不同风险偏好下虚拟企业风险规划研究

运筹与管理 ›› 2014, Vol. 23 ›› Issue (1): 234-243.

不同风险偏好下虚拟企业风险规划研究

卢福强¹, 黄敏², 毕华玲¹, 孙福权¹

1.东北大学秦皇岛分校,河北秦皇岛 066004;
2.东北大学信息科学与工程学院,流程工业综合自动化国家重点实验室,辽宁沈阳 110819

收稿日期:2011-09-25 出版日期:2014-01-25
作者简介:黄敏(1968-),女,博士,教授,博导,研究方向:虚拟企业风险管理,4PL风险管理等。
基金资助:
国家杰出青年科学基金资助项目(71325002,61225012);国家自然科学基金资助项目(71071028,70931001,71021061);高等学校博士学科点专项科研基金优先发展领域资助课题(20120042130003);高等学校博士学科点专项科研基金资助课题(20110042110024);中央高校基本科研业务专项基金(N110204003,N120104001);辽宁自然科学基金资助项目(201204796)

Risk Programming for Virtual Enterprise Based on Various Risk Preferences

LU Fu-qiang¹, HUANG Min², BI Hua-ling¹, SUN Fu-quan¹

1. Northeastem University at Qinhuangdao, Qinhuangdao 0666004, China;
2. College of Information Science and Engineering, Northeastern University; State Key Laboratory of Synthetical Automation for Process Industries(Northeastern University), Shenyang 110819, China

Received:2011-09-25 Online:2014-01-25

摘要/Abstract

摘要： 针对虚拟企业风险规划问题,在分析其各种风险具有随机性的特点的基础上,运用随机规划理论,分别建立风险规划的期望值模型和机会约束规划模型来描述决策者在不同风险偏好下的决策行为。针对所建立的模型,分别设计了基于蒙特卡罗模拟的粒子群优化算法、遗传算法和蚁群算法对其进行求解。仿真分析表明期望值模型较好地描述了风险中性决策者的决策行为,机会约束规划模型随着其偏好系数取值的不同描述了不同风险偏好(风险厌恶、风险中性、风险爱好)决策者的决策行为。通过对三种算法仿真结果的比较分析,表明基于蒙特卡罗模拟的粒子群优化算法在寻优能力、稳定性和收敛速度等方面优于其余两种算法,是解决此类风险规划问题的有效手段。

关键词: 虚拟企业, 风险偏好, 期望值模型, 机会约束规划模型, 粒子群优化算法

Abstract: For the stochastic characteristics of each risk for risk programming of virtual enterprise, with the stochastic programming theory, an expected value model and a chance constraint programming model are proposed to describe the decision behavior under various risk preferences. A Monte Carlo Simulation based Particle Swarm Optimization(MCS-PSO), Genetic Algorithm(MCS-GA)and Ant Colony Optimization(MCS-ACO)are designed to solve the models respectively. The simulation analysis shows that the expected value model describes the decision behavior of risk-neutral decision maker, and the chance constraint programming model describes the decision behavior of decision makers with various risk preference(risk-neutral, risk-averse, risk-loving)while the preference coefficient has different values. The comparison of the simulation results from the three proposed algorithm shows that the MCS-PSO performs better than MCS-GA and MCS-ACO on searching ability, reliability and convergence speed, and MCS-PSO is an effective way to solve this kind of risk programming problems.

Key words: virtual enterprise, risk preference, expected value model, chance constraint programming model, particle swarm optimization

中图分类号:

N949

卢福强, 黄敏, 毕华玲, 孙福权. 不同风险偏好下虚拟企业风险规划研究[J]. 运筹与管理, 2014, 23(1): 234-243.

LU Fu-qiang, HUANG Min, BI Hua-ling, SUN Fu-quan. Risk Programming for Virtual Enterprise Based on Various Risk Preferences[J]. Operations Research and Management Science, 2014, 23(1): 234-243.

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