Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time

Panadero, Javier; Ammouriova, Majsa; Juan, Ángel A.; Agustín, Adrián; Nogal, María; Serrat, Carles

doi:10.3390/app112412092

Cited by 7 publications

(6 citation statements)

References 65 publications

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“…Figure 6 shows a cross-problem analysis of the performance of AO algorithms when compared with the best-known solution (BKS), which might require minutes or even hours of computation, and the solution provided by a greedy heuristic-which, as the AO, usually requires less than a second of computation. The problems analyzed are: the uncapacitated facility location problem in an Internet of Vehicles context (UFLP-IoV) [7], the team orienteering problem (TOP) [122], the permutation flow-shop problem with deadlines and payoffs (PFSP-DP) [123], the vehicle routing problem (VRP) [90], the basic permutation flow-shop problem (PFSP) [90], the basic uncapacitated facility location problem (UFLP) [124], and the arc routing problem (ARP) [125]. Notice that, while the greedy heuristic typically offers solutions with a gap between 2% and 8% with respect to the BKS, the AO algorithm is capable of generating solutions with a gap lower than 2% in most cases.…”

Section: Computational Results Using Ao Algorithms In Isvnmentioning

confidence: 99%

Internet of Vehicles and Real-Time Optimization Algorithms: Concepts for Vehicle Networking in Smart Cities

et al. 2022

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Achieving sustainable freight transport and citizens’ mobility operations in modern cities are becoming critical issues for many governments. By analyzing big data streams generated through IoT devices, city planners now have the possibility to optimize traffic and mobility patterns. IoT combined with innovative transport concepts as well as emerging mobility modes (e.g., ridesharing and carsharing) constitute a new paradigm in sustainable and optimized traffic operations in smart cities. Still, these are highly dynamic scenarios, which are also subject to a high uncertainty degree. Hence, factors such as real-time optimization and re-optimization of routes, stochastic travel times, and evolving customers’ requirements and traffic status also have to be considered. This paper discusses the main challenges associated with Internet of Vehicles (IoV) and vehicle networking scenarios, identifies the underlying optimization problems that need to be solved in real time, and proposes an approach to combine the use of IoV with parallelization approaches. To this aim, agile optimization and distributed machine learning are envisaged as the best candidate algorithms to develop efficient transport and mobility systems.

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Section: Computational Results Using Ao Algorithms In Isvnmentioning

confidence: 99%

Internet of Vehicles and Real-Time Optimization Algorithms: Concepts for Vehicle Networking in Smart Cities

et al. 2022

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“…Additionally, they proposed an AO methodology to adapt to the ever-changing environment, stressing the urgency of providing real-time solutions to potentially save lives. Similarly, Panadero et al [90] studied the growing concern of UAVs and self-driving vehicles in smart cities to serve customers by a specified due date efficiently. An AO algorithm combining an extremely fast biased-randomized heuristic with a parallel computing approach was proposed to solve the problem.…”

Section: Agile E-routingmentioning

confidence: 99%

Battery Management in Electric Vehicle Routing Problems: A Review

Martin,

Escoto,

Guerrero

et al. 2024

Energies

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The adoption of electric vehicles (EVs) has gained significant momentum in recent years as a sustainable alternative to traditional internal combustion engine vehicles. However, the efficient utilization of batteries in EVs, coupled with the growing demand for sustainable transportation, has posed complex challenges for battery management in the context of electric vehicle routing problems in a broad sense, which includes vehicle routing problems, team orienteering problems, and arc routing problems, all of them using EVs. This paper presents a comprehensive review of the state-of-the-art approaches, methodologies, and strategies for battery management in each of the aforementioned optimization problems. We explore the relevant factors influencing battery performance and the interplay between routing, charging, and energy management in the context of EVs. The paper also discusses the advances in optimization algorithms, vehicle-to-grid integration, and intelligent decision-making techniques aimed at enhancing the range, reliability, and sustainability of EV operations. Key findings indicate a paradigm shift towards addressing uncertainties, dynamic conditions, and synchronization challenges inherent in large-scale and dynamic routing problems within the context of EVs that require efficient battery management.

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“…Eventually, their proposed model is solved by employing a BR simheuristics algorithm. Finally, Panadero et al (2021) address the smart cities and the recent technologies implemented in the vehicles, such as telecommunication systems, Internet-based technologies, and satellite services, to improve the efficiency of vehicles. In this work, a real-time TOP is considered due to the need for online decision making in a smart city, and an agile optimization algorithm based on fast BR heuristics and a parallel computing approach is presented as the solution approach.…”

Section: Stochastic Op and Stochastic Topmentioning

confidence: 99%

Solving the stochastic team orienteering problem: comparing simheuristics with the sample average approximation method

Panadero

Juan

Ghorbani

et al. 2023

Int Trans Operational Res

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The team orienteering problem (TOP) is an NP‐hard optimization problem with an increasing number of potential applications in smart cities, humanitarian logistics, wildfire surveillance, etc. In the TOP, a fixed fleet of vehicles is employed to obtain rewards by visiting nodes in a network. All vehicles share common origin and destination locations. Since each vehicle has a limitation in time or traveling distance, not all nodes in the network can be visited. Hence, the goal is focused on the maximization of the collected reward, taking into account the aforementioned constraints. Most of the existing literature on the TOP focuses on its deterministic version, where rewards and travel times are assumed to be predefined values. This paper focuses on a more realistic TOP version, where travel times are modeled as random variables, which introduces reliability issues in the solutions due to the route‐length constraint. In order to deal with these complexities, we propose a simheuristic algorithm that hybridizes biased‐randomized heuristics with a variable neighborhood search and MCS. To test the quality of the solutions generated by the proposed simheuristic approach, we employ the well‐known sample average approximation (SAA) method, as well as a combination model that hybridizes the metaheuristic used in the simheuristic approach with the SAA algorithm. The results show that our proposed simheuristic outperforms the SAA and the hybrid model both on the objective function values and computational time.

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Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time

Cited by 7 publications

References 65 publications

Internet of Vehicles and Real-Time Optimization Algorithms: Concepts for Vehicle Networking in Smart Cities

Internet of Vehicles and Real-Time Optimization Algorithms: Concepts for Vehicle Networking in Smart Cities

Battery Management in Electric Vehicle Routing Problems: A Review

Solving the stochastic team orienteering problem: comparing simheuristics with the sample average approximation method

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