An Optimization via Agent-based Simulation Framework to Integrate Stochastic Programming with Human Introduced Uncertainty
An Optimization via Agent-based Simulation Framework to Integrate Stochastic Programming with Human Introduced Uncertainty
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DOI：
10.1109/wsc40007.2019.9004909
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Mohammad Ramshani;Xueping Li;Anahita Khojandi;Lorna Treffert
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文献类型：
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作者：
Mohammad Ramshani;Xueping Li;Anahita Khojandi;Lorna Treffert
不确定性在几乎所有真实的世界优化问题中普遍存在。随机规划已经被广泛用于捕捉真实的世界优化问题的不确定性在许多不同的方面。然而，这些模型往往不足以充分捕捉一个系统或一个社会中涉及人类或子系统的相互作用所引入的随机性。另一方面，基于Agent的建模可以有效地处理系统中不同成员或元素之间的交互所产生的随机性。在这项研究中，我们开发了一个随机规划优化框架，通过嵌入基于代理的模型，允许由于系统参数的随机性以及代理之间的相互作用的不确定性。一个案例研究表明所提出的框架的有效性。
Uncertainty is ubiquitous in almost every real world optimization problem. Stochastic programming has been widely utilized to capture the uncertain nature of real world optimization problems in many different aspects. These models, however, often fall short in adequately capturing the stochasticity introduced by the interactions within a system or a society involving human beings or sub-systems. Agent-based modeling, on the other hand, can efficiently handle such randomness resulting from the interactions among different members or elements of a systems. In this study, we develop a framework for stochastic programming optimization by embedding an agent-based model to allow uncertainties due to both stochastic nature of system parameters as well as the interactions among the agents. A case study is presented to show the effectiveness of the proposed framework.