scholarly journals Wasserstein distributionally robust shortest path problem

2020 ◽  
Vol 284 (1) ◽  
pp. 31-43
Author(s):  
Zhuolin Wang ◽  
Keyou You ◽  
Shiji Song ◽  
Yuli Zhang
Keyword(s):  
Shortest Path ◽  
Shortest Path Problem ◽  
Distributionally Robust ◽  
Robust Shortest Path

scholarly journals An approach to the distributionally robust shortest path problem

2021 ◽  
pp. 105212
Author(s):  
Sergey S. Ketkov ◽  
Oleg A. Prokopyev ◽  
Evgenii P. Burashnikov
Keyword(s):  
Shortest Path ◽  
Shortest Path Problem ◽  
Distributionally Robust ◽  
Robust Shortest Path

New reformulations of distributionally robust shortest path problem

2016 ◽  
Vol 74 ◽  
pp. 196-204 ◽  
Author(s):  
Jianqiang Cheng ◽  
Janny Leung ◽  
Abdel Lisser
Keyword(s):  
Shortest Path ◽  
Shortest Path Problem ◽  
Distributionally Robust ◽  
Robust Shortest Path

Distributionally robust maximum probability shortest path problem

2021 ◽  
Author(s):  
Rashed Khanjani-Shiraz ◽  
Ali Babapour-Azar ◽  
Zohreh Hosseini-Noudeh ◽  
Panos M. Pardalos
Keyword(s):  
Shortest Path ◽  
Shortest Path Problem ◽  
Maximum Probability ◽  
Distributionally Robust

scholarly journals Lagrangian Relaxation for the Multiple Constrained Robust Shortest Path Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yiyong Pan
Keyword(s):  
Shortest Path ◽  
Resource Constraints ◽  
Shortest Path Problem ◽  
Extension Problem ◽  
Travel Time Reliability ◽  
Dual Nature ◽  
Numerical Studies ◽  
Lagrangian Dual ◽  
Dual Relaxation ◽  
Robust Shortest Path

The study focuses on a multiple constrained reliable path problem in which travel time reliability and resource constraints are collectively considered. Nonlinear optimization model is developed to model constrained robust shortest path problem. The dual nature of the proposed problem is deduced based on the Lagrangian duality theory. An efficient algorithm based on Lagrangian dual relaxation is designed to solve constrained robust shortest path problem. An extension problem that considers multiple constraints is discussed. Numerical studies indicate that the proposed algorithm is efficient in terms of obtaining the close-to-optimal solutions within reasonable computational times.

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