Tourist Trip Design Problem Research Articles

A systematic literature review for the tourist trip design problem: Extensions, solution techniques and future research lines

The tourism sector represents an opportunity for economic growth in countries with tourism potential. However, new trends in global tourism require efficiency in tourism supply chain management. One of the main changes is the trend of personalized tourism, which includes the management of itineraries by the tourist. The tourist trip design problem is the problem associated with personalized trip design. This problem has been modeled in the field of Operations Research in different ways, being the Orienteering Problem the most widely applied in the specialized literature. The objective of this paper is to carry out a systematic review of the contributions made to the tourist trip design problem. A review taxonomy is proposed to analyze the main variants, objectives, and solution techniques. Also, future research lines are analyzed and proposed to enrich the literature and provide new solutions to real problems.

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

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.

A new accessibility based team orienteering approach for urban tourism routes optimization (A Real Life Case)

One of the most crucial issues in the tourism industry that can help attract more tourists is to design optimal travel routes for tourists. The Team Orienteering Problem with Time Windows (TOPTW) is one of the approaches that can solve Tourist Trip Design Problem (TTDP). In this study, TOPTW has been applied to design a multi-objective model to determine the optimal travel routes, considering the accessibility. The present study aims 1) to maximize the profit from visiting the Points of Interest (POIs) to increase the satisfaction level of the travel agency, and 2) to maximize the number of visited POIs to improve the satisfaction level of the tourists. Two concepts of “indirect coverage” and “neighborhood radius” are applied to increase the accessibility of the tourist attractions and POIs. Thus, around each POI, a small radius and a large radius are considered, and if the points are in the radius coverage of the points located in the main routes, they can receive indirect coverage from those points. Also the time-window overlap issue for POIs is vital to determine the possibility of indirect coverage and makes the conceptual model of the research closer to reality. Also, several indicators are proposed for calculating accessibility. A multi-objective genetic algorithm (MOGA) is formulated to solve the studied problem, and the results obtained by the proposed algorithm are compared with those of CPLEX software (for small-scale) and NSGA-II algorithm (for large-scale). The findings indicate that the genetic algorithm outperforms the NSGA-II algorithm. A sensitivity analysis is also performed to evaluate the effects of the proposed model parameters on the optimization process. Finally, the model applicability in practice is examined based on a case study in Tehran, the capital of Iran.

A GRASP to solve the multi-constraints multi-modal team orienteering problem with time windows for groups with heterogeneous preferences

Improving the travel experience is a goal of tourist destinations. Tourists demand information and services that help plan and organise the trips adapted to their preferences and resources. Intelligent systems, recommendation systems, and electronic tourist guides can play a decisive role in tourist satisfaction and shaping the offer at the destination. These systems must satisfy the interests of tourists which travel alone or as a group. However, the development of these tools to generate group tourism itineraries is still limited. Group itineraries must also consider individual preferences and the selection of transport modes. The problem associated with the construction of tourist routes is called the Tourist Trip Design Problem. In this work, an extension of the Team Orienteering Problem with Time Windows is developed to model tourism planning. The model considers the construction of group routes, a cost and time limit associated with each participant, the selection of the mode of transport to go from one location to another, and heterogeneous preferences in the group. The model aims to maximise the preferences of group members by considering real and challenging constraints that increase complexity and avoid solutions in polynomial time. This is a novel model, and we generate sets of instances to assess the accuracy of our solution approach. A Greedy Randomised Adaptive Search Procedure is proposed to solve the model. Evaluation criteria based on time, cost, and preferences are used to guide the candidate’s selection in the construction phase. The results provided by our meta-heuristic are compared with those obtained by solving the Mixed Integer Programming formulation with exact solver. Computational results show the effectiveness and efficiency of the proposed approach.

The Role of Metaheuristics as Solutions Generators

Optimization problems are ubiquitous nowadays. Many times, their corresponding computational models necessarily leave out of consideration several characteristics and features of the real world, so trying to obtain the optimum solution can not be enough for a problem solving point of view. The aim of this paper is to illustrate the role of metaheuristics as solutions’ generators in a basic problem solving framework. Metaheuristics become relevant in two modes: firstly because every run (in the case of population based techniques) allows to obtain a set of potentially good solutions, and secondly, if a reference solution is available, one can set up a new optimization problem that allows to obtain solutions with similar quality in the objectives space but maximally different structure in the design space. Once a set of solutions is obtained, an example of an a posteriori analysis to rank them according with decision maker’s preferences is shown. All the problem solving framework steps, emphasizing the role of metaheuristics are illustrated with a dynamic version of the tourist trip design problem (for the first mode), and with a perishable food distribution problem (for the second one). These examples clearly show the benefits of the problem solving framework proposed. The potential role of the symmetry concept is also explored.

Branch-and-check approaches for the tourist trip design problem with rich constraints

The tourist trip design problem is an extension of the orienteering problem applied to tourism. The problem consists in selecting a subset of locations to visit from among a larger set while maximizing the benefit for the tourist. The benefit is given by the sum of the rewards collected at each location visited. We consider a variant of the problem that deals not only with “typical” constraints such as budget, opening-time hours (i.e., time windows at the locations), and maximum trip duration but also with other practical tourism constraints such as mandatory visits, limits on the number of locations of each type, and the order at which selected locations are visited. To solve this problem, we propose a branch-and-check approach in which the master problem selects a subset of locations, verifying all except time-related constraints, and these locations define candidate solutions to the master problem. For each candidate solution, the sub-problem checks whether a feasible trip can be built using the given locations. To accelerate the branch-and-check approach, we propose and test improvements, including preprocessing to tighten the master-sub problem, valid inequalities generated dynamically to strengthen the master problem, and a local branching and variable neighborhood search to find new feasible solutions. Finally, we report the experimental results and compare the performance of the proposed exact algorithm with that of a mathematical solver.

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

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.

A simulated annealing-based recommender system for solving the tourist trip design problem

Tourism recommender system (TRS) experienced high growth over the last decades given its significant role in tourist satisfaction. Tourists are usually worried about planning their trips since it requires a considerable effort and it is a time consuming task. The main purpose of TRS is to select points of interest (POIs) that match users preferences and to suggest personalized daily sightseeing Designing such a suitable traveling tour is modeled as a Tourist Trip Design Problem (TTDP). Numerous variants of the problem are presented in the literature such as the Team Orienteering Problem (TOP) evoked in this paper. The aim is to maximize the total collected score of visited POIs subject to distance and time constraints. For solving the TTDP, we propose a metaheuristic based on two steps (i) grouping the POIs into clusters based on the start and end locations and (ii) solving the routing problem for each cluster to generate the optimal routes. The proposed approach is a combination between k-means and simulated annealing algorithms, namely, KSA. Besides, we develop StayPlan which is a TRS that integrates KSA algorithm to tackle the TTDP. StayPlan incorporates three main modules which are, user preferences, data retrieving & scoring and TTDP solving & optimization. Computational experiments compare the performance of our proposed algorithm with four state-of-the-art methods. The results show the efficiency and effectiveness of KSA algorithm in solving the benchmark suite from the literature. KSA algorithm reaches the best-known solutions for 100% of tested instances. Additionally, our method outperforms all the considered algorithms in terms of CPU time, it is quite fast with an average run time of 31.49 s. Ultimately, an illustrative example of the adapted scenario is applied to Tunis city as a real case.

A reinforcement learning approach to the orienteering problem with time windows

The Orienteering Problem with Time Windows (OPTW) is a combinatorial optimization problem where the goal is to maximize the total score collected from different visited locations. The application of neural network models to combinatorial optimization has recently shown promising results in dealing with similar problems, like the Travelling Salesman Problem. A neural network allows learning solutions using reinforcement learning or supervised learning, depending on the available data. After the learning stage, it can be generalized and quickly fine-tuned to further improve performance and personalization. The advantages are evident since, for real-world applications, solution quality, personalization, and execution times are all important factors that should be taken into account.This study explores the use of Pointer Network models trained using reinforcement learning to solve the OPTW problem. We propose a modified architecture that leverages Pointer Networks to better address problems related with dynamic time-dependent constraints. Among its various applications, the OPTW can be used to model the Tourist Trip Design Problem (TTDP). We train the Pointer Network with the TTDP problem in mind, by sampling variables that can change across tourists visiting a particular instance-region: starting position, starting time, available time, and the scores given to each point of interest. Once a model-region is trained, it can infer a solution for a particular tourist using beam search. We based the assessment of our approach on several existing benchmark OPTW instances. We show that it generalizes across different tourists that visit each region and that it generally outperforms the most commonly used heuristic, while computing the solution in realistic times.

Rough approximation-based approach for designing a personalized tour route under a fuzzy environment

Trip planning significantly improves tourists’ experiences and enhances the competitive advantage of tourist attractions. We focus on the tourist trip design problem (TTDP) under a fuzzy environment, which is an extension of TTDP that considers the spatiotemporal route structure and variable sightseeing value at points of interest. A rough approximation-based model is proposed to deal with fuzzy variables, and a hybrid genetic algorithm is designed to identify the optimal route. We conduct a numerical experiment to assess the performance of the presented approach. The results of the Wilcoxon rank sum tests indicate that our approach performs significantly better than currently available methods. The evolution strategies based on improved particle swarm optimization also demonstrate better efficiency than existing approaches.

Multi-objective Tourist Trip Design Problem in Chiang Mai City

This study aims to solve a multi-objective tourist trip design problem (MO-TTDP) in order to simultaneously maximize tourist satisfaction and minimize total traveling cost. The goal is to identify optimal routing for tourist under time window and time horizon constraints. The mathematical model is first presented to find optimal routing under limited budget. However, the exact algorithm cannot find solution with multiple objectives simultaneously. Therefore, this study applies a metaheuristic, called Global Local and Near-Neighbour Particle Swarm Optimization (GLNPSO), to solve the MO-TTDP. Several local search strategies are also proposed to enhance the solution quality. The proposed algorithm is implemented on the real-case study in Chiang Mai City, Thailand. The experimental results show that the proposed algorithm yields a set of diverse and high quality non-dominated solutions.

An interactive preference-guided firefly algorithm for personalized tourist itineraries

The present research proposes an interactive optimization framework to aid tourists to organize their trip by generating personalized walking itineraries among several Points of Interest (POIs). The solution of the multi-objective Prize-Collecting Vehicle Routing Problem (MO-PCVRP) is used to simulate this tourist trip design problem. The objectives of the proposed formulation are the minimization of the total distance walked among selected POIs, the minimization of a fixed cost related to the number of the created itineraries, and the maximization of the total satisfaction gained by visiting the selected POIs. The optimization of the MO-PCVRP is conducted by the proposed Preference-Guided Firefly Algorithm (PGFA), which allows for preferences articulated by a decision-maker (DM) to guide the search. The PGFA is incorporated into an interactive framework, where a DM provides his/her preferential information, progressively during the optimization process, by ranking a small representative set of Pareto optimal solutions. The DM’s articulated preferences are elicited utilizing a preference disaggregation method, the UTASTAR, which results in a preference model, which is ultimately used to guide the search towards the DM’s Region of Interest (ROI) in the Pareto front. The effectiveness and robustness of the proposed interactive PGFA framework are demonstrated over experimental scenarios.

Improving Itinerary Recommendations for Tourists Through Metaheuristic Algorithms: An Optimization Proposal

In recent years, recommender systems have been used as a solution to support tourists with recommendations oriented to maximize the entertainment value of visiting a tourist destination. However, this is not an easy task because many aspects need to be considered to make realistic recommendations: the context of a tourist destination visited, lack of updated information about points of interest, transport information, weather forecast, etc. The recommendations concerning a tourist destination must be linked to the interests and constraints of the tourist. In this research, we present a mobile recommender system based on Tourist Trip Design Problem (TTDP)/Time Depending (TD) - Orienteering Problem (OP) - Time Windows (TW), which analyzes in real time the user's constraints and the points of interest's constraints. For solving TTDP, we clustered preferences depending on the number of days that a tourist will visit a tourist destination using a k-means algorithm. Then, with a genetic algorithm (GA), we optimize the proposed itineraries to tourists for facilitating the organization of their visits. We also used a parametrized fitness function to include any element of the context to generate an optimized recommendation. Our recommender is different from others because it is scalable and adaptable to environmental changes and users' interests, and it offers real-time recommendations. To test our recommender, we developed an application that uses our algorithm. Finally, 131 tourists used this recommender system and an analysis of users' perceptions was developed. Metrics were also used to detect the percentage of precision, in order to determine the degree of accuracy of the recommender system. This study has implications for researchers interested in developing software to recommend the best itinerary for tourists with constraint controls with regard to the optimized itineraries.

Monte Carlo sampling for the tourist trip design problem

Introduction: The Tourist Trip Design Problem is a variant of a route-planning problem for tourists interested in multiple points of interest. Each point of interest has different availability, and a certain satisfaction score can be achieved when it is visited. Objectives: The objective is to select a subset of points of interests to visit within a given time budget, in such a way that the satisfaction score of the tourist is maximized and the total travel time is minimized. Methods: In our proposed model, the calculation of the availability of a POI is based on the waiting time and / or the weather forecast. However, research shows that most tourists prefer to travel within a crowded and limited area of very attractive POIs for safety reasons and because they feel more in control. Results: In this work we demonstrate that the existing model of the Probabilistic Orienteering Problem fits a probabilistic variant of this problem and that Monte Carlo Sampling techniques can be used inside a heurist solver to efficiently provide solutions. Conclusions: In this work we demonstrate the existing model of the Probabilistic Orienteering Problem fits the stochastic Tourist Trip Design Problem. We proposed a way to solve the problem by using Monte Carlo Sampling techniques inside a heuristic solver and discussed several possible improvements on the model. Further extension of the model will be developed for solving more practical problems.

A fuzzy GRASP for the tourist trip design with clustered POIs

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.

Efficient Cluster-Based Heuristics for the Team Orienteering Problem with Time Windows

In the Team Orienteering Problem with Time Windows (TOPTW), a variant of the Vehicle Routing Problem with Profits, a set of locations is given, each associated with a profit, a visiting time and a time window. The aim is to maximize the overall profit collected by a number of routes, while the duration of each route must not exceed a given time budget. TOPTW is NP-hard and is typically used to model the Tourist Trip Design Problem. The latter deals with deriving near optimal multiple-day tours for tourists visiting a destination with several points of interest (POIs). The most efficient known heuristic approach to TOPTW which yields the best solution quality versus execution time, is based on Iterated Local Search (ILS). However, the ILS algorithm treats each node separately, hence it tends to overlook highly profitable areas of nodes situated far from the current solution, considering them too time-expensive to visit. We propose two cluster-based extensions to ILS addressing the aforementioned weakness by grouping nodes on separate clusters (based on geographical criteria), thereby making visits to such nodes more attractive. Our approaches improve ILS with respect to solutions quality and execution time as evidenced by experimental tests exercised on both existing and new TTDP-oriented benchmark instances.

Exploiting social media information toward a context-aware recommendation system

The rise of the social networks during the last few years has provided a vast amount of knowledge in several domains. Among them, route planning and point-of-interest recommendation have significantly benefited. Seen from the side of a tourist, they consist two challenging and time-consuming tasks since they may rely on many parameters and are limited by several constraints, such as time and budget available, user preferences, etc. In this paper we present Xenia, a context-aware system that works toward solving the aforementioned problems. More specifically, it aims to automatically construct travel routes, i.e., ordered visits to various places-of-interest. The user (tourist) indicates an initial and an ending point and her/his available time budget and the system proposes travel routes that maximize her/his travel experience, while adhering to the aforementioned limitations. This particular route planning problem is widely known as the “Tourist Trip Design Problem,” having several variations. In this work we solve this problem by modeling it through the “Orienteering Problem.” We harvest geo-tagged photos from the well-known social network Flickr and using the user-generated textual metadata that accompany them we extract areas-of-interest within a given city along with their underlying semantics. Moreover, by utilizing both the timestamps and the geo-tags of the photos we are able to identify the trajectory patterns of tourists, to detect popular places-of-interest and finally to estimate the average visit duration. Using this historical data we propose travel routes for four of the most popular Greek cities. The effectiveness of our approach is validated upon a twofold validation consisting by (a) a comparison versus the most typical baselines that have adopted by state-of-the-art works and (b) an empirical evaluation by real-life users.

Using a four-step heuristic algorithm to design personalized day tour route within a tourist attraction

The design of personalized day-tour routes for tourists plays a fundamental role in improving tourists’ travel experiences, and it is a crucial practice for managers of tourist attractions in an increasingly competitive marketplace. This study constructs a tourist recommendation system with consideration for aesthetic fatigue and variable sightseeing value. A four-step heuristic algorithm (involving a genetic algorithm and a difference evolution algorithm) is proposed, which serves as the nucleus for a new system to deal with the tourist trip design problem. To evaluate the performance of this algorithm, a case study was conducted at the Jiuzhai Valley in Sichuan, China. The results of paired sample t-tests indicated that the proposed heuristic algorithm indeed performed significantly better than existing methods. Furthermore, the study showed that our proposed system was able to design more realistic and better personalized routes for tourists than previous systems.

Recommending a sequence of interesting places for tourist trips

Tourist trip design problems (TTDP) support tourists in creating trips composed of multiple points of interest (POIs) or other travel-related items. We present a novel approach to generate routes comprising different POIs with a reasonable routing for a short city trip. In our scenario, a user enters a start and an end point in a web application together with preferences and receives a walking route with interesting places to visit along the way. The place discovery is based on retrieving arbitrarily rated places from Foursquare, so it is not restricted to certain cities or regions. The developed scoring mechanism rates the level of interest of a POI and accounts for the number of places per category. Discovered places are then combined to a practical route using a constraint-free and a constraint-based version of our algorithm. The algorithms are based on Dijkstra’s algorithm to find the shortest path in a graph. We show that Dijkstra’s algorithm can be modified to find not only the shortest paths, but also trips that solve the TTDP by maximizing the entertainment for the user while respecting time and budget constraints. The solution has been implemented in a practical web application. We conducted a user study showing that our test users highly accepted the application. Improvement with regard to user preferences for place categories lead to additional benefits in terms of user satisfaction with the routing and the match with their preferences. Finally, we outline challenges for future work on TTDPs in this article.

협업적 추천 기반의 여행 계획 시스템

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as ‘Tourist Trip Design Problems’, and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters’ specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user’s preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3(PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users’ preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.