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CN 34-1054/N

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A linear programming-based model for multi-object and multi-resource allocation in emergency rescue

  • Faculty of Information Technology, College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.06.004
  • Received Date: 14 September 2017
  • Accepted Date: 10 April 2018
  • Rev Recd Date: 10 April 2018
  • Publish Date: 30 June 2018
    Abstract

  • Abstract

    In an emergency rescue, the situations are different and the deadlines of tasks and available resources are limited, difficult for the single-object and one-to-one resource allocation approaches to handle which makes it problems. To this end, an innovative model is proposed for the multi-object and multi-resource allocation in emergency rescues. By combining the resources, the time consumption for task execution is reduced and the capability of resources is enhanced. In addition, through adjusting the weights of multiple objects, linear programming is employed to generate the resource allocation plan, which can satisfy different requirements of resource allocation in an emergency rescue. Finally, through employing the idea of the multi-stage resource allocation in dynamic programming, our model can handle the dynamics of tasks and resources in an emergency rescue. Experimental results show that our model has good adaptability to multi-resource allocation in different rescue tasks and objects. In addition, the multi-stage characteristic of our model can suit the dynamics of tasks and resources in emergency rescues.

    Abstract

    In an emergency rescue, the situations are different and the deadlines of tasks and available resources are limited, difficult for the single-object and one-to-one resource allocation approaches to handle which makes it problems. To this end, an innovative model is proposed for the multi-object and multi-resource allocation in emergency rescues. By combining the resources, the time consumption for task execution is reduced and the capability of resources is enhanced. In addition, through adjusting the weights of multiple objects, linear programming is employed to generate the resource allocation plan, which can satisfy different requirements of resource allocation in an emergency rescue. Finally, through employing the idea of the multi-stage resource allocation in dynamic programming, our model can handle the dynamics of tasks and resources in an emergency rescue. Experimental results show that our model has good adaptability to multi-resource allocation in different rescue tasks and objects. In addition, the multi-stage characteristic of our model can suit the dynamics of tasks and resources in emergency rescues.
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    • References(17)
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    MASHAYEKHY L, NEJAD M M, GROSU D, et al. An online mechanism for resource allocation and pricing in clouds[J]. IEEE Transactions on Computers, 2016, 65(4):1172-1184.
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    [3]
    HANSEN T M, ROCHE R, SURYANARAYANAN S, et al. Heuristic optimization for an aggregator-based resource allocation in the smart grid[J]. IEEE Transactions on Smart Grid, 2015, 6(4):1785-1794.
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    向逾，胥川，陈鹏予. 医学应急救援中资源优化调度方案研究[J]. 医疗卫生装备，2016，37(10)：40-42.
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    ZHANG Y, LEE C, NIYATO D, et al. Auction approaches for resource allocation in wireless systems: A survey[J]. IEEE Communications Surveys & Tutorials, 2013, 15(3):1020-1041.
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    FIEDRICH F, GEHBAUER F, RICKERS U. Optimized resource allocation for emergency response after earthquake disasters[J]. Safety Science, 2000, 35: 41-57.
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    GEROVAC B J, CARVER D C. Dynamic resource allocation: US8191070B2 [P/OL]. 2012-05-29[2017-08-14]. https://patents.google.com/patent/US8191070B2/en.
    [11]
    SU X, ZHANG M, BAI Q. Decentralized Task Allocation Under Space, Time and Communication Constraints in Disaster Domains[M]//Smart Modeling and Simulation for Complex Systems. Tokyo: Springer, 2015:41-57.
    [12]
    SU X, ZHANG M, BAI Q. Coordination for dynamic weighted task allocation in disaster environments with time, space and communication constraints[J]. Journal of Parallel & Distributed Computing, 2016, 97:47-56.
    [13]
    ZHANG Q, ZHU Q, BOUTABA R. Dynamic resource allocation for spot markets in cloud computing environments[C]// 2011 Fourth IEEE International Conference on Utility and Cloud Computing. Piscataway, NY, USA: IEEE Press, 2012:178-185.
    [14]
    YAACOUB E, DAWY Z. A survey on uplink resource allocation in ofdma wireless networks[J]. IEEE Communications Surveys & Tutorials, 2012, 14(2): 322-337.
    [15]
    SU X, ZHANG M, BAI Q. Dynamic task allocation for heterogeneous agents in disaster environments under time, space and communication constraints[J]. The Computer Journal, 2015, 58(8): 1776-1791.
    [16]
    SU X, ZHANG M, BAI Q, et al. A dynamic coordination approach for task allocation in disaster environments under spatial and communicational constraints[C]// Workshop at the Twenty-Eighth Conference on Artificial Intelligence. Quebec City, Canada: AAAI, 2014.
    [17]
    RAMCHURN S D, POLUKAROV M, FARINELLI A. Coalition formation with spatial and temporal constraints[C]// Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems. Toronto, Canada, 2010: 1181-1188.)
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    [1]
    MASHAYEKHY L, NEJAD M M, GROSU D, et al. An online mechanism for resource allocation and pricing in clouds[J]. IEEE Transactions on Computers, 2016, 65(4):1172-1184.
    [2]
    JUNG G, SIM K M. Agent-based adaptive resource allocation on the cloud computing environment[C]// 2011 40th International Conference on Parallel Processing Workshops. Piscataway, NY, USA: IEEE Press, 2016:345-351.
    [3]
    HANSEN T M, ROCHE R, SURYANARAYANAN S, et al. Heuristic optimization for an aggregator-based resource allocation in the smart grid[J]. IEEE Transactions on Smart Grid, 2015, 6(4):1785-1794.
    [4]
    GUERRIERO F, SURACE R, LOSCRI V, et a1. A multi-objective approach for unmanned aerial vehicle routing problem with soft time windows constraints[J]. Applied Mathematical Modelling, 2014, 38(3):839-852.
    [5]
    向逾，胥川，陈鹏予. 医学应急救援中资源优化调度方案研究[J]. 医疗卫生装备，2016，37(10)：40-42.
    [6]
    宁丙文. 共同提升应急救援能力——记第四届中国国际安全生产应急管理论坛[J]. 劳动保护，2013，(8)：108-110.
    [7]
    郭其云，杨军，郭威. 国际应急救援管理的分析探讨[J]. 消防科学与技术，2015，(5)：629-632.
    [8]
    ZHANG Y, LEE C, NIYATO D, et al. Auction approaches for resource allocation in wireless systems: A survey[J]. IEEE Communications Surveys & Tutorials, 2013, 15(3):1020-1041.
    [9]
    FIEDRICH F, GEHBAUER F, RICKERS U. Optimized resource allocation for emergency response after earthquake disasters[J]. Safety Science, 2000, 35: 41-57.
    [10]
    GEROVAC B J, CARVER D C. Dynamic resource allocation: US8191070B2 [P/OL]. 2012-05-29[2017-08-14]. https://patents.google.com/patent/US8191070B2/en.
    [11]
    SU X, ZHANG M, BAI Q. Decentralized Task Allocation Under Space, Time and Communication Constraints in Disaster Domains[M]//Smart Modeling and Simulation for Complex Systems. Tokyo: Springer, 2015:41-57.
    [12]
    SU X, ZHANG M, BAI Q. Coordination for dynamic weighted task allocation in disaster environments with time, space and communication constraints[J]. Journal of Parallel & Distributed Computing, 2016, 97:47-56.
    [13]
    ZHANG Q, ZHU Q, BOUTABA R. Dynamic resource allocation for spot markets in cloud computing environments[C]// 2011 Fourth IEEE International Conference on Utility and Cloud Computing. Piscataway, NY, USA: IEEE Press, 2012:178-185.
    [14]
    YAACOUB E, DAWY Z. A survey on uplink resource allocation in ofdma wireless networks[J]. IEEE Communications Surveys & Tutorials, 2012, 14(2): 322-337.
    [15]
    SU X, ZHANG M, BAI Q. Dynamic task allocation for heterogeneous agents in disaster environments under time, space and communication constraints[J]. The Computer Journal, 2015, 58(8): 1776-1791.
    [16]
    SU X, ZHANG M, BAI Q, et al. A dynamic coordination approach for task allocation in disaster environments under spatial and communicational constraints[C]// Workshop at the Twenty-Eighth Conference on Artificial Intelligence. Quebec City, Canada: AAAI, 2014.
    [17]
    RAMCHURN S D, POLUKAROV M, FARINELLI A. Coalition formation with spatial and temporal constraints[C]// Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems. Toronto, Canada, 2010: 1181-1188.)
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