考虑凸形障碍的应急设施选址与资源分配决策研究
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摘要
应急设施是应急救援的依托载体,其科学合理的选址事关应急救援的紧迫性和应急资源分配的及时性,障碍约束下的应急设施选址与应急资源分配决策研究具有重要的战略意义.从需求区域的视角和应急设施应急服务质量的视角构建基于障碍约束、容量及安全库存约束的应急设施选址与资源分配优化模型,引入安全库存机制,综合考虑时间性、经济性及地理阻断等多重约束限制,剖析选址和应急物资分配的决策过程,进行应急设施的选址决策和应急物资分配预案的制定.设计灰狼优化算法(GWO)与可视凸点绕障路径耦合算法求解模型,结果表明:所设计算法能有效实现绕障路径的优化,且在需求区域的不同时间满意度偏好下,获得最优的选址-分配方案,研究成果将为应急设施选址与资源分配提供模型和方法设计.
Emergency facilities are the carrier of emergency rescue, the scientific and reasonable location is related to the urgency of emergency rescue and the timeliness of distribution of emergency resources,it is of great strategic significance to study the location of emergency facilities and the decision-making of emergency resource allocation with obstacle constraints. From the perspective of demand area and the emergency service quality of emergency facilities, a location-allocation optimization model of emergency facilities based on obstacle constraints, capacity and safety stock constraints was constructed. The paper introduced a safe stock mechanism and considered the multiple constraints such as time, economy and geographic blocking, the decision-making process of the location and distribution was analyzed to establish a location-allocation scheme for emergency facilities. Coupled grey wolf optimizer(CWO) and visual convex point method was presented to solve the model, numerical examples show that the proposed algorithm can efficiently optimize path around barriers, and get the optimal location allocation scheme under different time satisfaction preference of various regions. The research results will provide a model and methodological design for the location and resource allocation of emergency facilities.
Emergency facilities are the carrier of emergency rescue, the scientific and reasonable location is related to the urgency of emergency rescue and the timeliness of distribution of emergency resources,it is of great strategic significance to study the location of emergency facilities and the decision-making of emergency resource allocation with obstacle constraints. From the perspective of demand area and the emergency service quality of emergency facilities, a location-allocation optimization model of emergency facilities based on obstacle constraints, capacity and safety stock constraints was constructed. The paper introduced a safe stock mechanism and considered the multiple constraints such as time, economy and geographic blocking, the decision-making process of the location and distribution was analyzed to establish a location-allocation scheme for emergency facilities. Coupled grey wolf optimizer(CWO) and visual convex point method was presented to solve the model, numerical examples show that the proposed algorithm can efficiently optimize path around barriers, and get the optimal location allocation scheme under different time satisfaction preference of various regions. The research results will provide a model and methodological design for the location and resource allocation of emergency facilities.
引文
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[4]朱建明.损毁情景下应急设施选址的多目标决策方法[J].系统工程理论与实践,2015, 35(3):720-727.Zhu J M. Methods of multi-objective decision-making for emergency facility location problem under failure scenario[J]. Systems Engineering—Theory&Practice, 2015, 35(3):720-727.
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[13] Farham M S, Siiral H, Iyigun C. The Weber problem in congested regions with entry and exit points[J]. Computers&Operations Research, 2015, 62:177-183.
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[17]马云峰.网络选址中基于时间满意的覆盖问题研究[D].武汉:华中科技大学,2005.Ma Y F. Time-satisfaction-based covering location problems on network[D]. Wuhan:Huazhong University of Science and Technology, 2005.
[18] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69:46-61.
[19] Saremi S, Mirjalili S Z, Mirjalili S M. Evolutionary population dynamics and grey wolf optimizer[J]. Neural Computing and Applications, 2015, 26(5):1257-1263.
[20] Komaki G M, Kayvanfar V. Grey wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time[J]. Journal of Computational Science, 2015, 8:109-120.
[21] Mirjalili S, Saremi S, Mirjalili S M, et al. Multi-objective grey wolf optimizer:A novel algorithm for multicriterion optimization[J]. Expert Systems with Applications, 2016, 47:106-119.
[22] Mahdad B, Srairi K. Blackout risk prevention in a smart grid based flexible optimal strategy using grey wolfpattern search algorithms[J]. Energy Conversion and Management, 2015, 98:411-429.
[23] Sulaiman M H, Mustaffa Z, Mohamed M R, et al. Using the gray wolf optimizer for solving optimal reactive power dispatch problem[J]. Applied Soft Computing, 2015, 32:286-292.
[24] Mohanty S, Subudhi B, Ray P K. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions[J]. IEEE Transactions on Sustainable Energy, 2016, 7(1):181—188.
[25] Lu C, Xiao S, Li X, et al. An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production[J]. Advances in Engineering Software, 2016, 99:161-176.
[26] Mirjalili S. How effective is the grey wolf optimizer in training multi-layer perceptrons[J]. Applied Intelligence,2015, 43(1):150-161.
[27]姚鹏,王宏伦.基于改进流体扰动算法与灰狼优化的无人机三维航路规划[J].控制与决策,2016, 31(4):701-708.Yao P, Wang H L. Three-dimensional path planning for UAV based on improved interfered fluid dynamical system and grey wolf optimizer[J]. Control and Decision, 2016, 31(4):701-708.
[2] Salman F S, Y(u|¨)cel E. Emergency facility location under random network damage:Insights from the Istanbul case[J]. Computers&Operations Research, 2015. 62(C):266-281.
[3] Elshaikl A, Salii S, Brimberg J, et aI. An adaptive perturbation-based heuristic:An application to the continuous p-centre problem[J]. Conmputers&Operations Research, 2016, 75(11):1-11.
[4]朱建明.损毁情景下应急设施选址的多目标决策方法[J].系统工程理论与实践,2015, 35(3):720-727.Zhu J M. Methods of multi-objective decision-making for emergency facility location problem under failure scenario[J]. Systems Engineering—Theory&Practice, 2015, 35(3):720-727.
[5]周愉峰,马祖军,王恪铭.应急物资储备库的可靠性P-中位选址模型[J].管理评论,2015, 27(5):198-208.Zhou Y F, Ma Z J, Wang K M. A reliability P-median location model for relief supplies reserve bases[J].Management Review, 2015, 27(5):198-208.
[6] Katz I N, Cooper L. Facility location in the presence of forbidden regions, I:Formulation and the case of Euclidean distance with one forbidden circle[J]. European Journal of Operational Research, 1981, 6(2):166-173.
[7] Aneja Y P, Parlar M. Technical note-algorithms for weber facility location in the presence of forbidden regions and/or barriers to travel[J]. Transportation Science, 1994, 28(1):70-76.
[8] Butt S E, Cavalier T M. An efficient algorithm for facility location in the presence of forbidden regions[J].European Journal of Operational Research, 1996, 90(1):56-70.
[9] Mc Garvey R CG, Cavalier T M. A global optimal approach to facility location in the presence of forbidden regions[J]. Computers&Industrial Engineering, 2003, 45(1):1—15.
[10] Amiri-Aref M, Javadian N, Tavakkoli-Moghaddam R, et al. A new mathematical model for the Weber location problem with a probabilistic polyhedral barrier[J]. International Journal of Production Research, 2013, 51(20):6110-6128.
[11] Bischoff M, Klamroth K. An efficient solution method for Weber problems with barriers based on genetic algorithms[J]. European Journal of Operational Research, 2007, 177(1):22-41.
[12] Bischoff M, Fleischmann T, Klamroth K. The multi-facility location-allocation problem with polyhedral barriers[J]. Computers&Operations Research, 2009, 36(5):1376-1392.
[13] Farham M S, Siiral H, Iyigun C. The Weber problem in congested regions with entry and exit points[J]. Computers&Operations Research, 2015, 62:177-183.
[14] Stanimirovic I P. Successive computation of some efficient locations of the Weber problem with barriers[J].Journal of Applied Mathematics and Computing, 2013, 42(1-2):193—211.
[15] Canbolat M S, Wesolowsky G O. A planar single facility location and border crossing problem[J]. Computers&Operations Research, 2012, 39(12):3156-3165.
[16] Canbolat M S, Wesolowsky G O. On the use of the Varignon frame for single facility Weber problems in the presence of convex barriers[J]. European Journal of Operational Research, 2012, 217(2):241-247.
[17]马云峰.网络选址中基于时间满意的覆盖问题研究[D].武汉:华中科技大学,2005.Ma Y F. Time-satisfaction-based covering location problems on network[D]. Wuhan:Huazhong University of Science and Technology, 2005.
[18] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69:46-61.
[19] Saremi S, Mirjalili S Z, Mirjalili S M. Evolutionary population dynamics and grey wolf optimizer[J]. Neural Computing and Applications, 2015, 26(5):1257-1263.
[20] Komaki G M, Kayvanfar V. Grey wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time[J]. Journal of Computational Science, 2015, 8:109-120.
[21] Mirjalili S, Saremi S, Mirjalili S M, et al. Multi-objective grey wolf optimizer:A novel algorithm for multicriterion optimization[J]. Expert Systems with Applications, 2016, 47:106-119.
[22] Mahdad B, Srairi K. Blackout risk prevention in a smart grid based flexible optimal strategy using grey wolfpattern search algorithms[J]. Energy Conversion and Management, 2015, 98:411-429.
[23] Sulaiman M H, Mustaffa Z, Mohamed M R, et al. Using the gray wolf optimizer for solving optimal reactive power dispatch problem[J]. Applied Soft Computing, 2015, 32:286-292.
[24] Mohanty S, Subudhi B, Ray P K. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions[J]. IEEE Transactions on Sustainable Energy, 2016, 7(1):181—188.
[25] Lu C, Xiao S, Li X, et al. An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production[J]. Advances in Engineering Software, 2016, 99:161-176.
[26] Mirjalili S. How effective is the grey wolf optimizer in training multi-layer perceptrons[J]. Applied Intelligence,2015, 43(1):150-161.
[27]姚鹏,王宏伦.基于改进流体扰动算法与灰狼优化的无人机三维航路规划[J].控制与决策,2016, 31(4):701-708.Yao P, Wang H L. Three-dimensional path planning for UAV based on improved interfered fluid dynamical system and grey wolf optimizer[J]. Control and Decision, 2016, 31(4):701-708.