Decision making for time-constrained commodity trasportation

Abstract

“Xenios”is a speci c DSS that was developed to assist the daily VRS activities of Greek transport rms during special events. This system incorporated essential functions of GIS, database systems and model management techniques to support overall routing, scheduling and decision-making processes for Vehicle Routing and Scheduling (VRS) problems encountered during the Athens 2004 Olympic Games (ATHENS 2004). 4500 Z. Manoussaridis, C. Mamaloukas and M. Sagheb-Tehrani During the Athens Olympic Games 2004 in Athens, the Vehicle Routing and Scheduling problem was very intense. Actually, the commodities transportation had to be done within strictly de ned time-periods and under many security and tra¢ c restrictions [2, 8]. Our work aimed at the successful setting up of DSS “Xenios” so that it could cover a wide range of variations of the VRS problem, especially problems with hard time-windows in the route generation [15, 17]. Mathematics Subject Classi cation: 68U35 Keywords: Decision Support Systems, VRS problem dimensions, commodity transportation 1 Standing VRS problems and decision-making Trying to develop a speci c Decision Support System (DSS) for VRS activities of transport rms, during ATHENS 2004, came up with the need to survey the various problem dimensions [14] and create a theoretical framework for the speci c context. This would assist us with the successful setting up of the DSS “Xenios”which covered a wide range of variations of the VRS problem encountered by transport rms during the Olympic Games 2004. The main di¤erence of this system from a previous DSS named “Dromones”[14] was the aspect of hard time-windows in route generation. Therefore, the process of VRS decision-making was considered as a quite rigorous one [17]. The system combined GIS features, database management system and several model management techniques to support routing, scheduling and decision-making processes needed by general transport rms [15]. Dantzing & Ramser [6] were the rst to present the mathematical de nition of the vehicle routing problem. In simple words, the problem focuses on the delivery of certain quantities of commodities to a number of customers who are scattered throughout a geographical area. A certain number of vehicles are available for these deliveries and each vehicle has a given capacity. Our aim is to determine a set of routes that each one would start and nish in the depot so that the overall covered route can be minimized always under the conditions that all orders are being served and the issue of time-windows is taken into account. Datzing & Ramser de ne the following speci cations explicitly [6]: A set of n points Pi (i = 1; :::; n), in which all orders are being delivered from a point P0 (the depot). A quantity of orders q that has to be delivered in Pi (i = 1; :::; n). The capacity of the truck is C (C > qi for i = 1; :::; n) A matrix of routs D = [dij] that de nes the route between each pair (i; j = 0; :::; n). Decision making for time-constrained commodity trasportation 4501 The objective is to nd a set of routes that each would start and end at point P0, in order to minimize the overall covered route from the trucks under the condition that all orders are delivered and the capacity of all trucks is respected. There are a number of variations as far as the denomination of the present problem is concerned. Consequently, the present problem will be called as a classical vehicle routing problem (CVRP). The grouping of the problem dimensions aimed to the formation of dimension groups with common characteristics. Several came from the relevant bibliography [1], [3], [4], [5], [7] while others revealed from the eld research conducted in various rms [13], [15]. Based on this groundwork, the formulation of the conceptual schema for the necessary database and the DSS rules de nition were accomplished without any severe problems (see g1).