Dirk Van Oudheusden

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 guided local search heuristic for the capacitated arc routing problem

Abstract This paper presents a new local search algorithm for the capacitated arc routing problem (CARP). The procedure uses single vehicle moves and moves that operate on two routes, both derived from a node routing context but properly adapted to work well for arc routing problems. We combine the algorithm with the meta-heuristic guided local search and further use the mechanisms of neighbor lists and edge marking to improve the solution quality and to save computation time. Experiments on standard benchmark problems from the literature show that our algorithm outperforms the existing heuristics for the CARP. On a set of new test problems, the local search approach consistently produces high quality solutions and often detects an optimal solution within limited computation time.

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.

The Mobile Tourist Guide: An OR Opportunity

AbstractNowadays, an average tourist plans a vacation using web sites, magazine articles and guidebooks. The inability to modify this holiday plan in real-time motivates the need for a Next Generation Mobile Tourist Guide (MTG). The MTG, a handheld embedded device, is aware of the tourists preferences, attraction values and trip information. Based on real-time and reliable data, the device can immediately suggest new integrated holiday plans. To develop these holiday plans, additional OR research and very specific decision models are required. In this paper, the Tourist Trip Design Problem (TTDP) is defined as an extension of the Orienteering Problem (OP).

Collection and Vehicle Routing Issues in Reverse Logistics

It is not yet known to what extent reverse logistics might increase the total amount of transportation in supply chains — partially since it will also reduce activities related to the use of new, extractive resources. It is clear, however, that the extra transportation will diminish the environmental benefits of closing the loop. Likewise, inefficient or ineffective transport activities limit the economic success of reprocessing products.

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.