Keenan, P. (2008) “Modelling vehicle routing in GIS” Operational Research, 8(3):201-218.
Modelling vehicle routing in GIS Peter Keenan Management Information Systems UCD Business School, University College Dublin, Dublin 4, Ireland. Email:
[email protected]
Abstract The field of vehicle routing has seen the development of a variety of mathematical techniques. While the practical application of these techniques has depended on the use of spatial information such as road networks, these techniques have developed independently of other users of spatial information. This paper argues that traditional routing techniques have neglected the importance of path constraints and that GIS approaches allow the modelling of an extended range of routing problems. The well-known taxonomy of routing problems by Bodin and Golden is extended to reflect the additional possibilities introduced by the use of GIS techniques. The paper concludes by suggesting that the synthesis of vehicle routing and GIS techniques in a spatial decision support system can greatly enhance the modelling of these problems. Keywords Vehicle Routing, Decision Support Systems, Geographic Information Systems.
1. Introduction Operations Research/Management Science (OR/MS) is a well-developed area of applied mathematics and within this field vehicle routing is one of the most actively researched areas. Many routing problems have been identified, one comprehensive bibliographic review (Laporte and Osman 1995) identified 500 articles representing relatively important contributions related to routing at the time. Multiple variations of the problem exist and these have been considered using a wide range of mathematical techniques. The majority of routing problem formulations assume that the quantities to be delivered or collected are located at distinct points. An efficient route is one that minimises the distance travelled between these points, subject to various loading and time constraints. Arc routing problems represent the other main type of problem, where the object of the routing process is to establish a sequence of arcs to be visited. While some routing problems can be solved using optimal techniques, for many practical problems only heuristic techniques are capable of providing an answer in an acceptable time.
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Mathematical routing models typically employ a simplified representation of the real world, allowing a more straightforward mathematical representation of the problem, which can be solved in reasonable time. While this simplification made problems more tractable, the models developed did not fully consider the range of issues of interest for practical scheduling of a fleet of vehicles. Innovations in information technology (IT) have allowed the storage of more data related to the problem and solution of larger problems. A variety of Decision Support Systems (DSS) for routing have been developed to facilitate interaction between the models and skilled users. This interaction allows the user overcome some of the limitations of the poor fit between the model and the real-world problem. Geographic Information Systems (GIS) are recognised as one of the new technologies which can be usefully introduced to DSS for vehicle routing (Keenan 1998). However, many examples of vehicle routing and GIS integration involve little more than the use of the GIS for storing data and displaying routes, the spatial processing abilities of the GIS are rarely used. A recent paper by Marzolf, Trépanier, and Langevin (2006) notes that GIS is largely used in OR/MS to feed mathematical models, with the modelling capabilities of the GIS being less often used. Transportation is a well-established area of application of GIS (Thill 2000) and a distinctive GIS-T community exists (Miller and Shaw 2001). While definitions may differ, GIS-T is concerned with a diverse set of transport related applications, including the maintenance of transport facilities, the aggregate movement of traffic and path finding applications. In general, these are concerned with limited transport networks, e.g. road or rail, within a larger geographic context. Many GIS-T applications require an extension of the traditional map representation of GIS to a network representation to support navigation (Goodchild 2000). GIS has a number of characteristics, which differ in their relevance to transport. Goodchild (1998) notes that GIS is based on multiple paradigms. First, GIS is used for digital map production. Second, it is used as an inventory and management tool for spatial distributed facilities. Third, GIS is a useful technology for integration of data. Fourth, GIS supports spatial analysis and finally GIS supports dynamic modelling. The OR/MS community recognise the value of GIS for maps and as a management tool for spatial data. This paper argues that further benefit can be gained from integrating OR/MS models with GIS based path-finding techniques to allow the definition of a wider range of routing problems where both a larger quantity and a greater variety of data can be considered.
2. Categories of Routing Data In modelling routing problems, three categories of data can be identified. Routing problems will contain data associated with locations, for instance data relating to the
depots and the customers. The second category of data in a routing problem relates to the vehicles that must visit these locations. Vehicle parameters include speed and capacity. Finally, the problem will contain a set of paths between locations. From these paths, the distances travelled and the travel time can be derived. The vehicle routing problem (VRP) can then be stated as a set of visits by vehicles to locations along a set of paths between these locations. Constraints in the problem will exist for locations, paths and vehicles. Many of these constraints are independent of each other. For example, a change in the stop time at a location may not directly affect the paths that can be used. Other constraints are interdependent; they are affected by the interaction between two types of data. For instance, a particular type of vehicle may not be able to visit a certain location or use a certain path. Traditional views of the routing problem have tended to emphasise the vehicle and location constraints, without paying much attention to path constraints. In widely cited definitions of the VRP, the completed vehicle route is typically defined as a sequence of points to be visited (Bodin and Golden 1981). This paper recommends a broader definition of vehicle routing problems to accommodate problems where the path taken is an important component of the problem. In this broader context,
Table 1. Constraints in Vehicle Routing Problems adapted from Bodin and Golden (1981)
location constraints
time to service a location number of depots nature of demands – deterministic or stochastic location of demands : points, arcs or polygons operations – pickups or drop-offs
vehicle constraints
number of depots size of fleet vehicle capacity constraints maximum vehicle route times
path constraints
underlying network – directed or undirected time to travel a given network segment vehicle type limitations on network segments
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relevant information can be usefully categorised into location, vehicle and path data. The main constraints of interest in routing problems are classified in Table 1. The traditional optimisation techniques used in routing, for example travelling salesman algorithms, consider a number of points on an XY plane. Some early routing software was based on the use of location co-ordinates and straight-line distance was used as a surrogate for actual travel distance. This abstraction of the problem assumed that selection of an appropriate path was a trivial exercise. In practice, the actual route is constrained by the need to use suitable roads. Consequently, the straight-line distance approach was replaced by the true distance approach, using distances calculated from the road network. As the true distance approach became an increasingly important feature of routing DSS design, GIS type data structures were needed to organise this. The incorporation of additional path data increased the usefulness of the problem formulation at the cost of making the software to solve it more complex. The traditional true distance approach calculates travel distances before the actual routing process. This approach reduces a complex road network into a relatively straightforward matrix of distances between locations. However, simple distance is not the only issue arising in many real-world problems. In practice, routing problems are frequently constrained by time rather than by distance travelled. It is therefore important to model different speeds on different sections of the road network. The speeds for the road network may be derived from the road classification, or measures such as the number of traffic lanes. Sophisticated routing models may adjust speeds to take account of alterations in travel speed due to traffic congestion or due to the existence of steep gradients. The traditional representation of distance or travel times as a simple matrix can accommodate only a few of these extra constraints. For complex problems, a simple matrix approach may be inadequate as it expects that the data does not change during the routing process, which may represent an unreasonable simplification of the real-world situation. An alternative is real-time calculation of travel times from a comprehensive model of the road network. In urban applications additional concerns include, one-way streets, no right or left turns, and vehicle size restrictions. In rural areas, large vehicles may be unable to negotiate all the roads in the network due to steep gradients or limited road width. GIS software provides the means to store and manipulate a detailed network representation. However, the pattern of development of GIS often led to map representations in GIS software that were unsuitable for network models (Ralston and Zhu 1991). Modern GIS software often incorporates specialised data structures for comprehensive network representation (Fischer 2004). However, even if the software can fully represent a complex network, the necessary data must also be available; for instance the detailed layout of grade separated junctions (Bodin and Levy 1994).
3. Interdependence of Routing Parameters The data requirements for vehicle routing are greatly increased if the parameters are interdependent or dependent on another variable such as time. For example, if traffic congestion is modelled, the appropriate speed may depend on the time of day. Other time related restrictions might exist such as restrictions on the use of vehicles in pedestrianised areas, where deliveries might be required to take place early in the morning. Time restrictions on locations can be characterised as time windows; this is a well-researched area of OR/MS (Cordeau, Desaulniers et al. 2001), but is not spatial in nature and will not be discussed here. Other problems, such as dynamic routing problems, have time related data interactions. In these problems additional data arises, in an unpredictable way, during the routing process (Psaraftis 1995). On example of a dynamic problem, where the location data can change, is courier parcel collection and delivery, where a request to collect a package from a new location may arise at any time. Similarly, real time information may be obtained on the paths available for routing if information is received on traffic congestion or road closures due to accidents, etc. Bertsimas and Simchi-Levi (1996) discuss the modelling issues for several dynamic routing problems, for example routing in situations where orders are received in real time. In a review of dynamic routing in the mid 1990s, Psaraftis (1995) noted the growth of GIS systems and GIS related technology, for example global positioning systems (GPS). This reflected an increasing trend toward the integration of routing and GIS techniques. In the decade since the paper by Psaraftis, the growth of mobile technologies and the widespread use of GPS has greatly facilitated the dynamic rerouting of vehicles (Giaglis, Minis et al. 2004; Zeimpekis, Giaglis et al. 2005). Complex interactions between vehicle parameters and paths may mean that fully loaded vehicles cannot use all segments of the network, because of steep gradients or because of weight limits on bridges, etc (Ray 2007). A vehicle may be unable to use a certain road while fully loaded, but could return this way when empty. In this situation, the road network cannot be pre-processed easily into a simple distance matrix; instead the interactions between vehicles and paths have to be incorporated in the operation of the vehicle routing model. This means that a sophisticated road network representation must be available to routing algorithms that are designed to use it. Over time, OR/MS researchers have sought to add more geographic detail to allow the use of more realistic models. This has led to the increasing interest in the synthesis of GIS and routing techniques, to enable more detailed problem representations to better support practical problems.
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Figure 1. Area served from bus stop will include neighbouring streets
A second real-world problem, where the parameters are interdependent, is the combined location-routing problem. Location problems are complex in themselves and are an existing area of integration of modelling techniques and GIS (Church 2002). Many types of problem require both a location phase and a routing phase; these can usefully be combined into a more complex model. In such a problem, the total demand or supply in the problem needs to be allocated to a limited number of locations. Vehicle routes are then generated to visit those locations. Many problems had multiple depots, requiring that customers be associated depots, and routes were generated to visit those customers. Besides this type of problem, however, there are some situations where the initial phase of the problem is an allocation of demand to delivery locations. In many real-world problems, the actual demand is distributed over a larger area than the actual location where delivery/collection takes place. For example, in public transport routing, a bus stop on a main street will service people travelling to a segment of that street and to segments of neighbouring side streets. Customers will walk from the area serviced to the bus stop (Figure 1), consequently the demand for public transport can be mapped on to bus stops (McDonnell, Ferreira et al. 2006). The location of the bus stops is in itself an important problem, which affects the routes generated. The location to be visited, the bus stop, can be represented as a point. In other problems, the actual demand is distributed on another type of location, for example an arc or a polygon. In such a problem there may be a series of potential
feasible locations, only one of which will be visited in a given area, this may be modelled as a generalised travelling salesman problem (Laporte, Asef-Vaziri et al. 1996). A complete solution of the location-routing problem will require the allocation of the total travel demand in a town to a limited number of service locations, which are then routed (Figure 2). An example of this type of application is school bus routing (Braca, Bramel et al. 1997; Spasovic, Chien et al. 2001). A related problem might arise with convenience shops, which provide customers in their local area with everyday purchases such as newspapers or bread. These shop locations can be modelled as a point in a routing delivery problem. However, some routing problems might be concerned with identifying an appropriate subset of shops to visit, estimating their potential sales from population data. At the risk of oversimplification, these types of problems can be approached on a two-phase basis, with the allocations being processed first and the routing completed as a separate stage. Comprehensive modelling of such a problem requires trade-offs between the location and routing phases of the problem The scale of the geographic area of interest in a routing problem may also differ greatly. Routes may service an area comprising several thousands of square kilometres. In other cases, the area to be covered by the route is much less, for example on an urban parcel delivery route. For problems where only a small area is of interest, the detailed geography of the area becomes a significant issue. Usually, urban applications will require more attention to geographic parameters and modern OR/MS applications in urban areas usually make some use of GIS data (Karadimas, Kouzas et al. 2005). For some classes of routing problem, for example public transport scheduling, the data required for the problem may be available from public sources. For instance, the census of population could be used to estimate demand for a variety of routing problems. Much of this information is associated with spatial units, such as administrative districts, rather than directly with the network being optimised. This requires that population associated with arcs or polygons is allocated to points, for example bus stops or subway stations, which are then visited by routes. Routing for some problems makes use of data on geographic objects other than the road network. An example might occur in census enumeration, where an enumerator wishes to complete visits in one census district before moving to the next one. This would require information on the polygons representing the census district and the arcs on the actual route. In a postal delivery problem, the postal addresses may be based on district names. In a postal problem the quantities of letters to be delivered may be derived from population data based on these districts, this requires a system that can effectively manipulate polygon data.
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4. Spatial Interactions in Routing Problems The data requirements for the representation of routing problems are increased where elements of the data are interdependent, as this means that it is impossible to preprocess paths into a simple distance matrix. This situation occurs where there are multiple path constraints or where the path constraints are not constant, but are dependent on time or on changing vehicle or location parameters. Some well-known routing problems fall into this category, for example arc routing problems, where the network itself is the object of the routing process and a detailed network representation is therefore required. Table 2 indicates some of the types of location data. Those routing problems with data interdependencies require more complex systems. Traditional techniques have neglected the importance of path constraints; a more complex problem results where interdependencies occur between paths and other aspects of the problem (Table 3).
Table 2. Types of Location Data Data Types Basic Types
Data Interdependencies
Type of Problem
Example
point
traditional delivery problems
delivery from warehouses to shops
arc
arc routing
postal delivery
polygon
generalised TSP
post-box collection
interaction with vehicle parameters
continuous demand, intermittent vehicle service
refuse collection: volumes based on time since last collection
interaction with path parameters
location – routing problem
public transport routing with stop location
Table 3. Types of Path Data Data Types Basic Types
Data Interdependencies
Type of Problem
Example
planar
XY co-ordinate distance
ship routing at sea
network constrained
true distance approach
urban delivery over street networks
interaction with location parameters
paths avoid objects
ship routing in relation to islands
interaction with vehicle parameters
vehicle load restrictions
weight limits on bridges
One example of a routing problem with potentially complex interactions between paths and location parameters is hazardous goods routing, for example the transport of toxic waste or nuclear materials. Routing for the transport of hazardous goods may wish to avoid certain areas, such as environmentally sensitive areas, roads with steep gradients, or areas with bad weather conditions. Hazardous waste routing problems are documented both in the GIS literature (Freckmann 1993) and in the OR/MS literature (Zografos and Androutsopoulos 2005). Routes generated for hazardous goods might wish to avoid populated areas, while at the same time remaining within a specified distance of emergency facilities (Figure 3). In this situation, vehicles are travelling on network-constrained paths, wishing to maintain a certain distance between the vehicle and certain point or polygon locations. Such vehicles might also wish to remain within a certain road distance of emergency facilities. Hazardous cargoes are potential targets for terrorists and this means that vehicles may wish to stay near police stations, as well as fire and ambulance services (Huang, Long et al. 2003). The transport of cash to banks is another type of problem where vehicles may wish to remain close to police stations (Tarantilis and Kiranoudis 2004). This type of problem might be further complicated by the existence of time constraints limiting the times at which these hazardous products might be transported, for example avoiding periods when roads are congested. Hazardous materials routing may need to respond to adverse weather conditions, such as snowstorms or strong winds and a DSS can help schedulers make these changes (Beroggi and Wallace 1994). The path of a storm could be modelled on a GIS based system and routes recalculated to avoid possible danger.
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Location to be avoided
Figure 3. Network constrained route avoiding a point location.
An example of interaction between paths and locations is security patrolling around a sensitive installation, such as an airport. This might require patrols on roads near to the airport. The precise security risk of a section of road would be influenced by factors such as its distance from the installation, which might be the irregular boundary of a large airport. Other factors such as elevation or sight lines to the installation could be considered. The region to be patrolled is derived from the polygon location of the airport (Figure 4) and the actual patrols might take place using road vehicles that are constrained to use the road network. The GIS routines could provide a measure of the need to patrol each road section and an appropriate arc routing algorithm could be used to design routes. A system for such a routing process would need to work with multiple interactions between the data structures in the problem. Another important class of routing problems, where GIS techniques are very relevant, arise in emergency evacuation applications. These applications may be concerned with evacuation from an area close to a fixed location. In many cases potential disaster situations can be simulated for evacuation planning purposes, for example around a nuclear power plant (Hobieka, Kim et al. 1994; de Silva and Eglese 2000) or where there is a risk of forest fire (Church and Cova 2000). Other evacuation situations arise as a result of dynamic conditions, for example a hurricane or tornado for this type of problem the design of the routes may be strongly influenced by geographic features (Anonymous 2000).
Sensitive Installation
Figure 4. Patrol area around irregular boundary of sensitive installation While many emergency evacuation applications are concerned with the aggregate movement of traffic, specific vehicles might also need to be routed. If a flood or earthquake has taken place, many of the potential routes may pass through areas rendered unsafe by the disaster. The extent of the danger may be calculated by the GIS by reference to geographic data and the suitability of particular road segments can be derived from this. This analysis could provide data to a routing model, for example routes may be generated to allow emergency services visit buildings in the path of a forest fire (Bonazountas, Kallidromitou et al. 2007). The sequencing of such routes would be largely determined by the need to visit those in most danger. This situation could be modelled on a GIS based system, using data on elevation, type of vegetation, etc. Therefore, this type of problem might benefit from a combination of GIS and routing techniques to model interactions between population data, elevation data and location data. Zerger and Ingle-Smith (2003) indicated the importance of the dispatch of emergency vehicles and repair crews in a disaster situation and noted the limitations of GIS for this task. GIS software can be used to derive an approximate measure of risk at different points in a road network (Zhang, Hodgson et al. 2000), which can then be processed by OR/MS algorithms to evaluate 11
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minimum risk paths (Patel and Horowitz 1994).
5. DSS and GIS based routing support The contribution that GIS techniques can make to a routing DSS will be small for problems that have no great spatial content. Such problems might include many of the delivery problems addressed in the classic vehicle routing literature. In such problems, the schedules may be tightly constrained by non-geographic elements such as time windows. GIS or mapping software might be used to provide an attractive onscreen interface, and GIS might provide a convenient way of storing the roads networks in the problem, but the database and spatial query capabilities of GIS will have little role to play. One example of such a problem is that of fuel delivery, where a multi-compartment tanker is used to service customer orders of different sizes. The vehicle may be required to carry a number of different types of fuel; different octane grades, unleaded or leaded, heating fuel, or diesel. In some countries, differences exist between fuels for tax reasons, for example if diesel for agricultural use is taxed less than that for road vehicles. A fuel tanker can typically service only a small number of orders per trip, less than three or four. The vehicles used for fuel delivery will typically have a number of compartments; orders will have to be allocated to these compartments without mixing different types of fuel. As different fuel types cannot be mixed, and as there are only a small number of points to be delivered, the efficient packing of the vehicle is the most important consideration. For this type of problem a graphic interface is hardly needed, the quality of solution being largely determined by the algorithm used. This group of problems has long been of concern to researchers in OR/MS, but is not especially relevant to GIS. . For problems that have few geographic parameters, a graphic interface is clearly useful if there are a large number of delivery/collection points. However, such an interface need not include a great deal of geographic information and need not be as complex as a GIS. Problems that fall into this category include the large number of routing problems that are concerned with delivery of parcels, supplies, etc. to a set of specific customers. The spatial complexity of such problems is largely determined by the number of customers to be delivered, as geographic features other than the location of customers are not relevant. The complex nature of these problems means that routing models can be of assistance, but the large number of possible solutions increases the difficulty of the problems. Therefore, suitable algorithms and a facility to modify the routes using a graphic interface are required. However, the visual component of the interface and the database component need not include a large number of geographic features. Commonly used interactive vehicle scheduling software is well suited to solving this group of problems. Routing for standard delivery problems in geographically compact regions, such as
urban areas, requires additional attention to geographic features. In this case, network features such as one-way streets, no left or right turns, traffic congestion, etc., will have an important part to play. These additional requirements indicate that a more sophisticated support system is appropriate. Such a system requires the ability to store and display the level of detail appropriate to the problem. Therefore, while many routing problems are straightforward on a regional scale, at a detailed urban scale more information is needed and therefore GIS based network analysis techniques will be more useful. For some types of routing problems, additional geographic parameters are introduced by the fact that the relevant data for the problem is largely external. Relevant external data will usually include population data, including socio-economic data for that population. For instance, a marketing research project may require researchers to visit a number of locations. The route generated will largely be determined by considerations such as structuring the age or socio-economic groups in the sample. For such problems a traditional GIS may be useful, but sophisticated routing techniques are probably not needed. Problems of this type are frequently addressed by people with GIS expertise, with little explicit use of OR/MS techniques. There is an increasing trend for network analysis tools, for example shortest path algorithms, to be included in GIS software. These tools allow useful analysis, but are not sufficient for complex routing problems. For problems that are spatially complex and have a number of geographic entities involved, we suggest that the use of a SDSS is appropriate. Such a SDSS would combine appropriate algorithms with that subset of GIS data that is pertinent to the problem. Population data is of obvious relevance, and other information found in a GIS may also be relevant, including the existence of geographic features such as mountains, lakes or rivers. Complex interactions between these features and the road network may have to be modelled for some types of routing problem. The potential contribution of GIS software is great because of the facilities for database interactions between different geographic features found in this software. We suggest that a SDSS with both GIS techniques and sophisticated vehicle routing models is needed for this type of problem. SDSS based systems will provide an important contribution in any situation where routing, and the road networks used for routing, needs to be related to other geographic features. Tourist routing may aim to design routes with the maximum scenic value or with a desired mixture of sights (van der Knapp 1993). These routes would require interaction of a routing algorithm with data on elevation, type of vegetation, location of rivers and lakes, etc. An example of routing applications with potentially complex interactions between spatial parameters might exist in the military field. Military applications typically 13
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make use of off-road vehicles. While such vehicles are in principle capable of straight-line travel over all terrain, the actual ground conditions greatly affect the speed at which such vehicles could travel. Military applications might involve a desire for vehicles to travel on high ground to gain a commanding position or to travel below the horizon to facilitate concealment (Rasmussen 1997). A GIS based system with the ability to handle elevation data could provide the necessary support to identify a path that minimises threat (Szczerba 1999). In a bulletin board response to web article (Schutzberg 2005) on the relationship between spatial techniques and computer games, Marble noted that game designers wishing to model human movement over natural terrain faced similar routing problems to those in GIS. Agriculture and forestry provide examples where GIS and routing techniques might usefully be combined. Martell (1998) notes the relevance of GIS to the use of OR/MS in forestry, while Ducheyne (2006) suggests that the potential of GIS largely remains unexploited and that SDSS is needed to release this potential. Agricultural routing problems provide an example where complex interaction may take place between the
Table 4. Example of an agricultural routing SDSS Objective identify collection points
Actions Required identify convenient locations on roads for collections
SDSS technique associate polygon volumes with arcs on road network
build routing problem identify quantities to be transported
calculate polygon areas in database and derive volumes from these
build road network
generate distance/ travel time data for use by mathematical procedures
standard GIS operation called within macro
establish routing parameters
enter speed, capacity etc.
user intervention using form interface
route vehicles
apply routing algorithm
customised routing model calling SDSS routines
refine solution
interactive user modification of routes
display routes in GIS interface, user alterations lead to dynamic recalculation of volumes
routes and various geographic features. A variety of agricultural and forestry routing applications might use off-road vehicles. These vehicles might be capable of low speed travel across fields, and higher speed travel on roads. The routes provided would have to optimise the point at which the off-road vehicle rejoined the road. A GIS could calculate the quantities of product to be transported by such vehicles by reference to such measures as crop yield per hectare. A GIS based system could model travel speeds for such vehicles taking into account gradients, various types of ground conditions and the existence of obstacles. The ground conditions or gradient might influence the type of crop planted, which would determine the quantity of product to be removed from the fields. The type of crop planted might alter ground conditions sufficiently to significantly change the speeds of the vehicles, for example if the ground was ploughed. Therefore, routing for this type of problem would involve many geographic parameters and complex interactions between them. Such a problem could only be approached by an effective synthesis of routing algorithms, and a DSS based on GIS techniques (Table 4). There are few, if any, existing examples of the synthesis of GIS techniques and routing algorithms to provide comprehensive decision support for a problem of this complexity.
6. Implementing routing SDSS There are two fundamental requirements for the successful combination of GIS and OR/MS techniques. Firstly, the GIS must have the necessary data structures and interface features for complex network representation and appropriate detailed data must be available. Secondly, the GIS must have the appropriate software flexibility to allow interaction with specialised OR/MS algorithms. There requirements are increasingly being met, as GIS software has generally become more capable and as GIS vendors have recognised the potential of a wider range of applications. However OR/MS researchers often perceive GIS software as slightly problematic (Tarantilis, Diakoulaki et al. 2004), so continued work is needed to ease the integration of GIS and OR/MS. Many routing urban applications require considerable detail in the representation of the road network. Issues of concern include the representation of grade separated junctions, multilane roads, turn tables and the storage of data on gradients (Miller and Shaw 2001, Chapter 3). Some GIS software, e.g. TransCad, is built specifically with network applications in mind, while modern versions of general software, e.g. ArcGIS, typically incorporate appropriate network functionality relevant to routing applications(Devlin, McDonnell et al. 2008). While digital spatial data is now widely available for developed countries, many traditional sources of spatial data were mapping agencies concerned to produce a visual representation of a map rather than a network that could be processed mathematically. This has led to other spatial data 15
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providers specialising in data for navigation purposes (Miller and Shaw 2001, Chapter 4). The pricing structure and availability of such data has an important influence on the development of transportation related GIS applications. Traditional routing DSS has generally been based around a third generation programming language such as Pascal or C++. The use of a programming language gives great flexibility and productivity is enhanced by the use of database, graphics and interface toolboxes. Modern visual programming languages allow the incorporation of components to extend the capabilities of the software. A SDSS is less likely to be built from first principles, but is usually based around existing GIS software. The GIS provides general-purpose database and interface components and sophisticated tools for selecting spatial data, while the routing components of the system are typically custom built. An integrated application requires that the modelling routines dynamically call spatial data handling routines within the GIS, therefore the software techniques used must be capable of accommodating this form of connection between the programs. Modern GIS software usually provides a macro language to allow procedures be automated, although these are unlikely to be suitable for complex modelling applications. One approach is for the GIS macro to call an existing external program without having any substantial interaction with it. This loose coupling approach may not provide sufficient integration for a sophisticated combination of GIS and OR/MS techniques. GIS may also provide an application-programming interface (API) to allow links to other programming languages; this approach is likely to prove useful for routing systems. The GIS vendors are moving their products towards commonly recognised standards. For example ESRI, the largest GIS vendor, has discontinued its proprietary scripting language, Avenue, and has moved its products to a Visual Basic for Applications (VBA) based scripting language. All vendors provide products that support popular software interchange standards such as Object Linking and Embedding (OLE) and typically an API for integration with Java, C++ and Microsoft Net.
7. Conclusion In this paper, we have looked at a broad spectrum of routing problems with respect to three types of constraint; locations, paths and vehicles. We have suggested that the first two of these are inherently spatial in nature, and that path restrictions have been given less attention in traditional routing applications. We have identified some of the interactions that can take place between these different types of spatial parameters. The incorporation of routing techniques into GIS would allow the building of a SDSS. We have identified the type of problems where we believe that a SDSS may contribute. Such a system would incorporate elements of a GIS with
appropriate OR/MS techniques. Existing work in the GIS and OR/MS areas have concentrated on different aspects of the routing problem. OR/MS researchers have developed sophisticated algorithms to deal with various vehicle and location constraints while paying less attention to path constraints. The OR/MS community have concentrated on modelling and have been willing to sacrifice accuracy of spatial representation for convenience of solution. GIS has developed techniques to represent different types of location and networks and to generate appropriate paths through these networks. GIS offers a much more detailed representation of the spatial relationships, but has previously been less focussed on modelling. There is widespread recognition of the value of GIS as a means of displaying routes and organising data for routing problems. This suggests an appreciation of the value of the cartographic and inventory function of GIS identified by Goodchild (1998) and discussed above. The use of GIS based networks and geocoding techniques can greatly facilitate many types of traditional routing problem, a good example of this being the Sears delivery application built using ESRI software (Weigel and Cao 1999). We suggest that a combination of GIS and OR/MS techniques can be extended to a range of problems with complex path restrictions and multiple vehicles, that have not been successfully addressed by routing techniques in the past. Some of the problems discussed in this paper, such as hazardous waste routing have been researched in the past by researchers working within both the management science and GIS traditions. We suggest that, for these problems, superior decision support can be achieved by a synthesis of techniques drawn from both of these fields. Such a synthesis will allow more complicated problems be addressed and will allow new problems to be modelled. Given the practical importance of many geographic data-intensive routing problems, the integration of GIS and routing techniques offers the prospect of greatly enhanced decision support for many important problems. Miller (1999) argued for a removal of “artificial boundaries” among the transportation, spatial analysis and GIS communities. In this paper, we propose that a better synthesis between OR/MS and GIS modelling has a vital part to play in this mix for transportation applications.
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