Joint maintenance and production operations decision making in flexible manufacturing systems

In highly flexible and highly integrated manufacturing systems such as semiconductor manufacturing, equipment has the capability of conducting different manufacturing operations and/or producing at various speeds. In such systems, degradation of a machine depends highly on the operations performed on it. Selection of operations executed on an equipment changes the degradation dynamics and hence directly affects preventive maintenance (PM) decisions. On the other hand, PM actions interrupt production and change the system reliability and equipment availability, which in turn directly affects decisions as to which operations should be performed on which piece of equipment. These strong dynamic interactions between equipment condition, operations executed on the equipment and product quality necessitate a methodology that integrates the decisions of maintenance scheduling and production operations. Currently, maintenance and production operations decision-making are two decoupled processes. To address the aforementioned problems, in this dissertation, we devise integrated decision-making policies for maintenance scheduling and production operations in flexible manufacturing systems (FMS) optimizing a customizable objective function that takes into account operation-dependent degradation models and production targets. The objective function consists of costs associated with scheduled and unscheduled maintenance, rewards for successfully completed products and penalties for missed production targets. In order to maximize the objective function, a paradigm based on metaheuristic optimization and evaluation of candidate solutions via discrete-event simulations of operations of the underlying manufacturing system is used. Firstly, we propose an operation-dependent decision-making policy for a multiple-product/multiple-equipment manufacturing system, where each product requires several operations for completion and the sequence in which different product types are produced is a priori given. The proposed method is tested in simulations of a cluster tool and the results show that operation-dependent maintenance decision-making outperforms the case where maintenance decisions are made without considerations of operation-dependent degradation dynamics. Secondly, we propose an integrated decision-making policy for maintenance scheduling and product sequencing where the sequence in which different product types can be arranged in a way to maximize the customizable profit function. The results show that jointly making maintenance and production sequencing decisions consistently and often significantly outperforms the current practice of making these decisions separately. Finally, a joint maintenance scheduling and production operations decision making policy is proposed for a flexible manufacturing system where the degradation states of the equipment are not perfectly observable, but are rather hidden states of a known Hidden Markov Model (HMM). Proposed integrated decision-making policy under imperfect degradation state observations is shown to consistently outperform the benchmark policies.