Multi-Objective Trip Planning With Solution Ranking Based on User Preference and Restaurant Selection

Abstract

The tourist trip design problem (TTDP) helps the trip planners, such as tourists, tour companies, and government agencies, automate their trip planning. TTDP solver chooses and sequences an optimal subset of point of interest (POIs), which adhere to the POIs attributes and tourist preferences, and then generates a travel itinerary that maximizes their pleasure. However, the traditional TTDP does not include the lunch period at a local restaurant, which causes the rest of the itinerary in the afternoon to shift, nor compulsory POIs that the trip planners must be included in the itinerary. Moreover, as tourism contributes to high greenhouse gas emissions, especially from its transportation, minimizing the itinerary's total distance is also considered. Unfortunately, this objective conflicts with the profit scores; no single itinerary can optimize both objectives simultaneously. Hence, the multi-objective technique and the results of non-dominated itineraries can be organized as a Pareto front. The trip planners can choose one suitable itinerary from the Pareto front based on their preferences. To address these real-world issues, we formulate a new variant of the well-known orienteering problem with time windows (OPTW) called the multi-objective orienteering problem with Time Windows, Restaurant Selection, and Compulsory POIs (MOPTW-RSCP). The proposed problem is provided with a mathematical formulation and two exact algorithms for solving them, i.e., greedy and branch-and-cut Pareto-based techniques. The algorithms' performance is tested against the Rattanakosin island (the old city of Bangkok) dataset. We conduct 24 test cases, and the computational results confirm the algorithms' efficiency.INDEX TERMS branch-and-cut algorithm, greedy algorithm, knee solution, low carbon tourism, multiobjective optimization, orienteering with time windows, point-of-interests, recommendation, solution ranking, tourist trip design problem

Multicriteria Decision Making in Sustainable Tourism and Low-Carbon Tourism Research

“…This third category includes the following methods: Principal Components Analysis (PCA) (Işik & Demir, 2017;Michalena et al, 2009); Linguistic multi-attribute group decision making (MAGDM) (Lin & Wang, 2017); Dive Site Risk Assessment Model (DSRAM), which combines AHP, Fuzzy set, and Evidential Reasoning (FER) (Anuar et al, 2020); Intuitionistic fuzzy preference relations (IFPRs) (Yang & Wang, 2020); Intuitionistic multiplicative UTAS-TAR method (IM-UTASTAR) ; and a thermodynamic feature-based method, the q-rung ortho-pair fuzzy set (q-ROFS) . We also highlight the use of some relatively new method combinations from two different articles by the same primary author, which covers Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Higher-Order Singular Value Decomposition (HOSVD), Self-Organizing Map (SOM), and Classification and Regression Tree Analysis (CART) (Nilashi et al, 2019) as well as ANFIS with Support Vector Machine (SVM) and Naïve Bayes, Support Vector Regression (SVR), Neural Network (NN), and Entropy weight method (EWM) (Nilashi et al, 2021); a Pareto-based method -the multi-objective orienteering problem with Time Windows, Restaurant Selection, and Compulsory POIs (MOPTW-RSCP) (Choachaicharoenkul et al, 2022) -and an unnamed method using compensatory and non-compensatory types of aggregation procedures with the Condorcet-and the Borda-type approach used to obtain a composite indicator value (Blancas & Lozano-Oyola, 2022); and lastly, a developed and generalized sustainable evaluation criteria system based on the effect of favourability (Pomucz & Csete, 2015).…”

Optimizing Task Completion Time in Disaster-Affected Regions with the WMDDPG-GSA Algorithm for UAV-Assisted MEC Systems

“…In [27], a state-transition-simulated annealing algorithm was proposed to find the shortest path for multi-criteria journey planning. Considering the different interests tourists have in different attractions, a greedy algorithm was used to minimize the total distance of the journey [28]. The above algorithms can be used as offloading strategies in this study, but the simulated annealing algorithm may have the problem of not finding the optimal solution, while the greedy algorithm may fall into the issue of local optima.…”

Analysis of Emotional Tendency of Tourism Consumers Based on LSTM and Fuzzy Control Algorithm

“…The proposed model provides a feasible solution to solve issues in the environmental management of tourism destinations. Choachaicharoenkul et al [18] developed a multi-objective trip planning method based on user preference and restaurant selection for tourist trip design problems (TTDP). The main aim of the method is to maximize the feasibility and significance range of the systems.…”