Wouter Souffriau

The orienteering problem: a survey

During the last decade, a number of challenging applications in logistics, tourism and other fields were modelled as orienteering problems (OP). In the orienteering problem, a set of vertices is given, each with a score. The goal is to determine a path, limited in length, that visits some vertices and maximises the sum of the collected scores. In this paper, the literature about the orienteering problem and its applications is reviewed. The OP is formally described and many relevant variants are presented. All published exact solution approaches and (meta) heuristics are discussed and compared. Interesting open research questions concerning the OP conclude this paper.

Iterated local search for the team orienteering problem with time windows

A personalised electronic tourist guide assists tourists in planning and enjoying their trip. The planning problem that needs to be solved, in real-time, can be modelled as a team orienteering problem with time windows (TOPTW). In the TOPTW, a set of locations is given, each with a score, a service time and a time window. The goal is to maximise the sum of the collected scores by a fixed number of routes. The routes allow to visit locations at the right time and they are limited in length. The main contribution of this paper is a simple, fast and effective iterated local search meta-heuristic to solve the TOPTW. An insert step is combined with a shake step to escape from local optima. The specific shake step implementation and the fast evaluation of possible improvements, produces a heuristic that performs very well on a large and diverse set of instances. The average gap between the obtained results and the best-known solutions is only 1.8% and the average computation time is decreased with a factor of several hundreds. For 31 instances, new best solutions are computed.

A Personalized Tourist Trip Design Algorithm For Mobile Tourist Guides

Mobile tourist guides evolve towards automated personalized tour planning devices. The contribution of this article is to put forward a combined artificial intelligence and metaheuristic approach to solve tourist trip design problems (TTDP). The approach enables fast decision support for tourists on small footprint mobile devices. The orienteering problem, which originates in the operational research literature, is used as a starting point for modelling the TTDP. The problem involves a set of possible locations having a score and the objective is to maximize the total score of the visited locations, while keeping the total time (or distance) below the available time budget. The score of a location represents the interest of a tourist in that location. Scores are calculated using the vector space model, which is a well-known technique from the field of information retrieval. The TTDP is solved using a guided local search metaheuristic. In order to compare the performance of this approach with an algorithm that appeared in the literature, both are applied to a real data set from the city of Ghent. A collection of tourist points of interest with descriptions was indexed and subsequently queried with popular interests, which resulted in a test set of TTDPs. The approach presented in this article turns out to be faster and produces solutions of better quality.

The City Trip Planner

Research highlights? City Trip Planner integrates selection of attractions with routing between them. ? It provides personalised interest estimation. ? The real-time planning takes into account personal constraints and opening hours. ? Usage statistics and user feedback demonstrate the appreciation by tourists. Over the last few years, advanced digital applications have become available to tourists. Some of these offer the possibility of creating personalised routes. This paper introduces a tourist expert system, called the City Trip Planner, that allows planning routes for five cities in Belgium. It is implemented as a web application that takes into account the interests and trip constraints of the user and matches these to a database of locations in order to predict personal interests. A fast and effective planning algorithm provides an on-the-fly suggestion of a personal trip for a requested number of days, taking into account opening hours of attractions and time for a (lunch) break. The expert system is discussed in detail. Usage statistics and user feedback demonstrate that it is highly appreciated by tourists.

A guided local search metaheuristic for the team orienteering problem

In the team orienteering problem (TOP) a set of locations is given, each with a score. The goal is to determine a fixed number of routes, limited in length, that visit some locations and maximise the sum of the collected scores. This paper describes an algorithm that combines different local search heuristics to solve the TOP. Guided local search (GLS) is used to improve two of the proposed heuristics. An extra heuristic is added to regularly diversify the search in order to explore more areas of the solution space. The algorithm is compared with the best known heuristics of the literature and applied on a large problem set. The obtained results are almost of the same quality as the results of these heuristics but the computational time is reduced significantly. Applying GLS to solve the TOP appears to be a very promising technique. Furthermore, the usefulness of exploring more areas of the solution space is clearly demonstrated.

A hybrid tabu search algorithm for automatically assigning patients to beds

OBJECTIVE We describe a patient admission scheduling algorithm that supports the operational decisions in a hospital. It involves efficiently assigning patients to beds in the appropriate departments, taking into account the medical needs of the patients as well as their preferences, while keeping the number of patients in the different departments balanced. METHODS Due to the combinatorial complexity of the admission scheduling problem, there is a need for an algorithm that intelligently assists the admission scheduler in taking decisions fast. To this end a hybridized tabu search algorithm is developed to tackle the admission scheduling problem. For testing, we use a randomly generated data set. The performance of the algorithm is compared with an integer programming approach. RESULTS AND CONCLUSION The metaheuristic allows flexible modelling and presents feasible solutions even when disrupted by the user at an early stage in the calculation. The integer programming approach is not able to find a solution in 1h of calculation time.

Tourist trip planning functionalities: state-of-the-art and future

When tourists visit a city or region, they cannot visit every point of interest available, as they are constrained in time and budget. Tourist recommender applications help tourists by presenting a personal selection. Providing adequate tour scheduling support for these kinds of applications is a daunting task for the application developer. The objective of this paper is to demonstrate how existing models from the field of Operations Research (OR) fit this scheduling problem, and enable a wide range of tourist trip planning functionalities. Using the Orienteering Problem (OP) and its extensions to model the tourist trip planning problem, allows to deal with a vast number of practical planning problems.

The Multiconstraint Team Orienteering Problem with Multiple Time Windows

This paper introduces the multiconstraint team orienteering problem with multiple time windows MC-TOP-MTW. In the MC-TOP-MTW, a set of vertices is given, each with a service time, one or more time windows, and a score. The goal is to maximize the sum of the collected scores, by a fixed number of tours. The tours are limited in length and restricted by the time windows and additional constraints. Next to a mathematical formulation of the MC-TOP-MTW, the main contribution of this paper is a fast and effective algorithm for tackling this problem, by hybridizing iterated local search with a greedy randomized adaptive search procedure. On a large test set, an average run has a score gap of only 5.19% with known high quality solutions, using 1.5 seconds of computational time. For 32% of the test instances, the known high quality solution was found or improved. This solution method also performs well on test instances of the TOPTW, the selective vehicle routing problem with time windows, and the MC-TOP-TW. A sensitivity analysis shows that the performance of the algorithm is insensitive to small changes in the parameter settings.

Metaheuristics for Tourist Trip Planning

The aim of this paper is to present an overview of metaheuristics used in tourism and to introduce Skewed VNS to solve the team orienteering problem (TOP). Selecting the most interesting points of interest and designing a personalised tourist trip, can be modelled as a TOP with time windows (TOPTW). Guided local search (GLS) and variable neighbourhood search (VNS) are applied to efficiently solve the TOP. Iterated local search (ILS) is implemented to solve the TOPTW. The GLS and VNS algorithms are compared with the best known heuristics and applied on large problem sets. The obtained results are almost of the same quality as the results of these heuristics but the computational time is reduced significantly. For some of the problems VNS calculates new best solutions. The results of the ILS algorithm, applied to large problem sets, have an average gap with the optimal solution of only 2.7%, with much less computational effort.

The travelling salesperson problem with hotel selection

In this paper, we present the travelling salesperson problem with hotel selection (TSPHS), an extension of the TSP with a number of interesting applications. We present a mathematical formulation, explain the difference with related optimization problems and indicate what makes this problem inherently more difficult. We develop a simple but efficient heuristic that uses two constructive initialization procedures and an improvement procedure consisting of several neighbourhood search operators designed specifically for this problem, as well as some typical neighbourhoods from the literature. We generate several benchmark instances of varying sizes and compare the performance of our heuristic with CPLEX (10.0). We also generate some problems with known optimal solutions and use these to further demonstrate that our heuristic achieves good results in very limited computation times.