Abstract
In 2017, Kiwi.com proposed the Travelling Salesman Problem 2.0. Despite some similarities with the classic Travelling Salesman Problem (TSP), the problem is more complex. It can be characterised as an asymmetric, time-constrained and generalised TSP. Moreover, infeasibility further complicates the challenge, as no flights may be available between certain airports on specific days. Exact methods often fail in solving these NP -Hard problems. Therefore, alternative approaches, such as heuristics, are typically favoured. A Monte Carlo Tree Search (MCTS) is implemented to tackle Kiwi's problem, an algorithm traditionally used in board games but adapted here for air travel optimisation. The MCTS has been chosen for its proven effectiveness in handling complex and high-dimensional search spaces.
Original language | English |
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Title of host publication | 2024 International Conference on Decision Aid Sciences and Applications (DASA) |
Publisher | IEEE |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 9798350369106 |
ISBN (Print) | 9798350369113 |
DOIs | |
Publication status | Published - 17 Jan 2025 |
Event | 2024 International Conference on Decision Aid Sciences and Applications - Manama, Bahrain Duration: 11 Dec 2024 → 12 Dec 2024 |
Conference
Conference | 2024 International Conference on Decision Aid Sciences and Applications |
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Abbreviated title | DASA 2024 |
Country/Territory | Bahrain |
City | Manama |
Period | 11/12/24 → 12/12/24 |
Keywords
- optimisation
- travelling salesman problem
- Monte Carlo tree search
- heuristic design