A memetic algorithm for the orienteering problem with hotel selection

Divsalar, Ali; Vansteenwegen, Pieter; Sörensen, Kenneth; Cattrysse, Dirk

doi:10.1016/j.ejor.2014.01.001

Cited by 56 publications

(26 citation statements)

References 30 publications

(37 reference statements)

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“…The OP with hotel selection introduced by Divsalar, Vansteenwegen, and Cattrysse [38] belongs to this class and is aimed at determining a fixed number of connected trips starting and ending in one of the hotels. A memetic algorithm was proposed for this problem by Divsalar et al [39]. An extended survey on the class of TTDPs is presented in Gavalas et al [51].…”

Section: Variants Of the Team Orienteering Problemmentioning

confidence: 98%

Chapter 10: Vehicle Routing Problems with Profits

Archetti¹,

Speranza²,

Vigo³

2014

Vehicle Routing

117

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The key characteristic of the class of Vehicle Routing Problems with Profits (VRPPs) is that, contrary to what happens for the most classical vehicle routing problems, the set of customers to serve is not given. Therefore, two different decisions have to be taken: (i) which customers to serve, and (ii) how to cluster the customers to be served in different routes (if more than one) and order the visits in each route. In general, a profit is associated with each customer that makes such a customer more or less attractive. Thus, any route or set of routes, starting and ending at a given depot, can be measured both in terms of cost and in terms of profit. The difference between route profit and cost may be maximized, or the profit or the cost optimized with the other measure bounded in a constraint.There are several types of applications that can be modeled by means of a problem of this class:• scheduling of the daily operations of a steel rolling mill (see, e.g., Balas [13, 16]);• design of tourist trips to maximize the value of the visited attractions in a limited period (see, e.g., Vansteenwegen and Van Oudheusden [109]);• identification of suppliers to visit to maximize the recovered claims with a limited number of auditors (see Ilhan, Iravani, and Daskin [61]);• recruiting of athletes from high schools for a college team (see Butt and Cavalier [24]);• planning of the visits of a salesperson (see, e.g., Ramesh and Brown [89]);• routing of oil tankers to serve ships at different locations (see Golden, Levy, and Vohra [57]);

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Section: Variants Of the Team Orienteering Problemmentioning

confidence: 98%

Chapter 10: Vehicle Routing Problems with Profits

Archetti¹,

Speranza²,

Vigo³

2014

Vehicle Routing

117

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“…Each tour is limited in length and should start and end in one of the hotels. Divsalar et al (2013Divsalar et al ( , 2014 propose Variable Neighborhood Search and Memetic algorithms for solving the OPHS.…”

Section: Related Workmentioning

confidence: 99%

Personalized multi-period tour recommendations

et al. 2017

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During a trip planning, tourists gather information from different sources, select and rank the places to visit according to their personal interests, and try to devise daily tours among them. This paper addresses the complex selection and touring problem and proposes a "filter-first, tour-second" framework for generating personalized tour recommendations for tourists based on information from social media and other online data sources. Collaborative filtering is applied to identify a subset of optional points of interest that maximize the potential satisfaction, while there are some preselected mandatory points that the tourists must visit. Next, the underlying orienteering problem is solved via an Iterated Tabu Search algorithm. The goal is to generate tours that contain all mandatory points and maximize the total score collected from the optional points visited daily, taking into account different day availabilities and opening hours, limitations on the tour lengths, budgets and other restrictions. Computational experiments on benchmark datasets indicate that the proposed touring algorithm is very competitive. Furthermore, the proposed framework has been evaluated on data collected from Foursquare. The results show the practical utility and the temporal efficacy of the recommended tours.

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“…An effective solution method for the MCPRP can be incorporated in such a framework to solve the routing problem more efficiently. If an orienteering problem approach is taken, this is similar to the orienteering problem with hotel selection (OPHS) of Divsalar, Vansteenwegen, and Cattrysse (2013); Divsalar, Vansteenwegen, Sörensen, and Cattrysse (2014) which considers a multi-period tourist trip planner application. Very recently, Çapar, Keskin, and Rubin (2015) reconsidered the MIP formulation for the MCPRP and also used a set of domination rules to greatly simplify the MIP formulation.…”

Section: Introductionmentioning

confidence: 99%

A minimum cost network flow model for the maximum covering and patrol routing problem

Dewil

Vansteenwegen

Cattrysse

et al. 2015

European Journal of Operational Research

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a b s t r a c tThis paper shows how the maximum covering and patrol routing problem (MCPRP) can be modeled as a minimum cost network flow problem (MCNFP). Based on the MCNFP model, all available benchmark instances of the MCPRP can be solved to optimality in less than 0.4s per instance. It is furthermore shown that several practical additions to the MCPRP, such as different start and end locations of patrol cars and overlapping shift durations can be modeled by a multi-commodity minimum cost network flow model and solved to optimality in acceptable computational times given the sizes of practical instances.

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A memetic algorithm for the orienteering problem with hotel selection

Cited by 56 publications

References 30 publications

Chapter 10: Vehicle Routing Problems with Profits

Chapter 10: Vehicle Routing Problems with Profits

Personalized multi-period tour recommendations

A minimum cost network flow model for the maximum covering and patrol routing problem

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