A fuzzy GRASP for the tourist trip design with clustered POIs

Expósito, Airam; Mancini, Susanna; Brito, Julio; Moreno, José A.

doi:10.1016/j.eswa.2019.03.004

Cited by 49 publications

(14 citation statements)

References 29 publications

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“…Then, the assignment of scores to each team member is made and finally, the construction of the routes is carried out (Chao et al, 1996 ). The Team Orienteering Problem with Time Window (TOPTW) is the most appropriate extension of the TOP to describe the TTDP because, generally, the POIs have an opening time window (Expósito et al, 2019a ; Gavalas et al, 2014 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several variants are considered when modeling real problems (Ruiz-Meza et al, 2020 ), such as time windows (Garcia et al, 2009 ; Souffiau et al, 2009 ), time-dependency (Garcia et al, 2010a , 2010b ), arc score (Arc Orienteering Problem -AOP) (Verbeeck et al, 2014 ), arc and POI scores (Mixed OP-AOP) (Gavalas et al, , 2016 , 2017 ; Malucelli et al, 2015 ), multi-commodity (Malucelli et al, 2015 ), multiple periods (Kotiloglu et al, 2017 ), time-based user interest (Lim et al, 2017 ) multiple constraints (Sylejmani et al, 2012 ), heterogeneous preferences (Malucelli et al, 2015 ; Zheng & Liao, 2019 ), uncertain travel times (Hasuike et al, 2013 ), previous tourist experiences (Zheng et al, 2017 ), time-dependent stochasticity (Liao & Zheng, 2018 ), hotel selection (Zheng et al, 2020 ), electric vehicles (EV) for tourism (Wang et al, 2018 ), scores and travel fuzzy times (Expósito et al, 2019a , 2019b ), and Clustered POIs, when POIs of different types of tourism are identified and grouped according to their class (Expósito et al, 2019a ).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Tourist trip design with heterogeneous preferences, transport mode selection and environmental considerations

Ruiz-Meza

Montoya‐Torres

2021

Ann Oper Res

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Tourism is one of the fastest-growing sectors in the world with a shift from mass tourism to personalized travel. Nevertheless, it generates significant environmental impacts. The current events associated with quarantine measures generated by COVID-19 represent, however, a risk for this sector. It is hence necessary to create strategies that allow efficient decision-making for all echelons and actors for a rapid recovery. Tourists are key actors, which makes necessary to facilitate tourism trip planning according to tourists’ preferences as a complex process. In this paper, we propose a novel model of tourist trip planning for heterogeneous preferences in a tourist group and selection of transport modes, in the first instance, while a second step seeks at minimizing the level of CO 2 emissions. A comparison of the two models is made considering the objectives associated with individual tourist benefits and group profit equity, in contrast to the inclusion of the cost of CO 2 emissions. A numerical comparison is carried out with a total of 546 data sets. Results illustrate the conflict between those objectives by generating an inverse relationship between the individual and group profit equity of tourists, in addition to individual benefit and emission minimization.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Tourist trip design with heterogeneous preferences, transport mode selection and environmental considerations

Ruiz-Meza

Montoya‐Torres

2021

Ann Oper Res

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“…Brito et al [24] consider the benefit of each POI as a fuzzy parameter, modelling the problem as a TOPTW and solve it with a fuzzy greedy randomised adaptive search procedures (FuzzyGRASP). Expósito et al [83] and Expósito et al [25] develop a TTDP with clustered POIs (TTDP-Clu) with the objective function and fuzzy constraints, which is then solved with a FuzzyGRASP.…”

Section: Fuzzy Approach To Ttdpmentioning

confidence: 99%

“…In the literature, this problem is called the tourist trip design problem (TTDP) and is generally modelled as an orienteering problem (OP) or a team orienteering problem (TOP) [23,24]. The TTDP aims to design a travel itinerary for tourists maximising their benefits without exceeding a budget [25]. Itinerary planning can include transportation, visiting hours, budget, and maximum duration of the trip [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Fuzzy Tourist Trip Design Problem with Heterogeneous Preferences and Sustainable Itineraries

2021

Self Cite

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Tourism has direct and indirect implications for CO2 emissions. Therefore, it is necessary to develop tourism management based on sustainable tourism, mainly in the transport process. Tourist itinerary planning is a complex process that plays a crucial role in tourist management. This type of problem, called the tourist trip design problem, aims to build personalised itineraries. However, planning tends to be biased towards group travel with heterogeneous preferences. Additionally, much of the information needed for planning is vague and imprecise. In this paper, a new model for tourist route planning is developed to minimise CO2 emissions from transportation and generate an equitable profit for tourists. In addition, the model also plans group routes with heterogeneous preferences, selects transport modes, and addresses uncertainty from fuzzy optimisation. A set of numerical tests was carried out with theoretical and real-world instances. The experimentation develops different scenarios to compare the results obtained by the model and analyse the relationship between the objectives. The results demonstrate the influence of the objectives on the solutions, the direct and inverse relationships between objectives, and the fuzzy nature of the problem.

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“…In recent years, China has been expanding domestic demand and promoting the service industry. This is a mixed blessing for the further development of tourism [1][2][3][4]. Faced with changing market, emerging conflicts, and growing demand for high-quality management, the tourism industry in China needs to undertake several challenging tasks: optimizing the industrial structure, renovating the growth mode, and improving development quality.…”

Section: Introductionmentioning

confidence: 99%

A Tourist Flow Prediction Model for Scenic Areas Based on Particle Swarm Optimization of Neural Network

Chen¹,

Liang²

2020

RIA

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In recent years, China has been expanding domestic demand and promoting the service industry. This is a mixed blessing for the further development of tourism. To make accurate prediction of tourist flow, this paper proposes a tourist flow prediction model for scenic areas based on the particle swarm optimization (PSO) of neural network (NN). Firstly, a system of influencing factors was constructed for the tourist flow in scenic areas, and the factors with low relevance were eliminated through grey correlation analysis (GCA). Next, the long short-term memory (LSTM) NN was optimized with adaptive PSO, and used to establish the tourist flow prediction model for scenic areas. After that, the workflow of the proposed model was introduced in details. Experimental results show that the proposed model can effectively predict the tourist flow in scenic areas, and provide a desirable prediction tool for other fields.

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A fuzzy GRASP for the tourist trip design with clustered POIs

Cited by 49 publications

References 29 publications

Tourist trip design with heterogeneous preferences, transport mode selection and environmental considerations

Tourist trip design with heterogeneous preferences, transport mode selection and environmental considerations

Multi-Objective Fuzzy Tourist Trip Design Problem with Heterogeneous Preferences and Sustainable Itineraries

A Tourist Flow Prediction Model for Scenic Areas Based on Particle Swarm Optimization of Neural Network

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