A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem

被引:0
|
作者
Uguina, Antonio R. [1 ]
Gomez, Juan F. [1 ]
Panadero, Javier [2 ]
Martinez-Gavara, Anna [3 ]
Juan, Angel A. [1 ]
机构
[1] Univ Politecn Valencia, Res Ctr Prod Management & Engn, Alcoy 03801, Spain
[2] Univ Autonoma Barcelona, Dept Comp Architecture & Operating Syst, Bellaterra 08193, Spain
[3] Univ Valencia, Stat & Operat Res Dept, Doctor Moliner 50, Burjassot 46100, Valencia, Spain
关键词
combinatorial optimization; team orienteering problem; reinforcement learning; learnheuristics; UNMANNED AERIAL VEHICLES; PATH RELINKING; DRONES; GRASP;
D O I
10.3390/math12111758
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors-such as traffic congestion, weather conditions, and battery level of each vehicle-to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25% better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Hybridized evolutionary local search algorithm for the team orienteering problem with time windows
    Labadie, Nacima
    Melechovsky, Jan
    Calvo, Roberto Wolfler
    JOURNAL OF HEURISTICS, 2011, 17 (06) : 729 - 753
  • [32] Memetic Algorithm with an Efficient Split Procedure for the Team Orienteering Problem with Time Windows
    Guibadj, Rym Nesrine
    Moukrim, Aziz
    ARTIFICIAL EVOLUTION, EA 2013, 2014, 8752 : 183 - 194
  • [33] An artificial bee colony algorithm approach for the team orienteering problem with time windows
    Cura, Tunchan
    COMPUTERS & INDUSTRIAL ENGINEERING, 2014, 74 : 270 - 290
  • [34] A Hybrid Variation Harmony Search Algorithm for the Team Orienteering Problem with Capacity Limitations
    Tsakirakis, Eleftherios
    Marinaki, Magdalene
    Marinakis, Yannis
    LEARNING AND INTELLIGENT OPTIMIZATION, LION, 2020, 11968 : 146 - 156
  • [35] A model-based algorithm for the Probabilistic Orienteering Problem
    Montemanni, Roberto
    Smith, Derek H.
    COMPUTERS & OPERATIONS RESEARCH, 2025, 176
  • [36] Uncertain Team Orienteering Problem With Time Windows Based on Uncertainty Theory
    Wang, Jian
    Guo, Jiansheng
    Chen, Jicheng
    Tian, Shan
    Gu, Taoyong
    IEEE ACCESS, 2019, 7 : 63403 - 63414
  • [37] A branch-and-price algorithm for a team orienteering problem with fixed-wing drones
    Sundar, Kaarthik
    Sanjeevi, Sujeevraja
    Montez, Christopher
    EURO JOURNAL ON TRANSPORTATION AND LOGISTICS, 2022, 11
  • [38] A Sampling-Based Metaheuristic for the Orienteering Problem with Stochastic Travel Times
    Papapanagiotou, Vassilis
    Montemanni, Roberto
    Gambardella, Luca Maria
    THEORY AND PRACTICE OF NATURAL COMPUTING, TPNC 2016, 2016, 10071 : 97 - 109
  • [39] A novel approach to the Orienteering Problem based on the Harmony Search algorithm
    Szwarc, Krzysztof
    Boryczka, Urszula
    PLOS ONE, 2022, 17 (02):
  • [40] Proportion-based robust optimization and team orienteering problem with interval data
    Ke, Liangjun
    Xu, Zongben
    Feng, Zuren
    Shang, Ke
    Qian, Xueming
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2013, 226 (01) : 19 - 31