Comparison of mixed integer programming and fast simulated annealing for optimizing beam weights in radiation therapy

被引:37
作者
Langer, M [1 ]
Morrill, S [1 ]
Brown, R [1 ]
Lee, O [1 ]
Lane, R [1 ]
机构
[1] ONE STONE CORP,CAMBRIDGE,MA 02140
关键词
radiation therapy planning; optimization; mixed integer programming; simulated annealing; dose-volume limits;
D O I
10.1118/1.597857
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Two competing methods for assigning intensities to radiation treatment beams were tested. One method was derived from mixed integer programming and the other was based on simulated annealing. The methods faced a common objective and identical constraints. The goal was to maximize the minimum tumor dose while keeping the dose in required fractions of normal organ volumes below a threshold for damage. The minimum tumor doses of the two methods were compared when all the dose-volume constraints were satisfied. A mixed integer linear program gave a minimum tumor dose that was at least 1.8 Gy higher than that given by simulated annealing in 7 of 19 trials. The difference was greater than or equal to 5.4 Gy in 4 of 19 trials. In no case was the mixed integer solution one fraction size (1.8 Gy) worse than that of simulated annealing. The better solution provided by the mixed integer program allows tumor dose to increase without violating the dose-volume limits of normal tissues. (C) 1996 American Association of Physicists in Medicine.
引用
收藏
页码:957 / 964
页数:8
相关论文
共 50 条
  • [1] Solving discrete lot-sizing and scheduling by simulated annealing and mixed integer programming
    Ceschia, Sara
    Di Gaspero, Luca
    Schaerf, Andrea
    COMPUTERS & INDUSTRIAL ENGINEERING, 2017, 114 : 235 - 243
  • [2] Hybrid Approaches based on Simulated Annealing and an Exact Algorithm for Mixed Integer Programming Problems
    Tamaki, Keitaro
    Tengan, Takeshi
    Nakamura, Morikazu
    2012 THIRD INTERNATIONAL CONFERENCE ON NETWORKING AND COMPUTING (ICNC 2012), 2012, : 435 - 440
  • [3] Optimizing simulated annealing schedules with genetic programming
    Bolte, A
    Thonemann, UW
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1996, 92 (02) : 402 - 416
  • [4] Beam orientation optimization for intensity-modulated radiation therapy using mixed integer programming
    Yang, Ruijie
    Dai, Jianrong
    Yang, Yong
    Hu, Yimin
    WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2006, VOL 14, PTS 1-6, 2007, 14 : 1758 - +
  • [5] Optimizing architectural layout design via mixed integer programming
    Keatruangkamala, K
    Sinapiromsaran, K
    Computer Aided Architectural Design Futures 2005, Proceedings, 2005, : 175 - 184
  • [6] A Mixed Linear Integer Programming Formulation and a Simulated Annealing Algorithm for the Mammography Unit Location Problem
    Andrade de Campos, Marcos Vinicius
    Stilpen Moreira de Sa, Manoel Victor
    Rosa, Patrick Moreira
    Vaz Penna, Puca Huachi
    de Souza, Sergio Ricardo
    Freitas Souza, Marcone Jamilson
    PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS (ICEIS), VOL 1, 2020, : 428 - 439
  • [7] Embedding of linear programming in a simulated annealing algorithm for solving a mixed integer production planning problem
    Teghem, J
    Pirlot, M
    Antoniadis, C
    JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 1995, 64 (1-2) : 91 - 102
  • [8] Simulated annealing with Tsallis weights - A numerical comparison
    Hansmann, UHE
    PHYSICA A, 1997, 242 (1-2): : 250 - 257
  • [9] Mixed integer programming model for optimizing the layout of an ICU vehicle
    Sanchez Alejo, Javier
    Garrido Martin, Modoaldo
    Ortega-Mier, Miguel
    Garcia-Sanchez, Alvaro
    BMC HEALTH SERVICES RESEARCH, 2009, 9
  • [10] Hybrid simulated annealing and mixed integer linear programming algorithm for optimal planning of radial distribution networks with distributed generation
    Popovic, Z. N.
    Kerleta, V. Dj.
    Popovic, D. S.
    ELECTRIC POWER SYSTEMS RESEARCH, 2014, 108 : 211 - 222