The urban tourism market has been increasingly dominated by the demand for personalized experiences. This paper focuses on the multiday urban personalized trip design problem considering the time windows and transportation mode recommendations (MUPTDP-TW-TMR). A two-stage methodology is proposed to solve this problem. In the first stage, a linear programming model for the multiday urban personalized trip design problem with time windows (MUPTDP-TW) is proposed. A modified memetic algorithm (MMA) is devised to solve it, containing efficient operators. In the second stage, a novel transportation mode recommendation model is developed, which considers the diversity of tourist preferences. A modified label-setting algorithm is applied to solve the model. The data from Chongqing, the most popular city in Southwest China, are collected to evaluate the performance of our approach. The case study based on tourist and popular preferences is conducted. Compared with the state-of-the-art algorithms, the experimental results show that our approach can design more reasonable and personalized itineraries for tourists.
AshworthG.PageS. J.Urban Tourism Research: Recent Progress and Current Paradoxes. Tourism Management, Vol. 32, No. 1, 2011, pp. 1–15.
2.
VansteenwegenP.SouffriauW.BergheG. V.OudheusdenD. V.The City Trip Planner: An Expert System for Tourists. Expert Systems With Applications, Vol. 38, No. 6, 2011, pp. 6540–6654.
3.
ZhengW.JiH.LinC.WangW.YuB.Using a Heuristic Approach to Design Personalized Urban Tourism Itineraries With Hotel Selection. Tourism Management, Vol. 76, 2020, p.103956.
4.
CastilloL.ArmengolE.OnaindíaE.SebastiáL.González-BoticarioJ.RodríguezA.FernándezS.AriasJ. D.BorrajoD.SAMAP: An User-Oriented Adaptive System for Planning Tourist Visits. Expert Systems With Applications, Vol. 34, No. 2, 2008, pp. 1318–1332.
5.
BorràsJ.MorenoA.VallsA.Intelligent Tourism Recommender Systems: A Survey. Expert Systems With Applications, Vol. 41, No. 16, 2014, pp. 7370–7389.
6.
CenamorI.de la RosaT.NúñezS.BorrajoD.Planning for Tourism Routes Using Social Networks. Expert Systems With Applications, Vol. 69, 2017, No. 1–9.
7.
VuD. M.KergosienY.MendozaJ. E.DesportP.Branch-and-Check Approaches for the Tourist Trip Design Problem With Rich Constraints. Computers & Operations Research, Vol. 138, 2022, p. 105566.
GaoM.LiuK.WuZ.Personalisation in Web Computing and Informatics: Theories, Techniques, Applications, and Future Research. Information Systems Frontiers, Vol. 12, No. 5, 2009, pp. 607–629.
10.
TliliT.KrichenS.A Simulated Annealing-Based Recommender System for Solving the Tourist Trip Design Problem. Expert Systems With Applications, Vol. 186, 2021, p. 115723.
11.
MrazovicP.Larriba-PeyJ. L.MatskinM.Improving Mobility in Smart Cities With Intelligent Tourist Trip Planning. Proc., IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), IEEE, New York, 2017, pp. 897–907.
12.
Da SilvaA. A.MorabitoR.PurezaV.Optimization Approaches to Support the Planning and Analysis of Travel Itineraries. Expert Systems With Applications, Vol. 112, 2018, pp. 321–330.
13.
TsiligiridesT.Heuristic Methods Applied to Orienteering. Journal of the Operational Research Society, Vol. 35, No. 9, 1984, pp. 797–809.
14.
GendreauM.LaporteG.SemetF.A Branch and Cut Algorithm for the Undirected Selective Traveling Salesman Problem. Networks, Vol. 32, 1998, pp. 263–273.
15.
VansteenwegenP.Van OudheusdenD. . The Mobile Tourist Guide: An OR Opportunity. OR Insight, Vol. 20, 2007, pp. 21–27.
16.
ZografosK. G.AndroutsopoulosK. N.Algorithms for Itinerary Planning in Multimodal Transportation Networks. IEEE Transactions on Intelligent Transportation Systems, Vol. 9, No. 1, 2008, pp. 175–184.
17.
AbbaspourR. A.SamadzadeganF.Time-Dependent Personal Tour Planning and Scheduling in Metropolises. Expert Systems With Applications, Vol. 38, No. 10, 2011, pp. 12439–12452.
18.
GavalasD.KasapakisV.KonstantopoulosC.PantziouG.VathisN.ZaroliagisC.The eCOMPASS Multimodal Tourist Tour Planner. Expert Systems With Applications, Vol. 42, No. 21, 2015, pp. 7303–7316.
19.
DivsalarA.VansteenwegenP.CattrysseD.A Variable Neighborhood Search Method for the Orienteering Problem With Hotel Selection. International Journal of Production Economics, Vol. 145, No. 1, 2013, pp. 150–160.
20.
SohrabiS.ZiaratiK.KeshtkaranM.A Greedy Randomized Adaptive Search Procedure for the Orienteering Problem With Hotel Selection. European Journal of Operational Research, Vol. 283, No. 2, 2020, pp. 426–440.
21.
SohrabiS.ZiaratiK.KeshtkaranM.ACS-OPHS: Ant Colony System for the Orienteering Problem With Hotel Selection. EURO Journal on Transportation and Logistics, Vol. 10, 2021, p. 100036.
22.
JandaghiH.DivsalarA.EmamiS.The Categorized Orienteering Problem With Count-Dependent Profits. Applied Soft Computing, Vol. 113, 2021, p. 107962.
23.
Ruiz-MezaJ.Montoya-TorresJ. R.A Systematic Literature Review for the Tourist Trip Design Problem: Extensions, Solution Techniques and Future Research Lines. Operations Research Perspectives, Vol. 9, 2022, p. 100228.
24.
DivsalarA.VansteenwegenP.ChitsazM.SörensenK.CattrysseD., eds. Personalized Multi-Day Trips to Touristic Regions: A Hybrid GA-VND Approach. Proc., European Conference on Evolutionary Computation in Combinatorial Optimization, Springer, Berlin, Heidelberg, 2014, pp. 194–205.
25.
KhodadadianM.DivsalarA.VerbeeckC.GunawanA.VansteenwegenP.Time Dependent Orienteering Problem With Time Windows and Service Time Dependent Profits. Computers & Operations Research, Vol. 143, 2022, p. 105794.
26.
DivsalarA.VansteenwegenP.SörensenK.CattrysseD.A Memetic Algorithm for the Orienteering Problem With Hotel Selection. European Journal of Operational Research, Vol. 237, No. 1, 2014, pp. 29–49.
27.
HeinenE.ChatterjeeK.The Same Mode Again? An Exploration of Mode Choice Variability in Great Britain Using the National Travel Survey. Transportation Research Part A: Policy and Practice, Vol. 78, 2015, pp. 266–282.
28.
ArroyoR.RuizT.MarsL.RasouliS.TimmermansH.Influence of Values, Attitudes Towards Transport Modes and Companions on Travel Behavior. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 71, 2020, pp. 8–22.
29.
DėdelėA.MiškinytėA.AndrušaitytėS.Nemaniūtė-GužienėJ. Dependence Between Travel Distance, Individual Socioeconomic and Health-Related Characteristics, and the Choice of the Travel Mode: a Cross-Sectional Study for Kaunas, Lithuania. Journal of Transport Geography, Vol. 86, 2020, p. 102762.
30.
PengZ.-R.HuangR.Design and Development of Interactive Trip Planning for Web-Based Transit Information Systems. Transportation Research Part C: Emerging Technologies, Vol. 8, No. 1, 2000, pp. 409–425.
31.
DijkstraE. W.A Note on Two Problems in Connexion With Graphs. Numerical Mathematics, Vol. 1, No. 1, 1959, pp. 269–271.
32.
CashinR. D.LiuP.ChenJ. X.Deploying the Advanced Public Transportation Systems (APTS) Technologies in Los Angeles: A Summary and Analysis. Journal of Advanced Transportation, Vol. 36, No. 1, 2010, pp. 107–129.
33.
MizarasV.AntoniouC.PachinisT.GoutzikasT., eds. Innovative Real Time Multi-Modal Passenger Information System for the Attika Region. Presented at the 15th World Congress on Intelligent Transport Systems and ITS America 2008 Annual Meeting, New York, NY, ITS America,Washington, D.C., 2008.
34.
Ruiz-MezaJ.BritoJ.Montoya-TorresJ. R.A GRASP to Solve the Multi-Constraints Multi-Modal Team Orienteering Problem With Time Windows for Groups With Heterogeneous Preferences. Computers & Industrial Engineering, Vol. 162, 2021, p. 107776.
35.
DivsalarG.DivsalarA.JabbarzadehA.SahebiH.An Optimization Approach for Green Tourist Trip Design. Soft Computing, Vol. 26, No. 9, 2022, pp. 4303–4332.
36.
ZhengW.LiaoZ.Using a Heuristic Approach to Design Personalized Tour Routes for Heterogeneous Tourist Groups. Tourism Management, Vol. 72, 2019, pp. 313–325.
37.
KargarM.LinZ.A Socially Motivating and Environmentally Friendly Tour Recommendation Framework for Tourist Groups. Expert Systems With Applications, Vol. 180, 2021, p. 115083.
38.
Ruiz-MezaJ.Montoya-TorresJ. R.Tourist Trip Design With Heterogeneous Preferences, Transport Mode Selection and Environmental Considerations. Annals of Operations Research, Vol. 305, No. 1, 2021, pp. 227–249.
39.
Ruiz-MezaJ.BritoJ.Montoya-TorresJ. R.Multi-Objective Fuzzy Tourist Trip Design Problem With Heterogeneous Preferences and Sustainable Itineraries. Sustainability, Vol. 13, No. 17, 2021, p. 9771.
40.
TrachanatziD.RigakisM.MarinakiM.MarinakisY.An Interactive Preference-Guided Firefly Algorithm for Personalized Tourist Itineraries. Expert Systems With Applications, Vol. 159, 2020, p. 113563.
41.
RigakisM.TrachanatziD.MarinakiM.MarinakisY.Tourist Group Itinerary Design: When the Firefly Algorithm Meets the n-Person Battle of Sexes. Knowledge-Based Systems, Vol. 228, 2021, p. 107257.
42.
LiaoZ.ZhangX.ZhangQ.ZhengW.LiW.Rough Approximation-Based Approach for Designing a Personalized Tour Route Under a Fuzzy Environment. Information Sciences, Vol. 575, 2021, pp. 338–354.
43.
LiaoZ.ZhengW.Using a Heuristic Algorithm to Design a Personalized Day Tour Route in a Time-Dependent Stochastic Environment. Tourism Management, Vol. 68, 2018, pp. 284–300.
44.
PěničkaR.FaiglJ.SaskaM.Variable Neighborhood Search for the Set Orienteering Problem and its Application to Other Orienteering Problem Variants. European Journal of Operational Research, Vol. 276, No. 3, 2019, pp. 816–825.
45.
DreyfusS.An Appraisal of Some Shortest Path Algorithms. Operations Research, Vol. 17, No. 3, 1969, pp. 395–412.
46.
CherkasskyB. V.GoldbergA. V.RadzikT. A.Shortest Paths Algorithms: Theory and Experimental Evaluation. Mathematical Programming, Vol. 73, 1996, pp. 129–174
47.
BellmanR.On a Routing Problem. Quarterly of Applied Mathematics, Vol. 16, No. 1, 1958, pp. 87–90.
48.
ZhanF. B.NoonC. E.Shortest Path Algorithms: An Evaluation Using Real Road Networks. Transportation Science, Vol. 32, No. 1, 1998, pp. 65–73.
49.
PapeU.Implementation and Efficiency of Moore-Algorithms for the Shortest Route Problem. Mathematical Programming, Vol. 7, No. 1, 1974, pp. 212–222.
50.
PallottinoS.Shortest-Path Methods: Complexity, Interrelations, and New Propositions. Networks, Vol. 14, 1984, pp. 257–267.
51.
DongM.WuC.HouF.Shortest Path Based Simulated Annealing Algorithm for Dynamic Facility Layout Problem Under Dynamic Business Environment. Expert Systems With Applications, Vol. 36, No. 8, 2009, pp. 11221–11232.
52.
ZhangX.ZhangY.HuY.DengY.MahadevanS.An Adaptive Amoeba Algorithm for Constrained Shortest Paths. Expert Systems With Applications, Vol. 40, No. 18, 2013, pp. 7607–7616.
53.
ChenP.TongR.YuB.WangY.Reliable Shortest Path Finding in Stochastic Time-Dependent Road Network With Spatial-Temporal Link Correlations: A Case Study From Beijing. Expert Systems With Applications, Vol. 147, 2020, p. 113192.
54.
YadegariE.Alem-TabrizA.ZandiehM.A Memetic Algorithm With a Novel Neighborhood Search and Modified Solution Representation for Closed-Loop Supply Chain Network Design. Computers & Industrial Engineering, Vol. 128, 2019, pp. 418–436.
55.
YangX.BostelN.DejaxP.A MILP Model and Memetic Algorithm for the Hub Location and Routing Problem With Distinct Collection and Delivery Tours. Computers & Industrial Engineering, Vol. 135, 2019, pp. 105–119.
56.
LeiC.ZhaoN.YeS.WuX.Memetic Algorithm for Solving Flexible Flow-Shop Scheduling Problems With Dynamic Transport Waiting Times. Computers & Industrial Engineering, Vol. 139, 2020, p. 105984.
57.
PengB.ZhangY.LüZ.ChengT. C. E.GloverF.A Learning-Based Memetic Algorithm for the Multiple Vehicle Pickup and Delivery Problem With LIFO Loading. Computers & Industrial Engineering, Vol. 142, 2020, p. 106241.
58.
YağmurE.KesenS. E.A Memetic Algorithm for Joint Production and Distribution Scheduling With Due Dates. Computers & Industrial Engineering, Vol. 142, 2020, p. 106342.
59.
PendharkarP. C.KoehlerG. J.A General Steady State Distribution Based Stopping Criteria for Finite Length Genetic Algorithms. European Journal of Operational Research, Vol. 176, No. 3, 2007, pp. 1436–1451.
