1、An internet-based logistics management system for enterprise chains N. Prindezis, C.T. Kiranoudis School of Chemical Engineering, National Technical University, 15780 Athens, Greece Received 13 September 2003； received in revised form 20 December 2003； accepted 27 January 2004 Available online 10 De
2、cember 2004 Abstract 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. Its architecture
3、 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 and the needs of
4、the Athens Central Food Market enterprises. 2004 Elsevier Ltd. All rights reserved. Keywords: Decision support system； e-Logistics； Transportation； Vehicle routing problem 1. Introduction Enterprise chains are the business model of the present and future regarding markets that involve small and medi
5、um 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 franchising are introduced as a part of this
6、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 network is the ideal place for organizing a
7、nd 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 by the tool
8、can be offered to each network member to facilitate transactions 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 logistics. The comme
9、rcial 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 (Prindezis, Kiranoudis, & Marinos-Kouris,2003). This paper presents the completed inter net system that is installed in the central web
10、 server 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 underlined and the algorithms developed are described within the unified internet environ
11、ment. 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 metaheuristics employed are provided. A characteristic case study is presented to illustrate the effectiveness of the proposed appro
12、ach for a real-world problem of distribution through the detailed road network of Athens. 2. Distribution through heterogeneous vehicle fleets The 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 simultan
14、eously the vehicle cost and the total distance travelled by the vehicles, subject to the following constraints: each vehicle has a predetermined load capacity, typically different from all other vehicles comprising the fleet (heterogeneous nature), the capacity of a vehicle cannot be exceeded, a sin
15、gle vehicle supplies each customers demand, the number of vehicles used 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 theoret
16、ical 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, offering s
17、imultaneously 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 academic researchers and professional societies in OR/MS, resul
18、ting in a number of papers concerning 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
19、 time or 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 t
20、he first algorithms that 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 re
21、garding 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, even if this causes a small loss in quality solution. These attributes are essential if an algorithm is to be implemented in a
22、 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, the time that an algorithm is left to propose a solution to the problem is of utmost importance to the problem. Certainly, t
23、here 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 quality instantly, then an aggressive strategy is to be implemented. If the user rela
24、xes the time of solution to be obtained, that is to say if the algorithm is left to search the solution space more effciently, 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 rou
25、tes 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 desire of the user to use vehicles of his own desire, owned or hired, according to his daily needs. The Generalized Route
26、 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 this case a seed route is inserted that contains only the depot. Rival nodes to be inserted are then ex
27、amined. All routes 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 *) tha
28、t minimizes the Clark 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 between nodes k and l. The Clark and Wright saving cal
29、culation 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 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 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 *) (2)
