An exact algorithm for team orienteering problems

Boussier, Sylvain; Feillet, Dominique; Gendreau, Michel

doi:10.1007/s10288-006-0009-1

Cited by 175 publications

(157 citation statements)

References 9 publications

Supporting

Mentioning

155

Contrasting

Unclassified

Order By: Relevance

“…The TOP has two characteristics [4,18]: the objective function is the maximum total profit and all targets are visited, at most, once. Clearly, for such problems, it is difficult to build the vehicle routing problem (VRP) model because the goal of the VRP is to use the minimum number of vehicles to serve all the vertices or to use the minimum total travel distance with a fixed number of vehicles [19,20]. Currently, the TOP has been widely used in solving tourist trip design problems [6,10,21], mobile crowdsourcing problems [22][23][24], UAV task allocation problems [25,26], pharmaceutical sales representative planning problems [27], and resource management allocation problem during wildfires [28].…”

Section: Related Workmentioning

confidence: 99%

Optimization of Pesticide Spraying Tasks via Multi-UAVs Using Genetic Algorithm

Luo

Niu

Zhu

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Task allocation is the key factor in the spraying pesticides process using unmanned aerial vehicles (UAVs), and maximizing the effects of pesticide spraying is the goal of optimizing UAV pesticide spraying. In this study, we first introduce each UAV's kinematic constraint and extend the Euclidean distance between fields to the Dubins path distance. We then analyze the two factors affecting the pesticide spraying effects, which are the type of pesticides and the temperature during the pesticide spraying. The time window of the pesticide spraying is dynamically generated according to the temperature and is introduced to the pesticide spraying efficacy function. Finally, according to the extensions, we propose a team orienteering problem with variable time windows and variable profits model. We propose the genetic algorithm to solve the above model and give the methods of encoding, crossover, and mutation in the algorithm. The experimental results show that this model and its solution method have clear advantages over the common manual allocation strategy and can provide the same results as those of the enumeration method in small-scale scenarios. In addition, the results also show that the algorithm parameter can affect the solution, and we provide the optimal parameters configuration for the algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimization of Pesticide Spraying Tasks via Multi-UAVs Using Genetic Algorithm

Luo

Niu

Zhu

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…A branch-and-price approach to solve problems with 2-4 team members and up to 100 locations is proposed in [17].…”

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

View full text Add to dashboard Cite

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.

show abstract

“…The TOP is known to be NP-hard (Laporte and Martello 1990;Boussier et al 2007). Thus, several heuristics and metaheuristics for this problem class have been proposed in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

The multi-vehicle profitable pickup and delivery problem

2016

View full text Add to dashboard Cite

The transportation industry expanded rapidly in a highly competitive environment. Logistics companies with insufficient volume of transport capacities are forced to make a selection of customers that they can integrate efficiently into their tours. This is of particular relevance in the pickup and delivery market, where shipments from several different customers can be moved on the same vehicle. In the literature, however, the problem of customer selection has not been applied for the given class of pickup and delivery problems so far. We want to fill this gap by introducing the multi-vehicle profitable pickup and delivery problem (MVPPDP), where multiple carriers transport goods from a selection of pickup customers to the corresponding delivery customers within given travel time limits. For this problem, we propose a method based on general variable neighborhood search (GVNS). We conduct experiments with two different variants of this method, namely a sequential (GVNSseq) and a self-adaptive (GVNSsa) version. Additionally, we compare it to an algorithm based on Guided Local Search (GLS), which is known to find good solutions for related problems very fast. The performance of these methods is examined on the basis of data instances with up to 1000 customer requests. In an experimental study, we observe that both variants of GVNS with 11 neighborhoods outperform GLS with regard to solution quality for all sizes of test instances. However, for medium sized and large instances, GLS shows an advantage in average runtimes.

show abstract

An exact algorithm for team orienteering problems

Cited by 175 publications

References 9 publications

Optimization of Pesticide Spraying Tasks via Multi-UAVs Using Genetic Algorithm

Optimization of Pesticide Spraying Tasks via Multi-UAVs Using Genetic Algorithm

Planning for opportunistic surveillance with multiple robots

The multi-vehicle profitable pickup and delivery problem

Contact Info

Product

Resources

About