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

DISCRETE PARTICLE SWARM OPTIMIZATION FOR THE ORIENTEERING PROBLEM

Discrete particle swarm optimization (DPSO) is gaining popularity in the area of combinatorial optimization in the recent past due to its simplicity in coding and consistency in performance.  A DPSO algorithm has been developed for orienteering problem (OP) which has been shown to have many practical applications.  It uses reduced variable neighborhood search as a local search tool.  The DPSO algorithm was compared with ten heuristic models from the literature using benchmark problems.  The results show that the DPSO algorithm is a robust algorithm that can optimally solve the well known OP test problems

Force-imitated particle swarm optimization using the near-neighbor effect for locating multiple optima

Copyright @ Elsevier Inc. All rights reserved.Multimodal optimization problems pose a great challenge of locating multiple optima simultaneously in the search space to the particle swarm optimization (PSO) community. In this paper, the motion principle of particles in PSO is extended by using the near-neighbor effect in mechanical theory, which is a universal phenomenon in nature and society. In the proposed near-neighbor effect based force-imitated PSO (NN-FPSO) algorithm, each particle explores the promising regions where it resides under the composite forces produced by the “near-neighbor attractor” and “near-neighbor repeller”, which are selected from the set of memorized personal best positions and the current swarm based on the principles of “superior-and-nearer” and “inferior-and-nearer”, respectively. These two forces pull and push a particle to search for the nearby optimum. Hence, particles can simultaneously locate multiple optima quickly and precisely. Experiments are carried out to investigate the performance of NN-FPSO in comparison with a number of state-of-the-art PSO algorithms for locating multiple optima over a series of multimodal benchmark test functions. The experimental results indicate that the proposed NN-FPSO algorithm can efficiently locate multiple optima in multimodal fitness landscapes.This work was supported in part by the Key Program of National Natural Science Foundation (NNSF) of China under Grant 70931001, Grant 70771021, and Grant 70721001, the National Natural Science Foundation (NNSF) of China for Youth under Grant 61004121, Grant 70771021, the Science Fund for Creative Research Group of NNSF of China under Grant 60821063, the PhD Programs Foundation of Ministry of Education of China under Grant 200801450008, and in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1 and Grant EP/E060722/2

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