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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Vydané v:	IEEE transactions on intelligent transportation systems Ročník 24; číslo 2; s. 1 - 15
Hlavní autori:	Park, Youngsoo, Lee, Sangyoon, Sung, Inkyung, Nielsen, Peter, Moon, Ilkyeong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Sung, Inkyung Nielsen, Peter Moon, Ilkyeong Lee, Sangyoon Park, Youngsoo
Author_xml	– sequence: 1 givenname: Youngsoo surname: Park fullname: Park, Youngsoo organization: Woowa Brothers Corporation, Seoul, South Korea – sequence: 2 givenname: Sangyoon surname: Lee fullname: Lee, Sangyoon organization: Samsung Advanced Institute of Technology, Samsung Electronics, Suwon-si, South Korea – sequence: 3 givenname: Inkyung orcidid: 0000-0002-7045-8143 surname: Sung fullname: Sung, Inkyung organization: Department of Materials and Production, Aalborg University, Aalborg, Denmark – sequence: 4 givenname: Peter orcidid: 0000-0002-4882-7942 surname: Nielsen fullname: Nielsen, Peter organization: Department of Materials and Production, Aalborg University, Aalborg, Denmark – sequence: 5 givenname: Ilkyeong orcidid: 0000-0002-7072-1351 surname: Moon fullname: Moon, Ilkyeong organization: Department of Industrial Engineering, Seoul National University, Seoul, South Korea
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SubjectTerms	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
Title	Facility Location-Allocation Problem for Emergency Medical Service With Unmanned Aerial Vehicle
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