An efficient four-phase heuristic for the generalized orienteering problem

Ramesh, R.; Brown, Kathleen M.

doi:10.1016/0305-0548(91)90086-7

Cited by 84 publications

(37 citation statements)

References 11 publications

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“…The methodology springs from the 4-phase heuristic Ramesh and Brown developed for the OP [47]. In the first phase, several initial solutions are created using a construction heuristic with different combinations of parameter values.…”

Section: The Problem Solving Methodologymentioning

confidence: 99%

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Large-Scale Dynamic Observation Planning for Unmanned Surface Vessels

Miller

2009

AIAA Infotech@Aerospace Conference

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With recent advances in research and technology, autonomous surface vessel capabilities have steadily increased. These autonomous surface vessel technologies enable missions and tasks to be performed without the direction of human operators, and have changed the way scientists and engineers approach problems. Because these robotic devices can work without manned guidance, they can execute missions that are too difficult, dangerous, expensive, or tedious for human operators to attempt. The United States government is currently expanding the use of autonomous surface vessel technologies through the United States Navy's Spartan Scout unmanned surface vessel (USV) and NASA's Ocean-Atmosphere Sensor Integration System (OASIS) USV. These USVs are well-suited to complete monotonous, dangerous, and time-consuming missions. The USVs provide better performance, lower cost, and reduced risk to human life than manned systems.In this thesis, we explore how to plan multiple USV observation schedules for two significant notional observation scenarios, collecting water temperatures ahead of the path of a hurricane, and collecting fluorometer readings to observe and track a harmful algal bloom. A control system must be in place that coordinates a fleet of USVs to targets in an efficient manner.We develop three algorithms to solve the unmanned surface vehicle observation-planning problem. A greedy construction heuristic runs fastest, but produces suboptimal plans; a 3-phase algorithm which combines a greedy construction heuristic with an improvement phase and an insertion phase, requires more execution time, but generates significantly better plans; an optimal mixed integer programming algorithm produces optimal plans, but can only solve small problem instances.

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Section: The Problem Solving Methodologymentioning

confidence: 99%

“…Ramesh and Brown [47] proposed a four-phase heuristic algorithm for the generalized orienteering problem, i.e., the cost function is not limited to a Euclidean function. The four phases consist of vertex insertion, cost improvement, vertex deletion, and maximal insertions.…”

Section: Algorithms For the Opmentioning

confidence: 99%

Large-Scale Dynamic Observation Planning for Unmanned Surface Vessels

Miller

2009

AIAA Infotech@Aerospace Conference

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“…A center-ofgravity heuristic is developed in [2], [8] and five step heuristic is introduced in [9]. More heuristic algorithms have been developed to solve the OP [10], [11], [12], [13], [14], [15].…”

Section: Related Workmentioning

confidence: 99%

Planning for opportunistic surveillance with multiple robots

Thakur

Likhachev

Keller

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We are interested in the multiple robot surveillance problem where robots must allocate waypoints to be visited among themselves and plan paths through different waypoints while avoiding obstacles. Furthermore, the robots are allocated specific times to reach their respective goal locations and as a result they have to decide which robots have to visit which waypoints. Such a problem has the challenge of computing the allocation of waypoints across robots, ordering for these waypoints and dynamical feasibility of the paths between waypoints. We present an algorithm that runs a series of graph searches to solve the problem and provide theoretical analysis that our approach yields an optimal solution. We present simulated results as well as experiments on two UAVs that validate the capability of our algorithm. For a single robot, we can solve instances having 10-15 waypoints and for multiple robots, instances having five robots and 10 waypoints can be solved.

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“…Golden, Wang, and Liu, produced a more efficient algorithm in which the algorithm "learns" over the course of the improvements [24]. Ramesh and Brown [34] developed an iterative method with four phases: (1) Build initial routes through a costbenefit analysis scheme; (2) Use Lin-Kernighan 2-opt method to improve routes; (3) Interchange nodes by deleting a node and replacing it with a more valuable node; and (4) Iterating through the phases until the marginal improvement of a round falls beneath a specified threshold [34].…”

Section: The Orienteering Problemmentioning

confidence: 99%

Operational Planning for Multiple Heterogeneous Unmanned Aerial Vehicles in Three Dimensions

Negron

2010

AIAA Infotech@Aerospace 2010

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Unmanned aerial vehicles are being incorporated in an increasing variety of operations. To take full advantage of the vehicles, the plans for the operations should integrate each vehicle's capabilities when planning the operations. This thesis focuses on planning operations for multiple, heterogeneous UAVs for the purpose of monitoring Earth's phenomena through data collection. The planning is done for flight in three dimensions. The problem also includes time window constraints for data collection and incorporates human input in the planning process.Two solution methods are presented: (1) a mixed-integer program, and (2) an algorithm that utilizes a metaheuristic to generate composite variables for a linear program, called the Composite Operations Planning Algorithm. The suitability of the two methods to solve the operations planning problem is compared based on the ability of each of the methods to find high-value, feasible solutions for large-scale, operationally sized problems in a reasonable amount of time. The analysis shows that the Composite Operations Planning Algorithm can develop operations plans for problems including 15 UAVs and 5000 nodes in less than 25 minutes using a desktop computer.

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An efficient four-phase heuristic for the generalized orienteering problem

Cited by 84 publications

References 11 publications

Large-Scale Dynamic Observation Planning for Unmanned Surface Vessels

Large-Scale Dynamic Observation Planning for Unmanned Surface Vessels

Planning for opportunistic surveillance with multiple robots

Operational Planning for Multiple Heterogeneous Unmanned Aerial Vehicles in Three Dimensions

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