×

New multi-objective optimization model for tourism systems with fuzzy data and new algorithm for solving this model. (English) Zbl 07603430


MSC:

90Bxx Operations research and management science
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Frias, A., Cabral, J., Costa, Á.: Logistic optimization in tourism networks. In: 55th European Regional Science Association Congress, Lisbon, Portugal (2015). doi:10.13140/RG.2.1.1617.0967
[2] Baggio, R., Symptoms of complexity in a tourism system, Tour. Anal., 13, 1, 1-20 (2008) · doi:10.3727/108354208784548797
[3] Baldacchino, G., Trains of Thought: Railways as Island Anthitheses, Int. J. Res. Island Cult., 2, 1, 29-40 (2008)
[4] Bansal, H.; Eiselt, H., Exploratory research of tourist motivations and planning, Tour. Manage., 25, 3, 387-396 (2004) · doi:10.1016/S0261-5177(03)00135-3
[5] Jariyachamsit, S., An investigation of safety in tourism: An experience of young tourists in bangkok, Thailand, Procedia Soc. Behav. Sci., 197, 1931-1935 (2015) · doi:10.1016/j.sbspro.2015.07.571
[6] Abbaspour, RA; Samadzadegan, F., Time-dependent personal tour planning and scheduling in metropolises, Expert Syst. Appl., 38, 10, 12439-12452 (2011) · doi:10.1016/j.eswa.2011.04.025
[7] Jigang, B.; Yifang, C., Tourism geography in China, 1978-2008: Whence, what and whither?, Prog. Hum. Geogr., 35, 1 (1999)
[8] Calver, SJ; Page, SJ, Enlightened hedonism: exploring the relationship of service value, visitor knowledge and interest, to visitor enjoyment at heritage attractions, Tour. Manage., 39, 23-36 (2013) · doi:10.1016/j.tourman.2013.03.008
[9] McElroy, JL; Tarlow, P.; Carlisle, K., Tourist harassment: review of the literature and destination responses, Int. J. Cult. Tour. Hosp. Res., 1, 4, 305-314 (2007) · doi:10.1108/17506180710824190
[10] Souffriau, W.; Vansteenwegen, P.; Vertommen, J.; Berghe, GV; Oudheusden, DV, A personalized tourist trip design algorithm for mobile tourist guides, Appl. Artif. Intell., 22, 10, 964-985 (2008) · doi:10.1080/08839510802379626
[11] Király, A.; Abonyi, J., Optimization of multiple traveling salesmen problem by a novel representation based genetic, Intell. Comput. Optim. Eng., 366, 241-269 (2011)
[12] Cormen, TH; Leiserson, CE; Rivest, RL; Stein, C., Introduction to algorithms (2001), Cambridge: MIT Press, Cambridge · Zbl 1047.68161
[13] Jaillet, P.; Lu, X., Online traveling salesman problems with service flexibility, Networks, 58, 2, 137-146 (2011) · Zbl 1229.90164 · doi:10.1002/net.20454
[14] Yu, J., Aslam, J., Karaman, S., Rus, D.: Anytime planning of optimal schedules for a mobile sensing robot. Int. Conf. Intell. Robots Syst (IROS) (2015)
[15] Zimmermann, H-J, Fuzzy programming and linear programming with several objective functions, Fuzzy Sets Syst., 1, 1, 45-55 (1978) · Zbl 0364.90065 · doi:10.1016/0165-0114(78)90031-3
[16] Cadenas, JM; Verdegay, JL, Using ranking functions in multi-objective fuzzy linear programming, Fuzzy Sets Syst., 111, 47-53 (2000) · Zbl 0952.90049 · doi:10.1016/S0165-0114(98)00451-5
[17] Chen, L., Multiobjective design optimization based on satisfaction metrics, Eng. Optim., 33, 601-617 (2001) · doi:10.1080/03052150108940935
[18] Coello, CA; Christiansen, AD, Multiobjective optimization of trusses using genetic algorithms, Comput. Struct., 75, 6, 647-660 (2000) · doi:10.1016/S0045-7949(99)00110-8
[19] Saffarian, S.; Mahmoudi, A.; Shafiee, M.; Jasemi, M.; Hashemi, L., Measuring the effectiveness of AHP and fuzzy AHP models in environmental risk assessment of a gas power plant, Hum. Ecol. Risk Assess., 27, 5, 1227-1241 (2020) · doi:10.1080/10807039.2020.1816809
[20] Mehrjoo, Sh; Jasemi, M.; Mahmoudi, A., A new methodology for deriving the efficient frontier of stocks portfolios: An advanced risk-return model, J. Artif. Intell. Data Min., 2, 2, 113-123 (2014)
[21] Jasemi, M.; Mahmoudi, A.; Jahanbin, M.; Piri, M., An innovative methodology to make a questionnaire “positive definite” by the statistical software of Spss, Middle East J. Sci. Res., 13, 9, 1267-1274 (2013)
[22] Pirouz, B.; Khorram, E., A computational approach based on the epsilon-constraint method in multi-objective optimization problems, Adv. Appl. Stat., 49, 6, 453-483 (2016) · Zbl 1357.90139
[23] Hashemi, L.; Mahmoodi, A.; Jasemi, M.; Millar, R.; Laliberté, J., Modeling a robust multi-objective locating-routing problem with bounded delivery time using meta-heuristic algorithms, Smart Resilient Transport., 3, 3, 2632-487 (2021) · doi:10.1108/SRT-08-2021-0008
[24] Ahmadi, E.; Jasemi, M.; Monplaisir, L.; Nabavi, MA; Mahmoodi, A.; Amini, P., New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition, Expert Syst. Appl., 94, 15, 21-31 (2018) · doi:10.1016/j.eswa.2017.10.023
[25] Mahmoudi, A.; Hashemi, L.; Jasemi, M.; Pope, J., A comparison on particle swarm optimization and genetic algorithm performances in deriving the efficient frontier of stocks portfolios based on a mean-lower partial moment model, Int. J. Financ. Econ., 26, 4, 5659-5665 (2021) · doi:10.1002/ijfe.2086
[26] Rizk Allah, RM; Abo Sinna, MA, A comparative study of two optimization approaches for solving bi-level multi-objective linear fractional programming problem, Opsearch, 58, 374-402 (2021) · Zbl 07498638 · doi:10.1007/s12597-020-00486-1
[27] Parsaei Motamed, M.; Bamdad, S., A multi-objective optimization approach for selecting risk response actions: considering environmental and secondary risks, Opsearch, 59, 266-303 (2022) · Zbl 07549133 · doi:10.1007/s12597-021-00541-5
[28] Ehrgott, M., Multicriteria Optimization (2005), Berlin: Springer, Berlin · Zbl 1132.90001
[29] Mahmoudi, F.; Nasseri, S., A new approach to solve fully fuzzy linear programming problem, J. Appl. Res. Ind. Eng., 6, 2, 139-149 (2019)
[30] Nasseri, S., A New Approach to Solve Fully Fuzzy Linear Programming with Trapezoidal Numbers Using Conversion Functions, J. New Res. Math., 1, 3, 19-28 (2015)
[31] Wei, C.; Chien, F.; Wang, MJ, An AHP-based approach to ERP system selection, Int. J. Prod. Econ., 96, 47-62 (2005) · doi:10.1016/j.ijpe.2004.03.004
[32] Mansoursamaei, M.; Hadighi, A.; Javadian, N., A New Approach Applying Multi objective Optimization using a Taguchi Fuzzy-based for Tourist Satisfaction Management, Int. J. Eng., 32, 3, 405-412 (2019)
[33] Kumar, PS, A simple method for solving type-2 and type-4 fuzzy transportation problems, Int. J. Fuzzy Logic Intell. Syst., 16, 4, 225-237 (2016) · doi:10.5391/IJFIS.2016.16.4.225
[34] Kumar, P.S.: PSK method for solving type-1 and type-3 fuzzy transportation problems. In: Management Association, I. (eds.) Fuzzy Systems: Concepts, Methodologies, Tools, and Applications, IGI Global, pp. 367-392 (2017). doi:10.4018/978-1-5225-1908-9.ch017
[35] Ezzati, R.; Khorram, E.; Enayati, R., A new algorithm to solve fully fuzzy linear programming problems using the MOLP problem, Appl. Math. Model., 39, 12, 3183-3193 (2015) · Zbl 1443.90339 · doi:10.1016/j.apm.2013.03.014
[36] Nasseri, SH; Mahmoudi, F., A New Approach to Solve Fully Fuzzy Linear Programming Problems Using the MOLP Problem, Int. J. Appl. Optim. Stud., 2, 3, 65-72 (2019)
[37] Pirouz, B., Ramezani Paschapari, J.: A computational algorithm based on normalization for constructing the pareto front of multiobjective optimization problems. In: 5th International Conference on Industrial and Systems Engineering, Mashhad, Iran (2019)
[38] Shukla, PK, On the normal boundary intersection method for generation of efficient front, Comput. Sci., 4487, 310-317 (2007)
[39] Messac, A.; Ismail-Yahaya, A.; Mattson, CA, The normalized normal constraint method for generating the Pareto frontier, Struct. Multidiscip. Optim., 25, 86-98 (2003) · Zbl 1243.90200 · doi:10.1007/s00158-002-0276-1
[40] Pinter, JD; Linder, D.; Chin, P., Global optimization toolbox for maple: An introduction with illustrative applications, Optim. Methods Softw., 21, 4, 565-582 (2006) · Zbl 1113.90125 · doi:10.1080/10556780600628212
[41] Khorram, E.; Khaledian, K.; Khaledyan, M., A numerical method for constructing the Pareto front of multi-objective optimization problems, J. Comput. Appl. Math., 261, 2014, 158-171 (2014) · Zbl 1278.90359 · doi:10.1016/j.cam.2013.11.007
[42] Meng, H.; Zhang, X.; Liu, S., New quality measures for multiobjective programming. Springer, Lect. Notes Comput. Sci., 3611, 1044-1048 (2005) · doi:10.1007/11539117_143
[43] Benson, HP; Sun, E., Outcome space partition of the weight set in multiobjective linear programming, J. Optim. Theory Appl., 105, 17-36 (1978) · Zbl 1028.90051 · doi:10.1023/A:1004605810296
[44] Haimes, YY; Lasdon, LS; Wismer, DA, On a bicriterion formulation of the problems of integrated system identification and system optimization, IEEE Trans. Syst. Man Cybern., 1, 296-297 (1971) · Zbl 0224.93016
[45] Guddat, J.; Guerra Vasquez, F.; Tammer, K.; Wendler, K., Multiobjective and Stochastic Optimization Based on Parametric Optimization (1985), Berlin: Akademie-Verlag, Berlin · Zbl 0583.90055
[46] Koski, J.: Multicriteria truss optimization. In: Stadler, W. (ed.) Multicriteria Optimization in Engineering and in the Sciences. vol. 37, pp. 263-307 (1988) · Zbl 0678.73058
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.