算法方面，时间很可能用来要集中精力发展快、简单(与发展的几个参数)和更强大的算法，即使这在品质的解决方案中产生小的损失。如果一个算法是在一个商业软件包实施，这些属性必不可少。3发达国家以外的系统算法是搜索的性质。如前所述，由于算法可以保证没有透露全局最优的一个算法，留给提出解决问题的时间是一个非常重要的问题。当然，有一个与贸易之间的休息时间的解决方案，并诱导其质量

7、的预期。这部分是执行一个简单的方法。如果系统询问用户生产出高品质的解决方案，即刻，那么积极战略的实施程序。如果用户放松时间的解决方案要获得，也就是说，如果该算法留下来搜索解的空间更高效，那么就有更加详细的算法空间。该算法采用了两个不同的部分。第一个是广义的路线构造算法创建质量很好的路线，后面的阶段进行改善。建造算法考虑到了车队的不同性质和特点的用户，根据他的日常需要，希望利用自己的意愿拥有或租用的车辆。 广义算法就是一个两阶段算法在未布线客户到已建成的部分解决方案插入。部分解决方案的设置最初是保持空，在这种情况下，插入种子路线只包含一个节点。竞争对手节点需要插入，然后检查。所有路线就业涉及单未

8、布线的客户。插入过程采用两个标准的 C1(i，u，j)和 C2(i，u，j)之间插入两个相邻的客户提供了新的客户用户接口和当前部分路线?。第一个标准，找到最佳可行插入点(i*，j*)最小化的在这个特定的插入点插入一个节点的计算，(1)C1(i,u,j)=d(I,u)+d(u,j)-d(I,j)在这个公式中，D 表达式(k，1)代表在覆盖节点之间的距离，k 和 1 所涉及的实际成本。Clark 和 Wright 算法引入这一阶段作为一个适当的强大技术建设集约化生产的极好的质量初始,一个组件最大限度的必要性在禁忌改进过程。第二阶段是实际确定最佳节点之间必须相邻节点插入(i*，j *)在第一阶段(所

9、罗门，1987 年)发现。从所有竞争对手节点，选择一个最大化的表达(2)C2 (i*, u, j *)=d(0,u)+d(u,0)- C1(i*, u, j *)其中 0 表示车厂节点。表达式选择行驶距离是直接从/到站到/从客户和额外的距离表示。由第一个标准，总之，建筑算法的第一阶段要求在所有可能的途径种子最佳的插入点，当这是检测到相应的节点插入。如果没有找到可行的节点，一个新的种子路线，其中包含一个节点，然后插入。该算法迭代，直到没有未布线节点。必须延伸路线的方式与客户充满了由有关用户的愿望指导，车队车辆的利用率。这就是说，车辆进行排序按照分配和调度运用的4 需要。在较低的重要性的用户，车辆

10、可先使用(关于用户的成本方面的原因及车辆可用性)即被加载在别人面前,。通常情况下，所有用户访问表示需要更大吨位的车辆，而不是利用低吨位的愿望，因此，装载车辆降序秩序的能力。对于随后的搜索算法的启发式实施积极的一部分。在此应用程序采用这种算法的基本组成部分是附近的定义，短期记忆和期望的标准。2.1 邻里邻里之间被定义为一种混合的最有利的局部搜索,变换另一种解决办法融合。特别是通过其在禁忌搜索迭代的移动类型中随机决定。一个预定义的概率水准分配给每一次移动类型。在此之后，决定是否执行移动操作是在一个单一的路线或不同的路线，再次随机。这一次，这两个行动的概率水平分配 50,的价值。随后，最好的邻居选定

11、，此举意味着选择计算。此举类型采用的是 2 选项的移动(贝尔等人，1983)，交易所将在 1-1(埃文斯与努尔贝克，1985)，当前第 1 页- 0 移动交换(埃文斯与努尔贝克，1985 年)，单路和不同路线。2.2 短期记忆短期记忆,被称为禁忌列表,是禁忌搜索最常用的组成部分。禁忌列表是强加的限制的解决方案，从解决方案的子集之间的循环搜索过程的解决办法。为实现这一目标，属性的动作更准确地原有的的逆转，被存储在一个禁忌列表。而逆转动作，包含属性禁忌列表存储在指定禁忌则在搜索过程之外。关于禁忌搜索变种实施，这些属性是在移动过程中的节点(所有的动作中使用的这种方法可以通过指出只有两个节点的特点)和

12、在这些节点属于相应的路线。这个数字的灵活限制被称为禁忌列表的大小或禁忌的周期。禁忌的列表管理是通过消除已禁忌列表上最长的已移动。2.3 标准意愿标准的愿望的准则是压倒一切的策略的短期记忆功能。使用标准的期望标准实施禁忌搜索方法。如果此举给出了比迄今为止发现的更高质量的解决方案，那么不 论其禁忌的地位，都将被选中。禁忌搜索算法终止条件是迭代次数进行比允许的最大数量。53 发展基于互联网的应用工具Web 服务提供新的商业机会，促进全球市场快速推出创新的产品和为客户提供更优质的服务。无论是企业需要的是什么，Web 服务都可以灵活地满足需求，并允许加速外包。反过来，开发人员可以专注于构建核心竞争力，创

13、造顾客和股东价值。因为现有的 Web 服务，应用程序开发也变得更加有效，无论他们在那里开发，都可以很容易地重用。目前存在对网络服务的技术要求的有很多，如营业标准、业务应用、关键任务交易平台和安全的一体化信息产品。不过，为使强大且动态系统的综合应用，以及行业标准和工具，扩大业务能力，企业的互操作性是必须。采取了充分利用网络服务的关键是要了解什么是 Web 服务，以及如何应对市场可能发生变化。人们需要能够在今天的平台和应用里投资，使开发人员快速而有效地实现这些好处，满足特定需求，提高企业的生产力。一般情况下，互联网的应用程序有两种实施方法，即基于基于客户的服务器和基本技术。这两种技术各有自己的优点

14、。对代码的发展和它们所提供的设施。服务器的应用程序涉及的动态创建的网页的发展。这些网页传送给客户端的网络浏览器和包含在 HTML 和 JavaScript 语言形式的代码，部分的 HTML 的网页，其中包含用户需求和 JavaScript 的部分控制部分都是静态页面的动态部分。通常情况下，代码的结构完全可以通过干预机制的 Web 服务器上的改变而增加。传动部分和服务器实现是基于如 ASP、JSP、PHP 和其它计算机语言等，这涉及到一个综合的动态网页应用程序开发。在基于服务器的所有应用，用户希望有关问题的特殊性(计算最短路径、执行路由算法、处理与数据库、等)，通过适当援引这些网页动态内容不同

15、部分。，在客户端的应用程序 Java 小程序为准，计算在服务器上执行。用户的通信保障由著名的 JAVA 机制充当用户的代码。一切都执行在客户端，数据在这种情况下必须被收回一次，并且这可能是最耗时的部分的交易。在服务器的应用程序，服务器资源是所有计算中，这就要求有关的硬件和软件功能强大的服务器设施。基于客户端的应用程序与数据传输负担(主要是与道路网络数据)。有一个该补救方法，即缓存。一旦加载，他们留在 Web 浏览器的快取档案将在需要时立即召回。6就我们而言，客户端的应用程序的开发。主要的原因是从有关个人资料的客户自行查看用户的用电量。事实上，这些信息是保密的，甚至在我们的系统从服务器涉及的方面

16、。数据管理在我们的制度好职能的主要作用。这种作用变得更为实质性的分配时，需要在一个像一个大的城市大型复杂和详细的道路网进行。更具体地说，为了生产计划提出的路线，系统使用的信息:, 在城市道路网在车厂地点和客户(在城市重视统筹地图)接受服务的客户的需求 , 所用车辆的能力，, 的净工作道路部分的空间特征研究，, 道路网地形，, 车辆的速度，考虑到道路的空间特征和地区范围内的移动，, 该公司车队车辆的合成。因此，该系统结合实时，可用空间特征与上述其他所有信息，以及造型，空间，非空间，和统计分析工具，图像处理形成一个可伸缩，可扩展和可互操作的应用环境。验证和核查，确保客户的地址的旅行时间和旅行距离准

17、确估计。在边界线的总时间的情况，可能导致低估了，而高估了编程路由计划可以降低司机的利用和失败的旅行时间车辆，并建立非生产性等待时间，以及(阿萨德， 1991)。数据对应感兴趣的区域，涉及两个不同的细节。更详细的网络，适当地地理编码(约 250,000 链接)和更少的路由(约 10,000 个链接)详细。两个网络完全重叠。该工具提供了解决方案有效地确定最短路径问题，在旅行时间和旅行距离来计算，在一个特定的道路网络，利用 Dijkstra 算法(温斯顿，1993 年)。特别是，Dijkstra 算法是在两种情况下使用，在发展过程中的路由计划。在第一种情况，它计算之间的车厂和可能对所有客户的旅行时间

18、，以便优化器将生成的车辆路线连接并在第二种情况下它决定了涉及两个节点(仓库或客户中)最短路径路由计划，因为这是确定先前的算法。由于这样的事实，即掉头和左，右转向限制是考虑到网络的路口，一个弧形的算法的变种考虑(江，韩，及陈，2002)。该系统使用的算法在以下部分中提到的优化，以自动生成车辆路线设置(即车辆应提供哪些客户的顺序)，同时最大限度地减少车辆的费用和总距离行驶的车辆，这一过程涉及活动往往更具有战略性和业务程序不到结构。该系统可以帮助规划人员和管理人员，以查看新的方式的信息和研究的问题，例如:, 每辆车的平均成本和路线，7, 车辆和产能利用率，, 服务水平和成本，, 通过增加或减少客户现

19、有的路由方案的修改。为了支持上述活动，拟议的系统接口提供了各种分析和列的地理数据功能。此外，该系统可以图形方式表示每辆汽车的路线分别削减它从最后的路由计划和为用户提供了感知的道路网络，并与所有细节车厂和客户的地点的能力。8外文翻译原文: An internet-based logistics management system for enterprisechainsN. Prindezis, C.T. KiranoudisSchool of Chemical Engineering, National Technical University, 15780Athens, GreeceRece

20、ived 13 September 2003; received in revised form 20 December2003; accepted 27January 2004Available online 10 December 20041.IntroductionEnterprise chains are the business model of the present and futureregarding markets that involve small and medium company sizes. Clearly,grouping activities towards

21、 a focused target facilitates anunderstandably improved market penetration guaranteed by a successfultrade mark of a leading company in the field. Several collaborationmodels that basically include franchising are introduced as a part ofthis integrated process. When such a network is introduced in o

22、rder toexploit a commercial idea or business initiative and subsequentlyexpanded as market penetration grows, several management issues ariseregarding the operations of the entire network. Such a network is theideal place for organizing and evaluating in a more centralized wayseveral ordinary operat

23、ions regarding supply chain and logistics Infact,tools developed for organizing management processes and operationalneeds of each individual company, can be developed in a more centralized fashion and the services provided by the tool can be offered to eachnetwork member to facilitate transactions a

24、nd tackle operationssimilarly. Web-based applications are an ideal starting place fordeveloping such applications. Typically such systems serve as a centraldepot for distributing common services in the field of logistics. Thecommercial application is stored in a central server and9services are provi

25、ded for each member of the group. A prototype ofsuch a server is described in a previous work (Prindezis, Kiranoudis, &Marinos-Kouris,2003).This paper presents the completed internet system that is installedin the central web server of the Athens Central Food Market that dealswith the integrated pro

26、blem of distribution for 690 companies thatcomprise a unique logistics and retail chain of enterprises. The needsof each company are underlined and the algorithms developed aredescribed within the unified internet environment. The problem solvedand services provided for each company is the one invol

27、ving distributionof goods through a heterogeneous fleet of trucks. New insights of themetaheuristics employed are provided. A characteristic case study ispresented to illustrate the effectiveness of the proposed approach for areal-world problem of distribution through the detailed road network ofAth

28、ens.102. Distribution through heterogeneous vehicle fleets The fleet management problem presented in this paper requires theuse of a heterogeneous fleet of vehicles that distribute goods through anetwork of clients (Tarantilis, Kiranoudis, & Vassiliadis, 2003,2004).Therefore, the system was designed

29、 in order to automaticallygenerate vehicle routes (which vehicles should de-liver to which customers and in which order), using rational,quantitative, spatial and non-spatial information and minimizingsimultaneously the vehicle cost and the total distance travelled by thevehicles, subject to the fol

30、lowing constraints:, each vehicle has a predetermined load capacity, typicallydifferent from all other vehiclescomprising the fleet (heterogeneous nature)., the capacity of a vehicle cannot be exceeded., a single vehicle supplies each customers demand., the number of vehicles used is predetermined.T

31、he problem has an obvious commercial value and has drawn theattention of OR community. Its great success can be attributed to thefact that it is a very interesting problem both from the practical andtheoretical points of view. Regarding the practical point of view, thedistribution problem involved d

32、efinitely plays a central role in theefficiency of the operational planning level of distribution management,producing economical routes that contribute to the reduction ofdistribution costs, offering simultaneously significant savings in allrelated expenses (capital, fuel costs, driver salaries). I

33、ts Importance in the practical level, motivated in tense theoretical work and thedevelopment of efficient algorithms.For the problem by academic researchers and professional societiesin OR/MS, resulting in a number of papers concerning the development ofa number of Vehicle Routing Information System

34、s (VRIS) for solving theproblem. The problem discussed is an NP-hard optimization problem, thatis to say the global optimum of the problem can only be revealed throughan algorithm of exponential time or space complexity with respect toproblem size. Problems of this type are dealt with heuristic orme

35、taheuristic techniques. Research on the development of heuristicalgorithms (Tarantilis & Kiranoudis, 2001,2002a, 2002b) for the fleetmanagement problem has made considerable progress since the firstalgorithms that were11proposed in the early 60s. Among them, tabu search is the champion(Laporte, Gend

36、reau, Potvin, & Semet,2000). The most powerful tabu searchalgorithmsare now capable of solving medium size and even largesizeinstances within extremely small computational environments regardingload and time. On the algorithmic side, time has probably come toconcentrate on the development of faster,

37、 simpler (with few parameters)and more robust algorithms, even if this causes a small loss in qualitysolution. These attributes are essential if an algorithm is to beimplemented in a commercial package. The algorithm beyond the system developed is of tabu search nature.As mentioned before, since the

38、 algorithms cannot reveal the guaranteedglobal optimum, the time that an algorithm is left to propose a solutionto the problem is of utmost importance to the problem. Certainly, thereis a trade-off between time expected for the induction of the solutionand its quality. This part was implemented in a

39、 straightforward way. Ifthe system is asked by the user to produce a solution of very highquality instantly, then an aggressive strategy is to be implemented. Ifthe user relaxes the time of solution to be obtained, that is to say ifthe algorithm is left to search the solution space more effciently,

40、thenthere is room for more elaborate algorithms.The algorithm employed has two distinct parts. The first one is ageneralized route construction algorithm that creates routes of verygood quality to be improved by the subsequent tabu phase. Theconstruction algorithm takes into account the peculiaritie

41、s of theheterogeneous nature of fleet and the desire of the user to use vehiclesof his own desire, owned or hired, according to his daily needs.The Generalized Route Construction Algorithm employed, is a two-phase algorithm where unrouted customers are inserted into alreadyconstructed partial soluti

42、ons. The set of partial solutions is initiallyempty, and in this case a seed route is inserted that contains only thedepot. Rival nodes to be inserted are then examined. All routes employed involve single unrouted customers. The insertionprocedure utilizes two criteria c1(i,u,j) and c2(i,u,j) to ins

43、ert a newcustomer u between two adjacent customers i and*j of a current partial route. The first criterion finds the bestfeasible insertion point (i ,j )12that minimizes the Clark and Wright saving calculation for insertinga node within this specific insertion point,(1)C1(i,u,j)=d(I,u)+d(u,j)-d(I,j)

44、In this formula, the expression d(k,l) stands for the actual costinvolved in covering the distance between nodes k and l. The Clark andWright saving calculation introduced in this phase serves as anappropriate strong intensification technique for producing initialconstructions of extremely good qual

45、ity, a component of utmostnecessity in tabu improvement procedure.The second phase involves the identification of the actual best nodeto be inserted*between the adjacent nodepair (i ,j ) found in the first phase(Solomon, 1987). From all rivalnodes, the one selected is the one that maximizes the expr

46、ession*(2)C2 (i, u, j )=d(0,u)+d(u,0)- C1(i, u, j )where 0 denotes the depot node. The expression selected is thetravelling distance directly from/to the depot to/ from the customer andthe additional distance expressed by the first criterion. In all, the first phase of the construction algorithm see

47、ks for the best insertionpoint in all possible route seeds and when this is detected, theappropriate node is inserted. If no feasible node is found, a new seedroute, containing a single depot, is inserted.The algorithm iterates until there are no unrouted nodes. It must bestretched that the way rout

48、es are filled up with customers is guided bythe desire of the user regarding,the utilization of his fleet vehicles.That is to say,vehicles are sorted according to the distribution andutilization needs of the dispatcher. Vehicles to be used first(regarding to user cost aspects and vehicle availabilit

49、y) will be loadedbefore others that are of lower importance to the user. Typically, allusers interviewed expressed the desire for the utilization of greatertonnage vehicles instead of lower tonnage, so vehicles for loading weresorted in descending order of capacity.For the subsequent aggressive part

50、 of the algorithm a tabu searchmetaheuristic was implemented. The basic components of this algorithmemployed in this application are the neighbourhood definition, theshort-term memory and the aspiration criterion. 2.1. Neighbourhood13The neighbourhood is defined as a blend of the most favorable loca

51、lsearch moves that transforms one solution to another. In particular, inits tabu search iteration the type of move adopted is decidedstochastically. A predefined probability level is assigned to each movetype. After that, it is decided whether the move operation is performed within a single route or

52、 between different routes, once morestochastically. This time, for both operations, the probability level isassigned a value of 50%. Subsequently, the best neighbour that theselected move implies is computed. The move types employed are the 2-Optmove (Bell et al., 1983), the 11 Exchange move (Evans&

53、 Norback , 1985),the10 Exchangemove (Evans &Norback, 1985), on both single route and different routes.2.2. Short-term memoryShort-term memory, known as tabu list, is the most often usedcomponent of tabu search. Tabu list is imposed to restrict the searchfrom revisiting solutions that were considered

54、 previously and todiscourage the search process from cycling between subsets of solutions.For achieving this goal, attributes of moves, more precisely thereversals of the original ones, are stored in a tabu list. The reversalmoves that contain attributes stored in tabu list are designated tabuand th

55、ey are excluded from the search process. Regarding the tabu searchvariant implemented, these attributes are the nodes involved in the move(all the moves used in the this method can be characterized byindicating only two nodes) and the corresponding routes where thesenodes belong to. The number ofite

56、rations that arcsmobility isrestricted isknown as tabu list size or tabu tenure. The management of the tabulist is achieved by removing the move which has been on the tabu listlongest. 2.3. Aspiration criterionThe aspiration criterion is a strategy for overriding the short-termmemory functions. The

57、tabu search method implemented, uses the standardaspiration criterion: if a move gives a higher quality solution than thebest found so far, then the move is selected regardless its tabu status.Tabu Search algorithm terminates when the number of iterationsconducted is larger than the maximum number o

58、f iterations allowed.143. Developing the internet-based application toolWeb services offer new opportunities in business landscape,facilitating a global marketplace where business rapidly createinnovative products and serve customers better. Whatever that businessneeds is, Web services have the flex

59、ibility to meet the demand and allowto accelerate outsourcing. In turn, the developer can focus on buildingcore competencies to create customer and shareholder value. Applicationdevelopment is also more efficient because existing Web services,regardless of where they were developed, can easily be re

60、used.Many of the technology requirements for Web services exist today,such as open standards for business to-business applications, mission-critical transaction platforms and secure integration and messagingproducts. However, to enable robust and dynamic integration ofapplications, the industry stan