Tour recommendation for groups

Consider a group of people who are visiting a major touristic city, such as NY, Paris, or Rome. It is reasonable to assume that each member of the group has his or her own interests or preferences about places to visit, which in general may differ from those of other members. Still, people almost always want to hang out together and so the following question naturally arises: What is the best tour that the group could perform together in the city? This problem underpins several challenges, ranging from understanding people’s expected attitudes towards potential points of interest, to modeling and providing good and viable solutions. Formulating this problem is challenging because of multiple competing objectives. For example, making the entire group as happy as possible in general conflicts with the objective that no member becomes disappointed. In this paper, we address the algorithmic implications of the above problem, by providing various formulations that take into account the overall group as well as the individual satisfaction and the length of the tour. We then study the computational complexity of these formulations, we provide effective and efficient practical algorithms, and, finally, we evaluate them on datasets constructed from real city data

Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation

[EN] The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T), the SEPIE Erasmus+Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Do C. Martins, L.; Tordecilla, RD.; Castaneda, J.; Juan-Pérez, ÁA.; Faulin, J. (2021). Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation. Energies. 14(16):1-30. https://doi.org/10.3390/en14165131130141

IoT analytics and agile optimization for solving dynamic team orienteering problems with mandatory visits

Transport activities and citizen mobility have a deep impact on enlarged smart cities. By analyzing Big Data streams generated through Internet of Things (IoT) devices, this paper aims to show the efficiency of using IoT analytics, as an agile optimization input for solving real-time problems in smart cities. IoT analytics has become the main core of large-scale Internet applications, however, its utilization in optimization approaches for real-time configuration and dynamic conditions of a smart city has been less discussed. The challenging research topic is how to reach real-time IoT analytics for use in optimization approaches. In this paper, we consider integrating IoT analytics into agile optimization problems. A realistic waste collection problem is modeled as a dynamic team orienteering problem with mandatory visits. Open data repositories from smart cities are used for extracting the IoT analytics to achieve maximum advantage under the city environment condition. Our developed methodology allows us to process real-time information gathered from IoT systems in order to optimize the vehicle routing decision under dynamic changes of the traffic environments. A series of computational experiments is provided in order to illustrate our approach and discuss its effectiveness. In these experiments, a traditional static approach is compared against a dynamic one. In the former, the solution is calculated only once at the beginning, while in the latter, the solution is re-calculated periodically as new data are obtained. The results of the experiments clearly show that our proposed dynamic approach outperforms the static one in terms of rewardsThis project has received the support of the Ajuntament of Barcelona and the Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001)Peer ReviewedPostprint (published version

Optimisation approaches for an orienteering problem with applications to wildfire management

During uncontrollable wildfires, Incident Management Teams (ITMs) dispatch vehicles for tasks aimed at reducing the hazard to key assets. The deployment plan is complicated by the need for vehicle capabilities to match asset requirements within time-windows determined by the progression of the fire. Assignment of the response vehicles to undertake protection activities at different assets is known as the asset protection problem. The asset protection problem is one of the real-life applications of the Cooperative Orienteering Problem with Time Windows (COPTW). The COPTW is a class of problems with some important applications and yet has received relatively little attention. In the COPTW, a certain number of team members are required to collect the associated reward from each node simultaneously and cooperatively. This requirement to have one or more team members simultaneously available at a vertex to collect the reward, poses a challenging task. It means that while multiple paths need to be determined as in the team orienteering problem with time-windows (TOPTW), there is the additional requirement that certain paths must meet at some of the vertices. Exact methods are too slow for operational purposes and they are not able to handle large scale instances of the COPTW. This thesis addresses the problem of finding solutions to COPTW in times that make the approaches suitable for use in certain emergency response situations. Computation of exact solutions within a reasonable time is impossible due to the nature of the COPTW. Thus, the thesis introduces an efficient heuristic approach to achieve reliable solutions in short computation times. Thereafter, a new set of algorithms are developed to work together as components of an adaptive large neighbourhood search algorithm. The proposed solution approaches in this work are the first algorithms that can achieve promising solutions for realistic sizes of the COPTW in a time efficient manner. In addition to the COPTW, this thesis presents an algorithmic approach to solve the asset protection problem. The complexities involved in the asset protection problem are handled by a metaheuristic algorithm. The asset protection problem is often further complicated by a wind change that is expected but with uncertainty in its timing. For this situation a two-stage stochastic model is introduced for the optimal deployment of response vehicles. The model addresses uncertainty in the timing of changes in the problem conditions for the first time in the literature. It is shown that deployment plans, which improve on current practices, can be generated in operational times thus providing useful decision support in time-pressured environments. The performance of the proposed approaches are validated through extensive computational studies. The computational results show that the proposed methods are effective in obtaining good quality solutions in times that are suitable for operational purposes. This is particularly useful for increasing the tools available to IMT's faced with making deployment decisions crucial to savings lives and critical assets

Spatial coverage in routing and path planning problems

Routing and path planning problems that involve spatial coverage have received increasing attention in recent years in different application areas. Spatial coverage refers to the possibility of considering nodes that are not directly served by a vehicle as visited for the purpose of the objective function or constraints. Despite similarities between the underlying problems, solution approaches have been developed in different disciplines independently, leading to different terminologies and solution techniques. This paper proposes a unified view of the approaches: Based on a formal introduction of the concept of spatial coverage in vehicle routing, it presents a classification scheme for core problem features and summarizes problem variants and solution concepts developed in the domains of operations research and robotics. The connections between these related problem classes offer insights into common underlying structures and open possibilities for developing new applications and algorithms

Solving Multi Objectives Team Orienteering Problem with Time Windows using Multi Integer Linear Programming

This study solves tourist trip planning using team orienteering problem with time windows with more than one objective. In MO-TOPTW, besides maximum score, there is minimum time that must be achieved to make sure tourist get effective and efficient routing. Score represent priority to visit the destinations, while time consist of visiting time and traveling time between destinations. Number of routing is determined and the goal is giving the tourist the best routing that fulfill all the constraints. The constraints are time windows and tourist’s budget time. Modification of mathematical programming will be done. We used small case to compare between heuristic procedure to develop the route with optimization. Optimization is implemented using Multi Integer Linear Programming using Lingo. The global optimum of optimization method gives better result than heuristic, with total score higher as 12% and total time lower 7.3%. Because this is NP-hard problem, the running time is 45 minutes 24 seconds, very long time for tourist to wait the result. Further research must be done to faster the process with preserving the best result