The orienteering problem: A survey

Vansteenwegen, Pieter; Souffriau, Wouter; Oudheusden, Dirk Van

doi:10.1016/j.ejor.2010.03.045

Cited by 792 publications

(463 citation statements)

References 57 publications

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“…References [15,27] use genetic algorithm whereas [16] uses a local search heuristic and [8] a binary search tree heuristic. The common term in the literature for these problems is tourist trip design problem (TTDP) or, simply, Orienting Problem [30]. In the two cases, the goal is to create a tour trip with the most desirable sites, subject to various budget and time constraints.…”

Section: Related Workmentioning

confidence: 99%

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ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

López-Matencio¹,

Vales‐Alonso²,

Costa-Montenegro³

2017

Mobile Information Systems

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In this work, we propose ANT, a network of agents which exchange information in a stigmergy-based fashion. In ANT, each system's actor distributes pheromones as a way of indicating places' attractiveness, as well as for building a proper routing path to those sites. The goal of ANT is to improve the chances of discovering points of interest, as well as to reduce the time required for doing so. We have applied ANT to a tourist mobility scenario, where both people and things (events, restaurants, performances, etc.) participate. ANT has achieved notable success in this example case. We find that probability of discovering temporary events and dates improves by more than 35%, while the mean time employed to determine static point decreases by more than a third. We also introduce a mobile-based architecture which performs ANT tasks efficiently and easily for the user.

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Section: Related Workmentioning

confidence: 99%

“…Note that TTDP and TSP are different problems: the TTDP objective is not the shortest path but finding a suitable route for a particular user. The survey works [30,31] collect the tourism-centered algorithms to solve this problem. Solving the TTDP is a daunting task (commonly, using metaheuristics) and it needs a lot of information.…”

Section: Related Workmentioning

confidence: 99%

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

López-Matencio¹,

Vales‐Alonso²,

Costa-Montenegro³

2017

Mobile Information Systems

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“…The goal of the problem is to find an optimal trip that maximizes user happiness under the constraint that all the POIs in the trip can be visited and the trip can be completed within the user time budget with a probability not less than a user specified threshold. This problem is NP-hard as it is a special case of either the Knapsack problem [11] or the Orienteering problem [25].…”

Section: Contributionsmentioning

confidence: 99%

“…The orienteering problem (OP) [25] studied in operation research and theoretical computer science is related to our problem. In OP, a set of vertices is given, each with a score.…”

Section: Operation Research and Schedulingmentioning

confidence: 99%

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

Zhang

Liang

Wang

et al. 2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP hard problem and previous solutions do not consider two practical and important constraints. One constraint is POI availability where a POI may be only available during a certain time window. Another constraint is uncertain traveling time where the traveling time between two POIs is uncertain. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real life LBSN data sets.

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“…Clearly, the OP may be used to model the simplest version of the TTDP wherein the POIs are associated with a profit (i.e., a degree of user satisfaction) and the goal is to find a single tourist itinerary that starts and ends at fixed locations and maximizes the profit collected within a given time budget (time allowed for sightseeing in a single day). Extensions of the OP have been successfully applied to model more complex versions of the single itinerary TTDP [1,3]. The OP with Time Windows (OPTW) considers visits at locations within a predefined time window; this allows modeling opening days/hours of POIs.…”

Section: Introductionmentioning

confidence: 99%

Efficient Metaheuristics for the Mixed Team Orienteering Problem with Time Windows

et al. 2016

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Given a graph whose nodes and edges are associated with a profit, a visiting (or traversing) time and an admittance time window, the Mixed Team Orienteering Problem with Time Windows (MTOPTW) seeks for a specific number of walks spanning a subset of nodes and edges of the graph so as to maximize the overall collected profit. The visit of the included nodes and edges should take place within their respective time window and the overall duration of each walk should be below a certain threshold. In this paper we introduce the MTOPTW, which can be used for modeling a realistic variant of the Tourist Trip Design Problem where the objective is the derivation of near-optimal multiple-day itineraries for tourists visiting a destination which features several points of interest (POIs) and scenic routes. Since the MTOPTW is a NP-hard problem, we propose the first metaheuristic approaches to tackle it. The effectiveness of our algorithms is validated through a number of experiments on POI and scenic route sets compiled from the city of Athens (Greece).

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The orienteering problem: A survey

Cited by 792 publications

References 57 publications

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

Efficient Metaheuristics for the Mixed Team Orienteering Problem with Time Windows

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