Facility Location-Allocation Problem for Emergency Medical Service With Unmanned Aerial Vehicle

This paper models an operation problem of an unmanned aerial vehicle for the emergency medical service (UEMS) system. The model is set up as a location-allocation problem. The coverage distance and capacity of the UEMS facility are modeled as functions of UAVs assigned. The allocation of the demand...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 24; no. 2; pp. 1 - 15
Main Authors: Park, Youngsoo, Lee, Sangyoon, Sung, Inkyung, Nielsen, Peter, Moon, Ilkyeong
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Autonomous aerial vehicles
Branch-and-price
Costs
Dynamic programming
emergency medical service
Emergency medical services
Emergency procedures
Emergency services
Heuristic algorithms
Integer programming
Knapsack problem
Linear programming
location-allocation
Lower bounds
Medical services
Mixed integer
Moon
Optimization
Resource management
robust optimization
unmanned aerial vehicle
Unmanned aerial vehicles
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:This paper models an operation problem of an unmanned aerial vehicle for the emergency medical service (UEMS) system. The model is set up as a location-allocation problem. The coverage distance and capacity of the UEMS facility are modeled as functions of UAVs assigned. The allocation of the demand point is constrained by the variable coverage distance of each facility. The robust optimization approach is used over the cardinality-constrained uncertain demand, which leads to a nonlinear optimization problem. The UEMS location-allocation problem (ULAP) is reformulated to a solvable problem. An extended formulation and corresponding branch-and-price (B&P) algorithm are also proposed, which strengthen the linear programming relaxation bound. The subproblem of the B&P algorithm is defined as a robust disjunctively constrained integer knapsack problem. Two solution approaches of mixed-integer linear programming reformulation and decomposed dynamic programming are designed for the subproblem. To provide time-efficient solutions for large-scale problems, a restricted master heuristic (RMH) is proposed based on the extended formulation. In computational experiments, the B&P algorithm provided a strong lower bound, and the RMH could find an effective feasible solution within an applicable computation time.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3223509