Uncertainty Management in Optimal Disassembly Planning Through Learning-Based Strategies

AbstractThis赠款提供资金的分析框架的发展能够代表，分析和最终管理的广泛水平的不确定性固有的新兴逆向物流过程。具体的应用领域将是最优拆卸规划（ODP）问题，这构成了任何逆向物流过程网络的核心活动之一。拟议研究计划中的具体步骤是：（i）开发拆卸过程决策的正式表示，这将能够明确地描述所涉及的不确定性;（ii）面对上述不确定性的最佳拆卸计划的特征;以及（iii）开发计算有效和高效的算法，能够分析过程不确定性的影响，并最终通过观察过程得出优化的拆卸计划行为在步骤（i）中寻求的代表性框架将基于Petri网建模框架，该框架是离散事件系统动态建模的标准框架之一。步骤（ii）和（iii）将基于动态规划理论和一些新兴的变体，称为“强化学习”算法。衍生技术的有效性和效率将进行评估，并通过应用程序上的一些典型的案例研究，从相关literature.If成功，本研究的结果将导致更好地了解ODP问题中所涉及的不确定性，他们将提供一个明确处理这些不确定性的方法框架。通过这种方式，它们将进一步增强所采取的优化方法与特定ODP问题背景的相关性，因为它们将使决策过程能够适应当前的操作和经济条件，并且它们最终将导致更环保和经济上荒谬的拆卸计划。同时，所提出的研究将丰富计算学习理论领域，因为通过利用特定的问题结构，预计将为最近出现的算法提供一些新的实现见解和技术。最后，拟议的研究计划和传播的衍生结果将提高认识的制造和物流界关于（i）的作用，在当代生产系统中的不确定性，和（ii）正式的方法和工具，以有效地处理这种不确定性的可用性。

AbstractThis grant provides funding for the development of an analytical framework able to represent, analyze and eventually manage the extensive levels of uncertainty inherent in the emerging reverse logistics processes. The particular application area will be the optimal disassembly planning (ODP) problem, which constitutes one of the core activities in any reverse logistics process network. Specific steps in the proposed research program are: (i) the development of a formal representation of the decision making underlying the disassembly process, that will be able to explicitly characterize the involved uncertainties; (ii) the characterization of the optimal disassembly plan in the face of the aforementioned uncertainties; and (iii) the development of computationally effective and efficient algorithms able to analyze the impact of the process uncertainties and to eventually derive an optimized disassembly plan through observation of the process behavior. The representational framework sought in Step (i) will be based on the Petri net modeling framework, which is one of the standard frameworks for modeling discrete event system dynamics. Steps (ii) and (iii) will be based on Dynamic Programming theory and some emerging variants of it known as "Reinforcement Learning" algorithms. The effectiveness and efficiency of the derived techniques will be assessed and demonstrated through application on a number of prototypical case studies to be obtained from the relevant literature.If successful, the results of this research will lead to a better understanding of the uncertainties involved in the ODP problem, and they will provide a methodological framework for explicitly dealing with these uncertainties. In this way, they will further enhance the relevance of the undertaken optimization approaches to the particular ODP problem context, since they will enable the adaptation of the decision making process to the prevailing operational and economic conditions, and they will eventually lead to more environmentally benign and economically ludicrous disassembly plans. At the same time, the proposed research will enrich the field of computational learning theory since, by taking advantage of the particular problem structure, it is expected to offer a number of new implementation insights and techniques for recently emerged algorithms. Finally, the proposed research program and the dissemination of the derived results will increase the awareness of the manufacturing and logistics community regarding (i) the role of the uncertainty in contemporary production systems, and (ii) the availability of formal methods and tools to effectively deal with this uncertainty.