Suchergebnisse

Both Royal Australian Air Force (RAAF) and Border Protection Command (BPC) aircraft fly maritime surveillance missions for the Australian Government on a regular basis. These missions involve searching particular areas of interest (AIs) for illegal fishing or people smuggling activities. In this work a square AI is considered, with an aircraft tasked to search for ships using its sensors (e.g., radar, electrooptical). Waypoints (points that must be visited) are included in the AI to ensure that the entire AI is covered. The aim for a particular search is to detect and classify as many ships as possible, while doing so in the shortest time. Depending on the ship density, the aircraft may not have time to search the entire AI. In this paper an augmentation of the traditional Travelling Salesman Problem (TSP) is considered, where the ships (cities) move with random velocities (dynamic TSP), have different start and end points (open TSP) and there is incomplete a priori knowledge of the problem space (on-line TSP), making this problem much more challenging. Earlier work (Marlow et al, 2007) considered a "baseline" case of an S-shaped search pattern and a default heading (the route flown when there are no ships currently detected) direct to the next waypoint. This paper considers three extensions with an aim to increase the level of ship classifications: these are 1) alternative initial flight paths, 2) alternative default headings and 3) including "ghost ships" in the search. The principle behind the S-shaped pattern is that the aircraft will cover the entire AI with its sensors, giving aircrew the best chance of detecting all ships in the AI. This paper considers alternative spiral waypoint patterns, both an "inspiral" (from one corner of the AI, spiraling towards the centre) and an "outspiral" (the reverse). These approaches are theoretically more likely to detect ships that enter the AI during the mission. The direct-to-waypoint default heading will minimise the travel time, but it also may potentially result in the aircraft not covering the entire AI with its sensors, particularly if it has already been diverted significantly from the "wayline" (the direct line between waypoints). In this work, a perpendicular return to the wayline is considered, which increases the distance travelled but is also likely to increase the probability of detecting more ships. A third option is also considered in which the aircraft continually aims for the midpoint on the wayline of the perpendicular intercept point and the waypoint. The object of ghost ships is to direct the aircraft to fly to areas of the AI that it may not otherwise visit. This may particularly be the case in low-density environments where the aircraft has to substantially divert from the wayline to classify ships and, in returning, inadequately cover other areas where ships may be present. Ghost ships remain in the current tour until they are "detected", whereupon they are removed. Results suggest that, at lower densities, the perpendicular default heading and including ghost ships provide an overall improvement in classifications of the order of a few percentage points, which in real terms translates to an extra 1-2 ships on average. Significantly it also translates to an increase in the percentage of cases where 100% classifications are achieved. These improvements generally come at a cost of increased distance travelled by the aircraft and thus greater fuel consumption. In the case of ghost ships, there is an additional cost in computational time due to the requirement to include them in the tour. Beyond the critical ship density (where classifications cannot physically reach 100%), these variations offer no real advantage and in some cases are counter-productive, so the baseline pattern is more appropriate in these circumstances.

The Royal Australian Navy's Patrol Boat Force carries out essential tasks in the surveillance, policing and defence of Australia's coastal waters. To help the Navy make efficient use of a new generation of boats, the authors have developed optimization procedures to schedule the activities of the boats and their crews. The proceduresembodied in a software system called CBM (Crews, Boats, Missions)use simulated annealing and specialized heuristic techniques within a multi-stage problem-solving framework. Tests show that CBM is reliable in terms of solution quality, and flexible with respect to the range of scheduling conditions applied. CBM has proved valuable to the Navy as an investigatory tool, and it is planned that it should be adapted for operational use, as part of a decision support system to aid in the ongoing management of patrol boat operations.

Both Royal Australian Air Force (RAAF) and Border Protection Command (BPC) aircraft fly maritime surveillance missions for the Australian Government on a regular basis. These missions involve searching particular areas of interest (AIs) for illegal fishing or people smuggling activities. In this work a square AI is considered, with an aircraft tasked to search for ships using its sensors (e.g., radar, electrooptical). Waypoints (points that must be visited) are included in the AI to ensure that the entire AI is covered. The aim for a particular search is to detect and classify as many ships as possible, while doing so in the shortest time. Depending on the ship density, the aircraft may not have time to search the entire AI. In this paper an augmentation of the traditional Travelling Salesman Problem (TSP) is considered, where the ships (cities) move with random velocities (dynamic TSP), have different start and end points (open TSP) and there is incomplete a priori knowledge of the problem space (on-line TSP), making this problem much more challenging. Earlier work (Marlow et al, 2007) considered a "baseline" case of an S-shaped search pattern and a default heading (the route flown when there are no ships currently detected) direct to the next waypoint. This paper considers three extensions with an aim to increase the level of ship classifications: these are 1) alternative initial flight paths, 2) alternative default headings and 3) including "ghost ships" in the search. The principle behind the S-shaped pattern is that the aircraft will cover the entire AI with its sensors, giving aircrew the best chance of detecting all ships in the AI. This paper considers alternative spiral waypoint patterns, both an "inspiral" (from one corner of the AI, spiraling towards the centre) and an "outspiral" (the reverse). These approaches are theoretically more likely to detect ships that enter the AI during the mission. The direct-to-waypoint default heading will minimise the travel time, but it also may potentially result in the aircraft not covering the entire AI with its sensors, particularly if it has already been diverted significantly from the "wayline" (the direct line between waypoints). In this work, a perpendicular return to the wayline is considered, which increases the distance travelled but is also likely to increase the probability of detecting more ships. A third option is also considered in which the aircraft continually aims for the midpoint on the wayline of the perpendicular intercept point and the waypoint. The object of ghost ships is to direct the aircraft to fly to areas of the AI that it may not otherwise visit. This may particularly be the case in low-density environments where the aircraft has to substantially divert from the wayline to classify ships and, in returning, inadequately cover other areas where ships may be present. Ghost ships remain in the current tour until they are "detected", whereupon they are removed. Results suggest that, at lower densities, the perpendicular default heading and including ghost ships provide an overall improvement in classifications of the order of a few percentage points, which in real terms translates to an extra 1-2 ships on average. Significantly it also translates to an increase in the percentage of cases where 100% classifications are achieved. These improvements generally come at a cost of increased distance travelled by the aircraft and thus greater fuel consumption. In the case of ghost ships, there is an additional cost in computational time due to the requirement to include them in the tour. Beyond the critical ship density (where classifications cannot physically reach 100%), these variations offer no real advantage and in some cases are counter-productive, so the baseline pattern is more appropriate in these circumstances.

The Royal Australian Navy's Patrol Boat Force carries out essential tasks in the surveillance, policing and defence of Australia's coastal waters. To help the Navy make efficient use of a new generation of boats, the authors have developed optimization procedures to schedule the activities of the boats and their crews. The proceduresembodied in a software system called CBM (Crews, Boats, Missions)use simulated annealing and specialized heuristic techniques within a multi-stage problem-solving framework. Tests show that CBM is reliable in terms of solution quality, and flexible with respect to the range of scheduling conditions applied. CBM has proved valuable to the Navy as an investigatory tool, and it is planned that it should be adapted for operational use, as part of a decision support system to aid in the ongoing management of patrol boat operations.

Traditional vehicle routing problems implicitly assume only one crew operates a vehicle for the entirety of its journey. However, this assumption is violated in many applications arising in humanitarian and military logistics. This paper considers a Joint Vehicle and Crew Routing and Scheduling Problem, in which crews are able to interchange vehicles, resulting in space and time interdependencies between vehicle routes and crew routes. It proposes a constraint programming model that overlays crew routing constraints over a standard vehicle routing problem. The constraint programming model uses a novel optimization constraint that detects infeasibility and bounds crew objectives. Experimental results demonstrate significant benefits of using constraint programming over mixed integer programming and a vehicle-then-crew sequential approach.

Traditional vehicle routing problems implicitly assume only one crew operates a vehicle for the entirety of its journey. However, this assumption is violated in many applications arising in humanitarian and military logistics. This paper considers a Joint Vehicle and Crew Routing and Scheduling Problem, in which crews are able to interchange vehicles, resulting in space and time interdependencies between vehicle routes and crew routes. It proposes a constraint programming model that overlays crew routing constraints over a standard vehicle routing problem. The constraint programming model uses a novel optimization constraint that detects infeasibility and bounds crew objectives. Experimental results demonstrate significant benefits of using constraint programming over mixed integer programming and a vehicle-then-crew sequential approach.

Road assets including road signs and utility poles are among the objects that are closely monitored by government bodies and transportation authorities in many countries. This paper presents a new approach to collect and analyse the information needed for road asset management and other digital cartography applications. Instead of using dedicated surveying vehicles, skilled drivers and high quality sensors that are common for traditional data collection, it envisions an infra-structure in which the data collection is done through cheap sensors that mainstream consumer cars can host in the near future. In order to investigate the new design, a prototype sensor named dSensor has been developed, each to be mounted on public transport vehicles, to collect information from the road scene. Each dSensor has three cameras, wireless modem, Global Positioning Systems (GPS), accelerometer, gyroscope, magnetometer and a laptop. A collection of vehicles equipped with dSensors and a server constitute the distributed mapping system called dMap for road asset management. The dMap design supports many features of an ideal road asset find and replace management system and in comparison to similar solutions or concepts for road asset management, the dMap is cost effective with high quality information particularly on matters such as update rate.

Road assets including road signs and utility poles are among the objects that are closely monitored by government bodies and transportation authorities in many countries. This paper presents a new approach to collect and analyse the information needed for road asset management and other digital cartography applications. Instead of using dedicated surveying vehicles, skilled drivers and high quality sensors that are common for traditional data collection, it envisions an infra-structure in which the data collection is done through cheap sensors that mainstream consumer cars can host in the near future. In order to investigate the new design, a prototype sensor named dSensor has been developed, each to be mounted on public transport vehicles, to collect information from the road scene. Each dSensor has three cameras, wireless modem, Global Positioning Systems (GPS), accelerometer, gyroscope, magnetometer and a laptop. A collection of vehicles equipped with dSensors and a server constitute the distributed mapping system called dMap for road asset management. The dMap design supports many features of an ideal road asset find and replace management system and in comparison to similar solutions or concepts for road asset management, the dMap is cost effective with high quality information particularly on matters such as update rate.