Toward a Theory of Systems Engineering Processes: A Principal–Agent Model of a One-Shot, Shallow Process
Toward a Theory of Systems Engineering Processes: A Principal–Agent Model of a One-Shot, Shallow Process
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DOI：
10.1109/jsyst.2020.2964668
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发表时间：
2019-03
影响因子：
4.4
通讯作者：
Salar Safarkhani;Ilias Bilionis;Jitesh H. Panchal
中科院分区：
文献类型：
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作者：
Salar Safarkhani;Ilias Bilionis;Jitesh H. Panchal
系统工程过程（SEP）协调不同个体的努力，以生成满足特定要求的产品。由于工程师是自利的代理人，系统工程层次结构中不同层次的目标可能会偏离系统级目标，这可能会导致预算和进度超支。因此，需要一种系统工程理论来解释系统设计中的人类行为。物理科学的经验表明，通过研究简单的假设场景可以产生大量知识，但这些场景仍然保留了原始问题的某些方面。为此，本文的目的是研究最简单的可以想象的SEP，一个principalagent模型的一次性，浅SEP。我们假设，系统工程师（SE）最大限度地提高系统的预期效用，而子系统工程师（SSE）寻求最大限度地提高他们的预期效用。此外，SE无法监视sSE的工作，并且可能没有关于其类型的完整信息。然而，SE可以通过提出具体合同来激励sSE。为了获得最优激励，我们提出并数值求解一个双层优化问题。通过大量的模拟，我们研究了不同的系统级价值函数下的各种组合的努力成本，解决问题的技能，和任务的复杂性所产生的最优激励。我们的数值例子表明，传递给代理的要求增加的任务的复杂性和不确定性的增加，他们减少的代理的成本。
Systems engineering processes (SEPs) coordinate the effort of different individuals to generate a product satisfying certain requirements. As the involved engineers are self-interested agents, the goals at different levels of the systems engineering hierarchy may deviate from the system-level goals, which may cause budget and schedule overruns. Therefore, there is a need of a systems engineering theory that accounts for the human behavior in systems design. As experience in the physical sciences shows, a lot of knowledge can be generated by studying simple hypothetical scenarios, which nevertheless retain some aspects of the original problem. To this end, the objective of this article is to study the simplest conceivable SEP, a principalagent model of a one-shot, shallow SEP. We assume that the systems engineer (SE) maximizes the expected utility of the system, while the subsystem engineers (sSE) seek to maximize their expected utilities. Furthermore, the SE is unable to monitor the effort of the sSE and may not have complete information about their types. However, the SE can incentivize the sSE by proposing specific contracts. To obtain an optimal incentive, we pose and solve numerically a bilevel optimization problem. Through extensive simulations, we study the optimal incentives arising from different system-level value functions under various combinations of effort costs, problem-solving skills, and task complexities. Our numerical examples show that, the passed-down requirements to the agents increase as the task complexity and uncertainty grow and they decrease with increasing the agents' costs.