带有遗憾值约束的4PL网络设计鲁棒优化模型与仿真
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摘要
第四方物流(4PL)网络运作过程中常因外部环境的干扰而发生中断,使网络安全受到威胁。考虑中断状态下4PL网络鲁棒优化设计问题,目标是构建在任意中断状态发生时仍能以较低的成本为客户提供满意服务的4PL网络。基于β-鲁棒解的定义,建立了带有遗憾值约束的4PL网络设计鲁棒优化模型。针对问题的NP-hard特性,利用磷虾群算法(KHA)对模型进行求解,并与人工鱼群算法(AFSA)进行了比较,通过仿真实例对算法的可行性和有效性进行了验证。仿真结果表明,KHA的性能优于经典的AFSA。通过对不同遗憾值β约束下4PL网络设计最佳方案的比较分析,验证了利用鲁棒优化模型设计4PL网络能够较好地规避风险,并达到最大限度节约成本的目的。
Operations of the Fourth Party Logistics(4PL) are often disrupted, due to the interference from the internal and external environment, which threatens the security of the network. This paper studies a 4PL network robust optimization design problem under unexpected disruptions. Our objective is to construct a 4PL network that can provide quality service to customers with a lower cost when disruption occurs. Based on the definition of β-robustness, a robust optimization model of 4PL network design with regret value constraints is developed. Due to the NP-hard characteristic of this type of problem, the Krill Herd Algorithm(KHA) and the Artificial Fish Swarm Algorithm(AFSA) are proposed as heuristic approaches to solve the proposed model. We further test the performance of the heuristic algorithms by simulation examples, which indicate that the KHA outperforms the classic AFSA. According to the analysis of the optimal structures of the 4PL network for different regret value β, the effectiveness of the robust optimization model is verified, which can avoid risk effectively and maximize cost savings.
Operations of the Fourth Party Logistics(4PL) are often disrupted, due to the interference from the internal and external environment, which threatens the security of the network. This paper studies a 4PL network robust optimization design problem under unexpected disruptions. Our objective is to construct a 4PL network that can provide quality service to customers with a lower cost when disruption occurs. Based on the definition of β-robustness, a robust optimization model of 4PL network design with regret value constraints is developed. Due to the NP-hard characteristic of this type of problem, the Krill Herd Algorithm(KHA) and the Artificial Fish Swarm Algorithm(AFSA) are proposed as heuristic approaches to solve the proposed model. We further test the performance of the heuristic algorithms by simulation examples, which indicate that the KHA outperforms the classic AFSA. According to the analysis of the optimal structures of the 4PL network for different regret value β, the effectiveness of the robust optimization model is verified, which can avoid risk effectively and maximize cost savings.
引文
[1] 姚建明. 4PL 模式下供应链资源整合的收益与风险决策[J]. 系统管理学报, 2011, 20(2): 180-187.
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[3] 李佳,刘艳秋,张颖,等. 考虑时间可靠度约束的4PL路径优化问题研究[J]. 工业工程, 2014, 17(4): 29-34.
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[8] Santoso T, Ahmed S, Goetschalckx M, et al. A stochastic programming approach for supply chain network design under uncertainty[J]. European Journal of Operational Research, 2005, 167(1): 96-115.
[9] Peng P,Snyder L V,Lim A,et al. Reliable logistics networks design with facility disruptions [J]. Transportation Research Part B, 2011, 45: 1190-1211.
[10] Meepetchdeey Y, Shah N. Logistical network design with robustness and complexity considerations [J]. International Journal of Physical Distribution & Logistics Management, 2007, 37(3): 201-222.
[11] 胡信布,何正文,徐渝.基于资源约束的突发事件应急救援鲁棒性调度优化[J].运筹与管理, 2013, 22(4): 72-79.
[12] Hahn G L, Kuhn H. Value-based performance and risk management in supply chains: A robust optimization approach [J]. International Journal of Production Economics, 2011(4): 106-116.
[13] Gandomi A H, Alavi A H. Krill Herd: A new bio-inspired optimization algorithm [J]. Communications in Nonlinear Science and Numerical Simulation, 2012 (17): 4831-4845.
[14] 田文迪,胡慕海,崔南方. 不确定性环境下鲁棒性项目调度研究综述[J]. 系统工程学报, 2014, 29(1): 135-144.
[15] Wang G G, Guo L H, Gandomi A H, et al. Simulated annealing-based Krill Herd algorithm for global optimization[J]. Abstract And Applied Analysis, 2013(11): 1-16.
[16] 李晓磊,邵之江,钱积新. 一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践, 2002, 22(11): 32-38.
[17] 谢凡荣. 运输网络中求最小费用最大流的一个算法[J]. 运筹与管理, 2000, 9(4): 33-38.
[2] Chen K H,Su C T. Activity assigning of fourth party logistics by particle swarm optimization-based preemptive fuzzy integer goal programming[J]. Expert System with Application, 2010, 37(5): 3630-3637.
[3] 李佳,刘艳秋,张颖,等. 考虑时间可靠度约束的4PL路径优化问题研究[J]. 工业工程, 2014, 17(4): 29-34.
[4] Cui Y, Huang M, Yang S, et al. Fourth party logistics routing problem model with fuzzy duration time and cost discount[J]. Knowledge-Based Systems, 2013, 50: 14-24.
[5] 王勇, 张战峰, 赖志柱. 基于 TOPSIS 法的第四方物流作业多属性优化指派模型[J]. 系统管理学报, 2011, 20(5): 569-577.
[6] 黄小原,晏妮娜.供应链鲁棒性问题的研究进展[J].管理学报, 2007,4(4):521-528.
[7] Poojari C A, Lucas C, Mitra G. Robust solutions and risk measures for a supply chain planning problem under uncertainty[J]. Journal of the Operational Research Society, 2008, 59: 2-12.
[8] Santoso T, Ahmed S, Goetschalckx M, et al. A stochastic programming approach for supply chain network design under uncertainty[J]. European Journal of Operational Research, 2005, 167(1): 96-115.
[9] Peng P,Snyder L V,Lim A,et al. Reliable logistics networks design with facility disruptions [J]. Transportation Research Part B, 2011, 45: 1190-1211.
[10] Meepetchdeey Y, Shah N. Logistical network design with robustness and complexity considerations [J]. International Journal of Physical Distribution & Logistics Management, 2007, 37(3): 201-222.
[11] 胡信布,何正文,徐渝.基于资源约束的突发事件应急救援鲁棒性调度优化[J].运筹与管理, 2013, 22(4): 72-79.
[12] Hahn G L, Kuhn H. Value-based performance and risk management in supply chains: A robust optimization approach [J]. International Journal of Production Economics, 2011(4): 106-116.
[13] Gandomi A H, Alavi A H. Krill Herd: A new bio-inspired optimization algorithm [J]. Communications in Nonlinear Science and Numerical Simulation, 2012 (17): 4831-4845.
[14] 田文迪,胡慕海,崔南方. 不确定性环境下鲁棒性项目调度研究综述[J]. 系统工程学报, 2014, 29(1): 135-144.
[15] Wang G G, Guo L H, Gandomi A H, et al. Simulated annealing-based Krill Herd algorithm for global optimization[J]. Abstract And Applied Analysis, 2013(11): 1-16.
[16] 李晓磊,邵之江,钱积新. 一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践, 2002, 22(11): 32-38.
[17] 谢凡荣. 运输网络中求最小费用最大流的一个算法[J]. 运筹与管理, 2000, 9(4): 33-38.