考虑物流服务水平的物流配送规划多目标模型

doi:10.3969/j.issn.0258-2724.2012.05.023

过晓芳,
王宇平^,

基金项目:

国家自然科学基金资助项目(60873099)

详细信息

通讯作者:
王宇平(1961-),男,教授,博士生导师,主要研究方向为智能计算与数据挖掘,E-mail:ywang@xidian.edu.cn

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出版历程
- 收稿日期:2011-09-09
- 刊出日期:2012-10-25

Multi-objective Model for Logistics Distribution Programming Considering Logistics Service Level

GUO Xiaofang,
WANG Yuping^,

摘要

摘要:为了提高顾客对物流服务的满意度,以最小化物流系统总费用和最大化物流服务水平为优化目标,考虑配送中心现有货物周转能力和产品配送时间对物流服务水平的影响,构建了三级供应链模式下物流配送规划的多目标优化模型.针对多目标优化问题不存在单一最优解的特点,利用预先设定的偏好区域和随机生成的权向量构造了适应度函数,提出了基于偏好的多目标进化算法求解模型.算例结果表明:通过求解多目标优化模型,得到10组非劣解,即10种物流配送方案均处于决策者的主观偏好范围内;各方案的物流系统总费用与服务水平成反比关系.
- 货物周转能力/
- 顾客满意度/
- 物流服务水平/
- 物流配送规划/
- 偏好多目标
Abstract:To improve the customer satisfaction on the service of logistics distribution, a multi-objective optimization model for logistics distribution programming under the three-level supply chain was developed. In this model, minimization of total expenses and maximization of logistics service level were optimization objectives, and the influence of both the commodity processing ability and distribution time on the logistics service level was taken into account. Given that there is no single optimal solution for multi-objective optimization problems, a preference-based multi-objective evolutionary algorithm for solving the model was proposed with a pre-defined preference area and randomly generated weight vector used to establish the fitness function. The results show that by solving the model, ten non-inferior solutions are obtained, each of which represents a distribution program. The ten solutions without exception lie in the subjective preference area of the decision maker and reflect the conflict relationship between total expenses and logistics service level.
- ability of commodity turnaround/
- customer satisfaction/
- logistics service level/
- logistics distribution programming/
- preference-based multi-objective

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参考文献 (14)

KONFORTY Y, TAMIR A. The single facility location problem with minimum distance constrains[J]. Location Science, 1998, 5(3): 147-163.

BRIMBERG J, HANSEN P, MLADENOVIC N. Decomposition strategies for large-scale continuous location-allocation problems[J]. Intemational Joumal of Management Mathematics, 2006, 17(4): 307-316.

鲁晓春,詹和生. 关于配送中心重心法选址的研究[J]. 北方交通大学学报,2000,24(6): 108-110. LU Xiaochun, ZHAN Hesheng. Study on address selection of distributing center using barycentric method[J]. Journal of Northern Jiaotong University, 2006, 24(6): 108-110.

杨波. 物流配送中心选址的随机数学模型[J]. 中国管理科学,2002,10(5): 57-61. YANG Bo. A randomized mathematic model of logistic system and allocation of its distribution center[J]. Chinese Journal of Management Science, 2002, 10(5): 57-61.

PIRKUL H, JAYARAMAN V. A multi-commodity, multi-plant, capacitated facility location problem: formulation and efficient heuristic solution[J]. Computers and Operations Research, 1998, 25(10): 869-878.

RODRÍGUEZ J J S, GARCÍA C G, PÉREZ J M, et al. A general model for the undesirable single facility location problem[J]. Operations Research Letters, 2006, 34(4): 427-436.

杨波. 多品种随机数学模型的物流配送中心选址问题[J]. 中国管理科学,2003,11(2): 45-49. YANG Bo. A randomized mathematic model of logistic system and allocation of its distribution center[J]. Chinese Journal of Management Science, 2003, 11(2): 45-49.

KLOSE A, DREXL A. Facility location models for distribution system design[J]. European Journal of Operational Research, 2005, 162(1): 4-29.

BIENSTOCKA C C, ROYNEB M B. An expanded model of logistics service quality: incorporating logistics information technology[J]. Int. J. Production Economics, 2008, 113(1): 205-222.

BARAHONA F, CHUDAK F A. Near-optimal solutions to large-scale facility location problems[J]. Discrete Optimization, 2005, 2(1): 35-50.

TANG Xifeng, MAO Haijun, LI Xuhong. Logistics facility location model based on reliability within the supply chain//Proceedings of the 4th IEEE International Conference on Management of Innovation and Technology. Bangkok: [s.n.], 2008: 1099-1103.

韩庆兰. 物流设施规划的多目标优化模型[J]. 控制与决策,2006,21(8): 957-960. HAN Qinglan. Multi-target optimization model of logistics establishment programming[J]. Control and Decision, 2006, 21(8): 957-960.

ISHIBUCHI H. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling[J]. IEEE Trans. on Evolutionary Computation, 2003, 7(2): 204-233.

DEB K, PRATAP A, AGRARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGAⅡ[J]. IEEE Trans. on Evolutionary Computation, 2002, 6(2): 182-197.

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