物流管理專業(yè)外文文獻(xiàn)翻譯--基于互聯(lián)網(wǎng)的連鎖企業(yè)的物流管理系統(tǒng)

1、An internet-based logistics management system for enterprise chainsN. Prindezis, C.T. KiranoudisSchool of Chemical Engineering, National Technical University, 15780 Athens, GreeceReceived 13 September 2003; received in r

2、evised form 20 December 2003; accepted 27 January 2004Available online 10 December 2004AbstractThis paper presents an Internet-Based Logistics Management System to coordinate and disseminate tasks and related information

3、 for solving the heterogeneous vehicle routing problem using appropriate metaheuristic techniques, for use in enterprise chain net works. Its architecture involves a JAVA Web applet equipped with interactive communicati

4、on capabilities between peripheral software tools. The system was developed in distributed software fashion technology for all computer platforms utilizing a Webbrowser, focusing on the detailed road network of Athens an

5、d the needs of the Athens Central Food Market enterprises. 2004 Elsevier Ltd. All rights reserved.Keywords: Decision support system; e-Logistics; Transportation; Vehicle routing problem1. IntroductionEnterprise chains a

6、re the business model of the present and future regarding markets that involve small and medium company sizes. Clearly, grouping activities towards a focused target facilitates an understandably improved market penetra

7、tion guaranteed by a successful trade mark of a leading company in the field. Several collaboration models that basically include franchising are introduced as a part of this integrated process. When such a network is i

8、ntroduced 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 netwo

9、rk 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

10、needs of each individual company, can be developed in a more centralized fashion and the services provided by the tool can be offered to each network member to facilitate transactions and tackle operations similarly. W

11、eb-based applications are an ideal starting place for developing such applications. Typically such systems serve as a central depot for with respect to problem size. Problems of this type are dealt with heuristic or met

12、aheuristic techniques. Research on the development of heuristic algorithms (Tarantilis & Kiranoudis, 2001,2002a, 2002b) for the fleet management problem has made considerable progress since the first algorithms tha

13、t were proposed in the early 60s. Among them, tabu search is the champion (Laporte, Gendreau, Potvin, & Semet,2000). The most powerful tabu search algorithmsare now capable of solving medium size and even largesize

14、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 r

15、obust algorithms, 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 sea

16、rch nature. As mentioned before, since the algorithms cannot reveal the guaranteed global optimum, the time that an algorithm is left to propose a solution to the problem is of utmost importance to the problem. Certainl

17、y, 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 user to produce a solution of very high q

18、uality 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 effciently, then the

19、re is room for more elaborate algorithms.The 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 subsequen

20、t tabu phase. The construction algorithm takes into account the peculiarities of the heterogeneous nature of fleet and the desire of the user to use vehicles of his own desire, owned or hired, according to his daily nee

21、ds.The Generalized Route Construction Algorithm employed, is a two-phase algorithm where unrouted customers are inserted into already constructed partial solutions. The set of partial solutions is initially empty, and in

22、 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 c1(i,u,

23、j) and c2(i,u,j) to insert a new customer u between two adjacent customers i and j of a current partial route. The first criterion finds the best feasible insertion point (i *,j *) that minimizes the Clark and Wright

24、saving calculation for inserting a node within this specific insertion point,C1(i,u,j)=d(I,u)+d(u,j)-d(I,j) (1) In this formula, the expression d(k,l) stands for the actual cost involved in covering the distance betw

25、een 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

26、 utmostnecessity in tabu improvement procedure. The second phase involves the identification of the actual best node to be inserted between the adjacent nodepair (i* ,j *) found in the first phase (Solomon, 1987). From