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1、论文题目：基于互联网的连锁企业的物流管理系统原文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 revised form 20 December 2003; accepted27 January 2004Avai

2、lable online 10 December 2004Abstract: This paper presents an Internet-Based Logistics Management System to coordinate and disseminate tasks and related information for solving the heterogeneous vehicle routing problem using appropriate metaheuristic techniques, for use in enterprise chain net works

3、. Its architecture involves a JAVA Web applet equipped with interactive communication 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

4、 and 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 are the business model of the present and future regarding markets that involv

5、e small and medium company sizes. Clearly, grouping activities towards a focused target facilitates an understandablyimproved market penetration guaranteed by a successfutrade mark of a leading company in the field. Several collaboration models that basically include franchising are introduced as a

6、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 managementissues arise regarding the operations of the entire network. Such a network is the ideal place for

7、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 fashion and the services provided

8、by the tool can be off ered to each network member to facilitatetransactions 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 services in the field of logistic

9、s. The commercial application is stored in a central serverand services are provided for each member of the group. A prototype of such a server is described in a previous work (Prindezis, Kiranoudis, & Marinos-Kouris,2003).This paper presentsthe completed inter net system that is installed in the ce

10、ntral web server of the Athens Central Food Market that deals with the integratedproblem of distribution for 690 companies that comprise a unique logistics and retail chain of enterprises. The needs of each company are underlined and the algorithms developed are described within the unified internet

11、 environment. The problem solved and servicesprovided for eachcompany is the oneinvolving distribution of goods through a heterogeneousfleet of trucks. New insights of the metaheuristics employed are provided. A characteristic case study is presented to 川ustrate the fe ectiveness of the proposed app

12、roach 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 a heterogeneous fleet of vehicles that distribute goods through a network of clients

13、(Tarantilis, Kiranoudis, & Vassiliadis, 2003, 2004).Therefore, 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 simultaneo

14、usly the vehicle cost and the total distance travelled by the vehicles, subjectto the following constraints:each vehicle has a predetermined load capacity, typically diff erent from all other vehicles comprising the fleet (heterogeneous nature),the capacity of a vehicle cannot be exceeded, a single

15、vehicle supplies each customers demand, the number of vehicles used is predetermined.The problem has an obvious commercial value andh as 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

16、points of view. Regarding the practical point of view, the distribution 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, off ering simul

17、taneously significant savings in all related expenses (capital, fuel costs, driver salaries). Its Importance in the practical level, motivated in tense theoretical work and the development of efficient algorithms.For the problem by academicresearchersand professional societies in OR/MS, resut ing in

18、 a number of papersconcerning the development of a number of Vehicle Routing 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

19、 space complexity with respect to problem size. Problems of this type are dealt with heuristic or metaheuristic 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

20、 algorithmsthat 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 instances within extremely small computational environments regarding l

21、oad and time. On the algorithmic side, time has probably come to concentrate on thedevelopment of faster, simpler (with few parameters) and more robust algorithms, even if this causesa small loss in quality solution. These attributes are essential if an algorithm is to be implemented in a commercial

22、 package.The algorithm beyond the system developed is of tabu search 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. Certainly, there is a tr

23、ade-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 quality instantly, then an aggressive strategy is to be implemented. If the user relaxes the time

24、 of solution to be obtained, that is to say if the algorithm is left to search the solution space more eff ciently, then there 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

25、good quality to be improved by the subsequent tabu phase. The construction algorithm takes into account the peculiarities of the heterogeneousnature of fleet and the desire of the user to use vehicles of his own desire, owned or hired, according tohis daily needs.The Generalized Route Construction A

26、lgorithm 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 this case a seed route is inserted that containsonly the depot. Rival nodes to be inserted are thenexamined.All routes

27、 employed involve single unrouted customers. The insertion procedure utilizes two criteria c1(i,u,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 Clar

28、k and Wright 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 betweennodes k and l. The Clark and Wright saving calculation introduced i

29、n this phase serves as an appropriate strongintensification technique for producing initial constructions of extremely good quality, a component of utmost necessity in tabu improvement procedure.The second phase involves the identification of the actual best node to be inserted between the adjacent

30、nodepair (i ,j ) found in the first phase (Solomon, 1987). From all rival nodes, the one selected is the one thatnaximizes the expressionC2 (i*, u,j *)=d(0,u)+d(u,0)- C1(i*, u, j *) (2)where 0 denotes the depot node. The expression selected is the travelling distance directly from/to the depot to/ f

31、rom the customer and the additional distance expressedby 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 appropriatenodeis inserted. If no feasible node is found, a new seed route

32、, containing a 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 t he utilization of his fleet vehicles. That is to say, vehicles are sorted according

33、 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 tothe user. Typically, all users interviewed expressed the desire for the utilization of greater

34、 tonnage vehicles instead of lower tonnage, so vehicles for loading weresorted in descending order of capacity.For the subsequent aggressive part of the algorithm a tabu search metaheuristic was implemented. The basic components of this algorithm employed in this application are the neighbourhood de

35、finition, the short-term memory and the aspiration criterion.2.1. NeighbourhoodThe neighbourhood 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 type of move adopted is decided stochastically. A p

36、redefined 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 dff erent routes, once more stochastically. This time, for both operations, the probability level is assigned a value of 50%. Subsequently, th

37、e best neighbour 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& Norback , 1985), thel -0 Exchangemove (Evans & Norback, 1985), on both single route and different routes.2.2. Short-term memoryShort-term memory,

38、known as tabu list, is the most often used component of tabu 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 goal, attributes of moves, mor

39、e 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 the nodes involved in the mo

40、ve (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 ofiterations that arcs mobilitys restricted is known as tabu list size or tabu tenure. Themanagement of the tabu list is achieved by remov

41、ing 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 foun

42、d so far, then the move is selected regardlessits tabu status.Tabu Search algorithm terminates when the number of iterations conducted is larger than the maximum number of iterations allowed.3. Developing the internet-based application toolWeb services of er new opportunities in businesslandscape, f

43、acilitating a global marketplace where business rapidly create innovative products and serve customers better. Whatever that business needs isWeb services have the flexibility to meet the demand and allow to accelerate outsourcing. In turn, the developer can focus on building core competencies to cr

44、eate customer and shareholder value. Application developmentis also more efficient becauseexisting Web services,regardless of where they were developed, can easily bereused.Many of the technology requirements for Web services exist today, such as open standards for businessto-business applications,

45、mission-critical transaction platforms and secure integration and messaging products. However, to enable robust and dynamic integration of applications, the industry standards and tools that extend the capabilities of to days business-to-business interoperability are required. The key to taking full

46、 advantageof Web services is to understand what Web services are and how the market is likely to evolve. One needs to be able to invest in platforms and applications today that will enable the developer to quickly and eff ectively realize these benefits as well as to be able to meet the specific nee

47、ds and increase business productivity.Typically, therearetwo basictechnologiesto be implemented when dealing with internet-based applications; namely server-based and client-based. Both technologies have their strong points regarding development of the code and the facilities they provide. Server-ba

48、sedapplications involve the development of dynamically created web pages. These pages are transmitted to the webbrowser of the client and contain code in the form of HTML and JAVASCRIPT language. The HTML part is the static part of the page that contains forms andcontrols for user needs and the JAVA

49、SCRIPT part is the dynamic part of the page. Typically, the structure of the code can be completely changed through the intervention of web server mechanisms added on the transmission part and implemented by server-based languages such as ASP, JSP, PHP, etc. This comes to the development of an integ

50、rated dynamic page applicationwhere user desire regarding problem peculiarities (calculating shortest paths, execute routing algorithms, transact with the database, etc.) is implemented byappropriately invoking diff erent parts of the dynamic content of such pages. In server-based applications all c

51、alculations are executed on the server. In client-based applications, JAVA applets prevail. Communication of the user is guaranteed by the well-known JAVA mechanism that acts asthe medium between the user and code.Everything is executed on the client side. Data in this case have to be retrieved, onc

52、e and this might be thetime-consuming part of the transaction.In server-basedapplications, server resourcesare used for all calculations and this requires powerful server facilities with respect to hardware and software. Client-based applications are burdened with datatransmission (chiefly related t

53、o road network data). There is a remedy to that; namely caching. Once loaded,they are left in the cache archives of the web browser tobe instantly recalled when needed.In our case, a client-based application was developed.The main reason was the demand from the users point of view for personal data

54、discretion regarding their clients. In fact, this information was kept secret in our system even from the server side involved.Data management plays major role in the good function of our system. This role becomes more substantiawhen the distribution takes place within a large anddetailed road netwo

55、rk like this of a major complex city. More specifically, in order to produce the proposed the routing plan, the system uses informationabout:the locations of the depot and the customers within the road network of the city (their co-ordinates attached in the map of the city), the demand of the custom

56、ers serviced, the capacity of the vehicles used, the spatial characteristicsof road segments of the net work examined, the topography of the road network, the speed of the vehicle, considering the spatial characteristics of the road and the area within of which is moved,the synthesis of the company

57、fleet of vehicles.Consequently, the system combines, in real time, the available spatial characteristics with all other information mentioned above, and tools for modelling, spatial, non-spatial, and statistical analysis, image processing forming a scalable, extensible and interoperable application

58、environment.The validation and verification of addressesof customers ensure the accurate estimation of travel times and distances travelled. In the case of boundary in thetotal route duration, underestimates of travel time may lead to failure of the programmed routing plan whereas overestimates can

59、lower the utilization of drivers and vehicles, and create unproductive wait times as well (Assad, 1991). The data corresponding to the area of interest involved two diff erent details. A more detailed network, appropriately for geocoding (approximately 250,000 links) and a less detailed for routing

60、(about 10,000 links). The two networks overlapped exactly. The tool that provides solutions to problems of ef ectively determining the shortest path, expressed in terms of travel time or distance travelled, within a specific road network, using the Dijkstra algorithm(Winston,1993). In particular, th

61、e Dijkstra s algorithm is used in two cases during the process of developing the routing plan. In the first case, it calculates the travel times between all possible pairs of depot and customers so that the optimizer would generate thevehicle routes connecting them and in the second case it determin

62、es the shortest path between two involved nodes (depot or customer) in the routing plan, as this was determined by the algorithm previously. Due to the fact, that U-turn and left-,right-turn restrictions were taken into consideration for network junctions, an arc-based variant of the algorithm was t

63、aken into consideration (Jiang, Han, & Chen, 2002).The system uses the optimization algorithms mentioned in the following part in order to automatically generate the set of vehicle routes (which vehicles should deliver to which customers and in which order) minimizing simultaneously the vehicle cost

64、s and the total distance travelled by the vehicles This processinvolves activities that tend to be more strategic and less structured than operational procedures. The system helps planners and managers to view information in new way and examine issues such as:the average cost per vehicle, and route,

65、the vehicle and capacity utilization, the service level and cost, the modification of the existing routing scenario by adding or subtracting customers.In order to support the above activities, the interface of the proposed system provides a variety of analyzed geographic and tabulated data capabilit

66、ies. Moreover, the system can graphically represent each vehicle routeseparately, cutting it o? from the final routing plan and off ering the user the capability for perceiving the road network and the locations of depot and customers withall details.4. Case studyThe system developed was used in the Central FoodMarket of Athens, Greece. The specific Market involves 2 an area of