外文文献译文-基于互联网的连锁企业的物流管理系统(共10页).doc

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1、精选优质文档-倾情为你奉上外文文献译文资料来源：基于互联网的连锁企业的物流管理系统作者：N.Prindezis，C.T.Kiranoudis1 引言连锁企业是现在和将来适合中小型公司规模的商业模式。显然，在这个领域分组活通过一家成功的商标责任公司朝着有利于改善中心目标市场的渗透。几个合作模式，主要包括引入特许经营作为这一过程的一部分。当这样的一个网络的引入是为了利用商业理念和商业计划和市场渗透率的增长管理的几个问题方面出现的整个网络的运作随后扩大。这样的网络是组织和更集中的方式对供应链和物流几个普通的操作评价的理想场所。事实上，工具开发的组织管理过程和各个公司的业务需求，可以在一个更集中的方式

2、开发工具提供的服务可以提供给每个网络成员促进跨行动和解决业务同样。基于Web的应用是一个理想的起点为开发此类应用的地方。这样的系统通常作为分配公共服务在物流领域中的一个中心仓库。商业应用存储在中央服务器和服务提供给每个组的成员。一个这样的服务器原型描述了以前的工作。本文介绍了完成网络系统是安装在雅典中央食品市场中心的Web服务器，与690公司，包括一个独特的物流和零售连锁企业配送一体化问题研究。每个公司的需求正在排队和算法开发的范围内统一的网络环境描述。每一个公司提供解决问题和服务是一个涉及通过混合车队的卡车货物配送。提供了新的见解的启发式。一个特征的案例研究，提出了通过详细的道路网络雅典的说

3、明所提出的方法的一个现实世界的分布问题的有效性。2 通过异构车队分布本文提出的车队管理问题需要采用异构车队的车辆，将货物通过网络客户使用。因此，系统的设计是为了自动生成车辆路线的车辆应肝哪些客户订单合理采用定量的空间和非空间信息，同时最大限度地减少车辆的成本和总距离受到以下限制车辆行驶：l 每辆车都有从其他车辆组成的车队的异质性，通常不同的预定的负载能力。l 车辆不能超载。l 一个单一的车辆提供每个客户的需求所使用的车辆的数量是预定的。 该问题具有明显的商业价值，已引起或社会的关注。它的巨大成功可以归因于一个事实，从实践和理论的观点，这是一个非常有趣的问题。对于实际的观点所涉及的分布问题，在配

4、电生产管理经济的路线，有助于分销成本，同时提供显着节省燃料成本驱动所有相关费用资本减薪运作层面的效率确实起着核心的作用。在实践层面的重要性动机强烈的理论工作和高效率的算法。 通过学术研究人员和专业团体或/ MS的问题，导致了大量的关于一些求解车辆路径问题的信息系统开发的论文VRIS。讨论的问题是一个NP-难的优化问题，即全局最优解的问题，只能通过对问题规模指数的时间或空间复杂度的算法显示。这种类型的问题的启发式或遇到启发式技术。为车队管理问题，对启发式算法的开发研究具有自认为是在early60s首次提出的算法取得了长足的进步。其中，禁忌搜索是冠军。最强大的禁忌搜索算法是现在能够解决中等尺寸和大

5、尺寸的情况下，非常小，甚至在计算环境的负荷和时间。在算法方面的时间大概要集中精力发展的更快更简单的参数少、更强大的算法即使这会导致质量的解决方案，一个小的损失。如果一个算法是在一个商业软件包实现这些属性是必不可少的。在系统开发的算法搜索的性质。如前所述，由于算法不能保证全局最优解，揭示了一个算法是留给提出解决问题的时间是非常重要的问题。当然有时间预期的解决方案和质量感应之间的一种折衷。这一部分是执行一个简单的方法。如果系统是由用户要求产生一个非常高的质量就那么积极的策略是实施的解决方案。如果用户放松时间的解决方案得到的就是说如果算法是左对解空间的搜索更有效地将有更加详细的算法空间。 该算法采用

6、了两个不同的部分。第一个是一个广义的路线构造算法生成的质量很好，被后面的阶段改进路线。建设算法考虑到了车队的不同性质和特点的用户可以根据自己的日常需要使用或租用车辆拥有自己的欲望的欲望。广义路径构建算法是一个两阶段算法在未焙烧的客户插入已建成的部分解决方案。部分解决方案的设置最初是空的，在这种情况下，插入种子路线，只包含仓库。竞争对手节点插入然后检查。所有路线涉及单未布线的客户。插入过程采用两个标准的c1iuj和c2iuj插入一个新的客户，你的两个相邻的客户我和J的电流路径之间的部分。第一个标准找到最好的可行的插入点我最小的克拉克和赖特插入一个节点在这个特定的插入点节能计算。第二阶段是实际确定

7、最佳节点插入到相邻的节点对之间的（i，j）在第一阶段发现的所罗门1987。从所有对手的节点的一个选择是一个最大的表达。C2(i,u,j)=d(0,u)+d(u,0)-C1(I,u,j) 其中0代表站节点。表达式选择行驶距离是直接从/到仓库到/从客户和额外的距离表示的第一准则。在所有的建设算法的第一阶段要求在所有可能的路径的种子最好的插入点，当这是检测到相应的节点插入。如果没有找到可行的节点的一种新的含有单段插入种子路线。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他的车队车辆利用用户的愿望指导。也就是说车辆进行排序按照分配和调度运用的需要。车辆首先要使用关于用户的成本和

8、车辆方面的情况会在别人面前，对用户的重要性较低加载。通常所有用户访问表示希望利用更大吨位的车辆，而不是较低吨位，装载车辆降序秩序的能力。对禁忌搜索启发式算法满足后续实施积极的一部分。该算法在这种应用中的基本成分是邻域定义短期记忆和期望的标准。2.1 邻里邻域的定义是最有利的本地搜索动作，变换另一种解决办法融合。特别是在其禁忌搜索迭代的移动形式是随机决定的。一个预定义的概率水平分配给每个移动类型。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。这次行动的概率水平分配一个值50。随后，此举意味着选择计算最好的邻居。2.2 短时记忆短期记忆称为禁忌列表是最常用的标签搜索组件。禁忌列

9、表来限制搜索的解决方案被认为是以前从重新并阻止搜索过程的解决方案的子集之间的循环。为实现这一目标属性的动作更准确地原有的逆转被存储在一个禁忌名单。包含反转动作属性禁忌列表存储在指定的禁忌，他们被排除在搜索过程。关于禁忌搜索变种实施这些属性是在移动过程中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，在这些节点属于相应的路线的节点。迭代，弧的流动性限制的数量被称为禁忌列表的大小或禁忌任期。禁忌列表的管理是通过消除移动已禁忌名单上最长的。2.3 吸入标准标准的愿望是压倒一切的短期记忆功能的策略。禁忌搜索方法的实现使用标准的期望标准：如果一个移动给出了更高质量的解决方案，比最好的发现到目前为止将

10、无论是其禁忌状态选择。禁忌搜索算法终止时的迭代次数进行大于允许的最大迭代次数。3 发展基于互联网的应用工具Web服务提供的商业景观的新机会促进全球市场在业务快速推出创新的产品和更好的服务客户。无论是企业需要的是Web服务可以灵活地满足需求，并允许加速外包。使开发人员可以专注于构建核心竞争力，创造客户和股东价值。应用程序的开发也更有效，因为现有的Web服务，无论他们在那里开发可以很容易地重用。许多Web服务的技术要求存在的今天，如商业应用的关键任务交易平台的开放标准和安全的一体化和信息产品。然而要使强大和动态应用的行业标准和工具，延长两天的业务能力，业务互操作的集成。为了充分利用Web服务的关键

11、是要了解什么是Web服务，以及如何在市场很可能演变。我们必须投资于今天的平台和应用，使开发人员快速而有效地实现这些好处，以及能够满足特定需求，提高企业的生产力。通常有两种基本的技术实施处理时，基于互联网的应用，即基于服务器和客户端的基础。这两种技术都有自己的长处对代码的发展和它们所提供的设施。基于服务器的应用程序需要动态创建的网页开发。这些页面传送给客户端的浏览器和包含代码的HTML和JavaScript语言形式。HTML是包含用户需求和JavaScript的部分页面的动态部分窗体和控件的网页的静态部分。通常，代码的结构可以完全改变了通过Web服务器的机制的干预增加传动部分和实现的基于服务器的

12、语言如ASP，JSP PHP等来发展综合动态页面的应用程序在用户的愿望有关问题的特点，计算最短路径路由算法与数据库执行交易的等。适当的调用等页面的动态内容的不同部分的实现。在基于服务器的应用程序的所有计算都在服务器上执行的。在应用程序的Java小应用程序客户端为准。用户的通信是由著名的JAVA的机制，作为用户的代码之间的中保。所有的任务都是在客户端执行。在这种情况下每次药检所一次数据，这可能是费时的交易的原因之一。在应用服务器的资源服务器是所有计算中，这需要强大的服务器设施有关的硬件和软件。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。有救济，即缓存。一旦加载，他们留在Web浏览器的

13、快取档案将在需要时立即召回。在我们的情况下，开发一个基于客户端的应用。主要的原因是从用户的角度来看，需求的个人数据的自由裁量权，他们的客户。事实上，这个信息是保密，甚至在我们的系统所涉及的服务器端。外文文献原文Material source：An internet-based logistics management system forenterprise chainsAuthor：N.Prindezis,C.T.Kiranoudis1 IntroductionEnterprise chains are the business model of the present and future

14、 regarding markets that involve small and medium company sizes. Clearly，grouping activities towards a focused target facilitates an understandably improved market penetration guaranteed by a successful trade mark of a leading company in the field. Several collaboration models that basically include

15、franchising are introduced as a part of this integrated process. When such a network is introduced in order to exploit a commercial idea or business initiative and subsequently expanded as market penetration grows several management issues arise regarding the operations of the entire network. Such a

16、 network is the ideal place for organizing and evaluating in a more centralized way several ordinary operations regarding supply chain and logistics. Infact，tools developed for organizing management processes and operational needs of each individual company can be developed in a more centralized fas

17、hion and the services provided by the tool can be offered to each network member to facilitate trans actions and tackle operations similarly. Web-based applications are an ideal starting place for developing such applications. Typically such systems serve as a central depot for distributing common s

18、ervices in the field of logistics. The commercial application is stored in a central server and services are provided for each member of the group. A prototype of such a server is described in a previous work. This paper presents the completed internet system that is installed in the central web ser

19、ver of the Athens Central Food Market that deals with the integrated problem of distribution for 690 companies that comprise a unique logistics and retail chain of enterprises. The needs of each company are under lined and the algorithms developed are described within the unified internet environmen

20、t. The problem solved and services provided for each company is the one involving distribution of goods through a heterogeneous fleet of trucks. New insights of the met heuristics employed are provided. A characteristic case study is presented to illustrate the effectiveness of the proposed approach

21、 for a real-world problem of distribution through the detailed road network of Athens.2 Distribution through heterogeneous vehicle fleetsThe fleet management problem presented in this paper requires the use of aheterogeneous fleet of vehicles that distribute goods through a network of clients. There

22、fore the system was designed in order to automatically generate vehicle routes which vehicles should de-liver to which customers and in which order using rational quantitative spatial and non-spatial information and minimizing simultaneously the vehicle cost and the total distance travelled by the v

23、ehicles subject to the following constraints: lEach vehicle has a predetermined load capacity typically different from all other vehicles comprising the fleet heterogeneous nature.lThe capacity of a vehicle cannot be exceeded.lA single vehicle supplies each customers demand the number of vehicles us

24、ed is predetermined.The problem has an obvious commercial value and has drawn the attention of OR community. Its great success can be attributed to the fact that it is a very interesting problem both from the practical and theoretical points of view. Regarding the practical point of view the distrib

25、ution problem involved definitely plays a central role in the efficiency of the operational planning level of distribution management producing economical routes that contribute to the reduction of distribution costs offering simultaneously significant savings in all related expenses capital fuel co

26、sts driver salaries. Its Importance in the practical level motivated intense theoretical work and the development of efficient algorithms.For the problem by academic researchers and professional societies in OR/MS, resulting in a number of papers concerning the development of a number of Vehicle Rou

27、ting Information Systems VRIS for solving the problem. The problem discussed is an NP-hard optimization problem that is to say the global optimum of the problem can only be revealed through an algorithm of exponential time or space complexity with respect to problem size. Problems of this type are d

28、ealt with heuristic or met heuristic techniques. Research on the development of heuristic algorithms for the fleet management problem has made considerable progress since the first algorithms that were proposed in the early60s. Among them, tabu search is the champion. The most powerful tabu search a

29、lgorithms are now capable of solving medium size and even large size instances within extremely small computational environments regarding load and time. On the algorithmic side time has probably come to concentrate on the development of faster simpler with few parameters and more robust algorithms

30、even if this causes a small loss in quality solution. These attributes are essential if an algorithm is to be implemented in a commercial package. The algorithm beyond the system developed is of tabu search nature. As mentioned before since the algorithms cannot reveal the guaranteed global optimum

31、the time that an algorithm is left to propose a solution to the problem is of utmost importance to the problem. Certainly there is a trade-off between time expected for the induction of the solution and its quality. This part was implemented in a straightforward way. If the system is asked by the us

32、er to produce a solution of very high quality instantly then an aggressive strategy is to be implemented. If the user relaxes the time of solution to be obtained that is to say if the algorithm is left to search the solution space more efficiently then there is room for more elaborate algorithms.The

33、 algorithm employed has two distinct parts. The first one is a generalized route construction algorithm that creates routes of very good quality to be improved by the subsequent tabu phase. The construction algorithm takes into account the peculiarities of the heterogeneous nature of fleet and the d

34、esire of the user to use vehicles of his own desire owned or hired according to his daily needs.The Generalized Route Construction Algorithm employed is a two-phase algorithm where unroasted customers are inserted into already constructed partial solutions. The set of partial solutions is initially

35、empty and in this case a seed route is inserted that contains only the depot. Rival nodes to be inserted are then examined.All routes employed involve single unrouted customers. The insertion procedure utilizes two criteria c1iuj and c2iuj to insert a new customer u between two adjacent customers i

36、and j of a current partial route. The first criterion finds the best feasible insertion point i j that minimizes the Clark and Wright saving calculation for inserting a node within this specific insertion point.In this formula the expression k1 stands for the actual cost involved in covering the dis

37、tance between nodes k and l. The Clark and Wright saving calculation introduced in this phase serves as an appropriate strong intensification technique for producing initial constructions of extremely good quality a component of utmost necessity in tabu improvement procedure.The second phase involve

38、s the identification of the actual best node to be inserted between the adjacent node pair(I,j)found in the first phase Solomon 1987. From all rival nodes the one selected is the one that maximizes the expression. C2(i,u,j)=d(0,u)+d(u,0)-C1(I,u,j) Where 0 denotes the depot node. The expression selec

39、ted is the travelling distance directly from/to the depot to/ from the customer and the additional distance expressed by the first criterion. In all the first phase of the construction algorithm seeks for the best insertion point in all possible route seeds and when this is detected the appropriate

40、node is inserted. If no feasible node is found a new seed route containing single depot is inserted.The algorithm iterates until there are no unrouted nodes. It must be stretched that the way routes are filled up with customers is guided by the desire of the user regarding the utilization of his fle

41、et vehicles. That is to say vehicles are sorted according to the distribution and utilization needs of the dispatcher. Vehicles to be used first regarding to user cost aspects and vehicle availability will be loaded before others that are of lower importance to the user. Typically all users intervie

42、wed expressed the desire for the utilization of greater tonnage vehicles instead of lower tonnage so vehicles for loading were sorted in descending order of capacity.For the subsequent aggressive part of the algorithm a tabu search met heuristic was implemented. The basic components of this algorith

43、m employed in this application are the neighborhood definition the short-term memory and the aspiration criterion.2.1 NeighborhoodThe neighborhood is defined as a blend of the most favorable local search moves that transforms one solution to another. In particular in its tabu search iteration the ty

44、pe of move adopted is decided stochastically. A predefined probability level is assigned to each move type. After that it is decided whether the move operation is performed within a single route or between different routes once more stochastically. This time for both operations the probability level

45、 is assigned a value of 50.Subsequently the best neighbor that the selected move implies is computed. The move types employed are the 2-Opt move Bell et al. 1983 the 11 Exchange move Evans amp Nor back 1985 the10 Exchange move Evans amp Nor back 1985 on both single route and different routes.2.2 Sho

46、rt-term memoryShort-term memory known as tabu list is the most often used component of tab search. Tabu list is imposed to restrict the search from revisiting solutions that were considered previously and to discourage the search process from cycling between subsets of solutions. For achieving this

47、goal attributes of moves more precisely the reversals of the original ones are stored in a tabu list. The reversal moves that contain attributes stored in tabu list are designated tabu and they are excluded from the search process. Regarding the tabu search variant implemented these attributes are t

48、he nodes involved in the move all the moves used in the this method can be characterized by indicating only two nodes and the corresponding routes where these nodes belong to. The number of iterations that arcs mobility is restricted is known as tabu list size or tabu tenure. The management of the t

49、abu list is achieved by removing the move which has been on the tabu list longest.2.3 Aspiration criterionThe aspiration criterion is a strategy for overriding the short-term memory functions. The tabu search method implemented uses the standard aspiration criterion: if a move gives a higher quality solution than the best found so far then the move is selected regardless its tabu status.Tabu Search algor