Queue-time constraints (QTC) define a limit on the time that a lot can wait between two process steps in its flow. In semiconductor manufacturing, lots that exceed that time limit experience yield loss, need rework, or get scraped. QTCs are difficult to schedule, since a lot needs to wait to be released to the first process step until there is available capacity to process the final step. However, exactly calculating if there is enough capacity is computationally expensive. In this work we propose a deep Reinforcement Learning (RL) method to manage releasing lots into the queue time constraint. We analyze the performance of our RL method and compare it to seven baseline solutions. Our empirical evaluation shows that the RL method outperforms the baselines in five performance metrics including the number of queue-time violations and makespan, while requiring negligible online compute time.

In semiconductor manufacturing, production units (wafers) are transferred and processed in lots. While the current convention is a lot size of 25 wafers in semiconductor manufacturing, there has been much discussion about changing this standard to a smaller value. The principal motivation behind moving to smaller lot sizes is to decrease the average cycle time of a wafer. In this paper, we develop a simple queueing model to gain an insight into the relationship among cycle time, lot size, and the fab technology level. This analysis shows that, depending on the system parameters, moving to smaller lot sizes does not always yield cycle time improvements.

This paper argues that the cloud computing industry faces many decision problems where operations research OR could add tremendous value. To this end, we provide an OR perspective on cloud computing in three ways. First, we compare the cloud computing process with the traditional pull supply chain and introduce the cloud IT supply chain as the system of moving information from suppliers to consumers through the cloud network. Second, based on this analogy, we organize the cloud computing decision space by identifying the problems that need to be solved by each player in the supply chain---namely, 1 cloud providers, 2 cloud consumers, and 3 cloud brokers. We list the OR problems of interest from each player's perspective and discuss the tools that may need to be developed to solve them. Third, we survey past and current research in this space and discuss future research opportunities in cloud computing operations research.

Businesses deal with huge databases over a geographically distributed supply network. When this is combined with scheduling and planning needs, it becomes too difficult to handle. Recently, Fast Consumer Goods sector tends to consolidate their manufacturing facilities on a single supplier serving to a distributed customer network. This decentralized structure causes imperfect information sharing between customers and the supplier. We model this problem as a single machine distributed scheduling problem with job agents representing the customers and the machine agent representing the supplier. We developed Auction Based Algorithm by exploiting the opportunity to use game theoretic approach to solve the problem in the decentralized utility case. Results of our extensive computational experiments indicate that Auction Based Algorithm converges to the upper bound found for the total utility measure.