An Exact A-Based Tree Search Algorithm for TSP With Sequence-and-Load Dependent Risk

The hazardous material transportation requires extensive care owing to the disastrous consequences of accidents, such as chemical spills or radioactive exposures. Consequently, a minimum risk delivery plan that is dynamically decided by the cargo load of the vehicle at each customer must be schedule...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on intelligent transportation systems Ročník 25; číslo 9; s. 10817 - 10834
Hlavní autoři: Cha, Hyungjoo, Lee, Chanho, Xie, Chi, Lu, Qing-Chang, Eun, Joonyup, Cheong, Taesu
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.09.2024
Témata:
Accidents
Hazardous material delivery
Hazardous materials
Heuristic algorithms
iterative deepening A
Logistics
Optimization
Routing
Transportation
traveling salesman problem
ISSN:1524-9050, 1558-0016
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The hazardous material transportation requires extensive care owing to the disastrous consequences of accidents, such as chemical spills or radioactive exposures. Consequently, a minimum risk delivery plan that is dynamically decided by the cargo load of the vehicle at each customer must be scheduled. We introduce a traveling salesman problem (TSP) with a sequence-and-load dependent risk, which differs from the conventional TSP as the arc costs are determined by the hazardous cargo load at each decision epoch. We define our problem in a dynamic programming formulation and present mixed-integer linear program with a nonlinear objective function. To efficiently retrieve exact optimal solutions, we propose an iterative-deepening A*-based tree search algorithm using admissible lower and efficient upper bound algorithms for guaranteed optimality. Numerical experiments indicate that the proposed algorithm outperforms a current state-of-the-art solver. An ablation study and sensitivity analysis demonstrate the effectiveness of the proposed algorithm and derive managerial insights.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2024.3384576