Emergency material scheduling optimization model and algorithms: A review

被引:33
作者
Hu, Hui [1 ]
He, Jing [1 ]
He, Xiongfei [1 ]
Yang, Wanli [1 ]
Nie, Jing [1 ]
Ran, Bin [2 ]
机构
[1] Changan Univ, Sch Automobile, Xian 710064, Shaanxi, Peoples R China
[2] Univ Wisconsin, Sch Civil & Environm Engn, Madison, WI 53706 USA
关键词
Emergency material scheduling (EMS); Optimization model; Heuristic algorithm; Disruption; PARTICLE SWARM OPTIMIZATION; ANT COLONY OPTIMIZATION; LOGISTICS DISTRIBUTION; ROUTING OPTIMIZATION; DISASTER; TRANSPORTATION; OPERATION;
D O I
10.1016/j.jtte.2019.07.001
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In the emergency management of disruptions, efficient emergency material scheduling (EMS) is a key factor to save people's lives and reduce loss. Based on the literature of EMS and related areas in recent years, the research was summarized from two aspects of EMS optimization model and algorithms. It is concluded that the EMS optimization models mainly aim at the shortest time, shortest distance, minimum cost, maximum satisfaction and fairness, etc. The constraints usually include the quantity of supply depots, relief supply and vehicles, the types of commodities, the road network conditions, the budgets and the demand forecast of emergency materials. Multi-objective model is more complex and it usually considers more than one objective. To find the optimized solution, the multi-objective model with complex constraints needs more efficient algorithms. The existing algorithms, including mathematic algorithm and heuristic algorithm, have been categorized. For NP-hard (non-deterministic polynomial hard) problems, heuristic algorithms should be designed, which mainly include genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), etc. Based on the characteristics of the optimization model and various algorithms, appropriate algorithm or tools should be chosen and designed to obtain the optimized solution of EMS model. Finally, the development trends of EMS optimization model and algorithm in the future are proposed. (C) 2019 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of Owner.
引用
收藏
页码:441 / 454
页数:14
相关论文
共 98 条
  • [51] Liu H., 2008, SUBNATIONAL FISCAL R, V2008, P38
  • [52] An evolutionary game based particle swarm optimization algorithm
    Liu, Wei-Bing
    Wang, Xian-Ha
    [J]. JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2008, 214 (01) : 30 - 35
  • [53] Liu Y., 2010, THESIS
  • [54] Real-time relief distribution in the aftermath of disasters - A rolling horizon approach
    Lu, Chung-Cheng
    Ying, Kuo-Ching
    Chen, Hui-Ju
    [J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2016, 93 : 1 - 20
  • [55] Ma Dongqing, 2014, COMPUTER ENG APPL, V50, P246
  • [56] Ma X, 2011, Seismic design and behavior of self-centering braced frame with controlled rocking and energy dissipating fuses
  • [57] Ma Y., 2019, NATURAL HAZARDS RISK, V10, P1246
  • [58] A hybrid genetic - Particle Swarm Optimization Algorithm for the vehicle routing problem
    Marinakis, Yannis
    Marinaki, Magdalene
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (02) : 1446 - 1455
  • [59] Meng D., 2015, THESIS
  • [60] Mishra B.K., 2018, INT C INF COMM TECHN