Iterated local search for the team orienteering problem with time windows

Vansteenwegen, Pieter; Souffriau, Wouter; Berghe, Greet Vanden; Oudheusden, Dirk Van

doi:10.1016/j.cor.2009.03.008

Cited by 285 publications

(233 citation statements)

References 30 publications

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“…Inspired by [17], each included node in a walk of the solution is associated with its arrival (arrive), starting (start) and leaving (leave) time, as well as the latest time the arrival at the node can take place (maxArrive), such that the walk remains feasible. Considering the walk ).…”

Section: The Iterated Local Search Metaheuristicmentioning

confidence: 99%

“…With regard to the algorithmic approaches for solving MTOPTW, we propose two metaheuristic approaches, namely an Iterated Local Search approach inspired by the one presented by Vansteenwegen et al [17] for the TOPTW, and a Simulated Annealing approach. Among other operations, both these approaches repeatedly execute local-search steps.…”

Section: Mtoptw Algorithmic Approaches-preprocessing Phasementioning

confidence: 99%

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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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Section: The Iterated Local Search Metaheuristicmentioning

confidence: 99%

Section: Mtoptw Algorithmic Approaches-preprocessing Phasementioning

confidence: 99%

Efficient Metaheuristics for the Mixed Team Orienteering Problem with Time Windows

et al. 2016

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“…When POIs have an associated time window, the problem is called TOP with Time Windows (TOPTW) [14]. Interested readers can find a thorough revision of literature about OPTW and TOPTW in [15,16,17,18].…”

Section: State Of the Artmentioning

confidence: 99%

Personalized Tourist Route Generation

García

Arbelaitz

Linaza

et al. 2010

Current Trends in Web Engineering

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Abstract.When tourists are at a destination, they typically search for information in the Local Tourist Organizations. There, the staff determines the profile of the tourists and their restrictions. Combining this information with their up-to-date knowledge about the local attractions and public transportation, they suggest a personalized route for the tourist agenda. Finally, they fine tune up this route to better fit tourists' needs. We present an intelligent routing system to fulfil the same task. We divide this process in three steps: recommendation, route generation and route customization. We focus on the last two steps and analyze them. We model the tourist planning problem, integrating public transportation, as the Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) and we present an heuristic able to solve it on real-time. Finally, we show the prototype which generates and customizes routes in real-time.

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“…The TTDP are studied by [5] and [6] where electronic tourist guide was developed using an optimization method. Vansteenwegen et al [6] employed an effective iterated local search that included an insert and shake steps to escape from local optima.…”

Section: Introductionmentioning

confidence: 99%

“…Vansteenwegen et al [6] employed an effective iterated local search that included an insert and shake steps to escape from local optima. Kantor and Rosenwein [7] proposed two heuristics for solving the OP with time window.…”

Section: Introductionmentioning

confidence: 99%

Economic tour package model using heuristic

Abdul-Rahman¹,

Benjamin²,

Bakar³

2014

AIP Conference Proceedings

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Abstract.A tour-package is a prearranged tour that includes products and services such as food, activities, accommodation, and transportation, which are sold at a single price. Since the competitiveness within tourism industry is very high, many of the tour agents try to provide attractive tour-packages in order to meet tourist satisfaction as much as possible. Some of the criteria that are considered by the tourist are the number of places to be visited and the cost of the tour-packages. Previous studies indicate that tourists tend to choose economical tour-packages and aiming to visit as many places as they can cover. Thus, this study proposed tour-package model using heuristic approach. The aim is to find economical tour-packages and at the same time to propose as many places as possible to be visited by tourist in a given geographical area particularly in Langkawi Island. The proposed model considers only one starting point where the tour starts and ends at an identified hotel. This study covers 31 most attractive places in Langkawi Island from various categories of tourist attractions. Besides, the allocation of period for lunch and dinner are included in the proposed itineraries where it covers 11 popular restaurants around Langkawi Island. In developing the itinerary, the proposed heuristic approach considers time window for each site (hotel/restaurant/place) so that it represents real world implementation. We present three itineraries with different time constraints (1-day, 2-day and 3-day tour-package). The aim of economic model is to minimize the tour-package cost as much as possible by considering entrance fee of each visited place. We compare the proposed model with our uneconomic model from our previous study. The uneconomic model has no limitation to the cost with the aim to maximize the number of places to be visited. Comparison between the uneconomic and economic itinerary has shown that the proposed model have successfully achieved the objective that minimize the tour cost and cover maximum number of places to be visited.

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Iterated local search for the team orienteering problem with time windows

Cited by 285 publications

References 30 publications

Efficient Metaheuristics for the Mixed Team Orienteering Problem with Time Windows

Efficient Metaheuristics for the Mixed Team Orienteering Problem with Time Windows

Personalized Tourist Route Generation

Economic tour package model using heuristic

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