A fuzzy GRASP for the tourist trip design with clustered POIs

doi:10.1016/j.eswa.2019.03.004

Expert Systems with Applications

Volume 127, 1 August 2019, Pages 210-227

https://doi.org/10.1016/j.eswa.2019.03.004 Get rights and content

Highlights

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We present a new extension of the Tourist Trip Design Problem (TTDP).
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The TTDP with Clustered POIs (TTDP-Clu) have POIs are grouped in clusters.
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A MIP model is developed for TTDP-Clu.
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A Fuzzy GRASP metaheuristic is proposed to generate solutions of the problem.
•
The metaheuristic solutions are compared with MIP model.

Abstract

In tourist sector, expert and intelligent systems should perform at least two main tasks or services: point of interest recommendation and route generation. In this regard, the personalized electronic tourist guide, generally implemented on hand-held device, such as mobile applications or in web. These tools must work like an expert and intelligent system that perform the services mentioned above then they should need low computation effort. In this paper we focus on the route generation based on scores of the points of interest and the distance or time between them. We consider a new extension of the Tourist Trip Design Problem, named Tourist Trip Design Problem with Clustered Points of Interest, where points of interest are grouped in clusters representing different types of attraction sites. Moreover, minimum/maximum limits are imposed on the number of points of interest belonging to the each clusters that are visited in the same route. Since it is a novel problem, we generate two sets of instances in order to evaluate the accuracy of our solution approach. A Fuzzy GRASP (Greedy Randomized Adaptive Search Procedure), in which both distance based and score based evaluation criteria are used to guide the candidates selection in the construction phase is proposed. The results provided by our heuristic are compared with those obtained by solving the MIP formulation. Computational results carried out on real and real-like instances show the effectiveness and efficiency of the proposed approach and its suitability to be part of a Personalized Electronic Tourist Guide in hand-held devices.

Introduction

The Tourists visiting a touristic area, for one or several days, face the problem of selecting which points of interest (POIs) to visit and to design a route for each trip day. Before the tourist arrive to destination they have to spend time in compile information about POIs in destination in order to planning visits in the staying days. They have to consider the POIs to visit, their visit time, traveling times between POIs, and other information to planning their touristic route for each day of staying. This issue that taking into account the preferences of the tourist and several information about POIs is a time-consuming task.

In tourism industry the application of expert and intelligent systems play an important role in tourist satisfaction (Kabassi, 2010). Expert and intelligent systems are one of the prominent research domains of Artificial Intelligence and they are specially applicable to tourism services. Broadly, this systems include two main services; recommendation of point of interest and route generation. In tourist services, the Personalized Electronic Tourist Guides (PETs) is an integrated solution that performs two services discussed above on a hand-held device. An extensive review of PETs can be found in Vansteenwegen (2008a) and Souffriau (2010).

In this paper we focus on the task of route generation combining heuristic solution approach and a new problem of tourist routes. The planning of tourist routes is a challenging quest involving a number of issues such as visiting time required for each POI, POI’s opening time windows, traveling distance among POIs and time available for sightseeing for each day. Each POI is associated to a score, which represent its level of attractiveness and touristic interest. The basic version of the Tourist Trip Design Problem (TTDP) has been modeled in the literature as a Team Orienteering Problem with Time Windows (TOPTW) (Gavalas, Konstantopoulos, Mastakas, & Pantziou, 2014). However, real applications involve further several issues therefore to provide a more realistic representation of the reality, additional constraints need to be addressed. In this paper, we present the Tourist Trip Design Problem with Clustered POIs (TTDP-Clu), in which we consider that the set of POIs are clustered by categories representing different types of visiting sites (museum, amusement park, beach, restaurant, etc.). The goal is to define a set of feasible routes, one for each day of staying, that maximize the total score collected. The routes must start and end at a given starting point (which represent the hotel) and the duration of each route, (computed considering both travel, visit and waiting times) cannot exceed a maximum value. In addition, for each category the number of POIs that can be visited on a single day may be bounded or even fixed. For instance, for the category of restaurants for lunch the number of visits for each route must be exactly one, while for other categories we can have only one side limits.

Due to the computational complexity of TTDP we propose a metaheuristic approach for solve the aforementioned problem. Specifically we propose a fuzzy version of standard GRASP metaheuristic. The fuzzy GRASP proposed differs from standard GRASP for consider a fuzzy set of promising POIs to be include in the solution. The proposed metaheuristic is compared with the results obtained by solving the MIP formulation of the proposed problem.

The remainder of the present paper is organized as follows. Section 2 reviews the main models of the TTDP and their applications in the literature. Sections 3 and 4 is dedicated to the presentation of the problem formulation and associated objectives. A Fuzzy Greedy Randomized Adaptive Search Procedure (Fuzzy GRASP) metaheuristic for the resolution of the proposed problem is introduced in Section 5. The computational experiments are discussed and analyzed in Sections 6 and 7. Finally, the main conclusions and several lines for further research are provided in Section 8.

Section snippets

Literature review

The TTDP has becoming an interesting topic of research in the last years (Vansteenwegen & Van Oudheusden, 2007). Tourists requests and expectations have become higher than in the past. This information is used to plan their trip in details in order to exploit their time in the most productive way visiting the most interesting visiting sites. Travel agencies generally propose thematic predefined routes but tourists would like to construct their suited ad-hoc itinerary taking into account their

Problem description

In this section we report a detailed description of the TTDP-Clu. To each POI i is associated a score or profit p_i, a visit time v_i, an opening time window [e_i, l_i] and the category, c, to which it belongs. The index $i = 0$ denotes the starting and ending point that usually corresponds to the hotel, apartment or other kind of accommodation of the tourist. This point and the POIs are considered as the vertices or nodes of a connected graph. Each POI can be included in at most one route. The sets of

Mathematical formulation

The TTDP-Clu can be formulated as a Mixed-Integer Programming (MIP) as follows. Let us first define the following sets, parameters and variables.

Sets:

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C set of categories
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K set of routes
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I set of POIs
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I^c set of POIs belonging to category c
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I⁰ set of nodes ( $I = I^{0} ∖ {0})$

Parameters:

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p_i POI i score or profit, ∀i ∈ I
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t_ij travel time between point i and j, ∀i, j ∈ I⁰
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v_i visit time for POI i, ∀i ∈ I
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T_max maximum route duration, ∀k ∈ K
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[e_i, l_i] opening time windows for POI i, ∀i ∈ I
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$N_{c}^{\min}$ minimum number of POIs

Solution approach

In order to return high quality solutions for the defined optimization problem, a Fuzzy GRASP has been used. The standard GRASP is a two-phase metaheuristic, consisting of a construction phase and a local search improvement phase (Resende & Ribeiro, 2016). It is executed a number of maxIterations times in a multistart strategy and the best solution found is kept. The standard GRASP construction phase provides a feasible solution by iteratively randomly selecting a new POI from a Restricted

Computational experiments

The computational experiments carried out in our study are described in this section. The aim of the experiments is to evaluate the accuracy of the Fuzzy GRASP metaheuristic proposed to solve the newly introduced TTDP-Clu. Since this is the first time that a solution procedure is proposed for this problem, the results provided by our heuristic are compared with those obtained by solving the MIP formulation. Prior to the aforementioned computational experiments, a parameter analysis was

Results analysis

In this subsection we report a detailed comparison of computational results obtained by solving the mathematical model proposed in Section 4 and those obtained by running the Fuzzy GRASP. The mathematical model MIP is solved with the commercial software Xpress 7.9 with a timelimit of 1 h; i.e., 3600 s. The Fuzzy GRASP has been run on a machine equipped with a processor Intel Core i7-3610QM CPU at 2.30 GHz and 16 GB RAM while the MIP is executed on a machine equipped with a Intel I7-5500U at

Conclusions and further research

In this paper we have defined a new itineraries design problem arising in tourist trip planning operations. The problem, named TTDP-Clu, is an extension of the TTDP where POIs are grouped in clusters representing different types of attraction sites and a minimum/maximum limit is imposed on the number of POIs, belonging to the same cluster, that must/can be visited in the same route. This problem came out from specific needing arising in tourist trip planning operations, in which the current

Acknowledgment

This work has been partially funded by Ministerio de Economía y Competitividad (Spanish Government) with FEDER funds, grant TIN2015-70226-R, and by Fundación CajaCanarias (Ref. 2016TUR19). Airam Expósito-Márquez would like to thank the Agencia Canaria de Investigación, Innovación y Sociedad de la Información and the Fondo Social Europeo (FSE) for the financial support he receives through his post-graduate grant.

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View full text

A fuzzy GRASP for the tourist trip design with clustered POIs

Highlights

Abstract

Introduction

Section snippets

Literature review

Problem description

Mathematical formulation

Solution approach

Computational experiments

Results analysis

Conclusions and further research

Acknowledgment

Computers & Operations Research

European Journal of Operational Research

Swarm and Evolutionary Computation

Journal of the American Statistical Association

IET Software

Telematics and Informatics

European Journal of Operational Research

The International Journal of Advanced Manufacturing Technology

Expert Systems with Applications

Operational Research Insight

The capacitated team orienteering and profitable tour problems

Journal of the Operational Research Society

Metaheuristics for the team orienteering problem

Journal of Heuristics

Vehicle routing problems with profits

Tuning metaheuristics - A machine learning perspective

Approximation algorithms for orienteering and discounted-reward TSP

SIAM Journal on Computing

A memetic algorithm for the team orienteering problem

4OR: A Quarterly Journal of Operations Research

An exact algorithm for team orienteering problems

4OR: A Quarterly Journal of Operations Research