Upstream Processor Research Articles

Effect of upstream re-sequencing in controlling cycle time performance of batch processors

This paper demonstrates the effect of incorporating upstream sequence decisions in the cycle time control of batch processors. The focus is on dynamic control of a batch processor where near-future arrival information is available by predicting the process times on its upstream processor. In a particular decision point, the dynamic control task is to decide whether to start the batch process with one of the available product types immediately or to wait for future arrivals coming from the upstream processor. While considering a wait decision alternative, a re-sequencing approach at the upstream processor is proposed to shorten the arrival time of the desired product. Combining this architecture with a rolling horizon decision making, we propose a new look-ahead batch control strategy. A serial-batch processor system that is typical in wafer fabrication is modeled to demonstrate the approach. Simulation based benchmarking shows promising results.

Modelling and analysis of a multi-stage system involving batch processors with incompatible job families

Batch processors can concurrently process more than one job, and are commonly used in complex manufacturing systems such as wafer fabrication facilities (wafer fabs). Previous work has shown that there can be a significant reduction in cycle time when the control of the upstream serial processor is dependent on the batch processor. We wish to determine whether this is also true when the upstream processor is also a batch processor, with smaller capacity. We model a two-stage system with an upstream batch processor and a downstream batch processor under differing simple control policies as a continuous time-discrete state Markov chain. In doing so, we show that the concept of constraining the production of the upstream processor in accordance to the anticipated needs of the downstream batch processor can similarly reduce mean system cycle time when the upstream processor is a smaller batch processor. We also perform simulation experiments, using parameters close to those found in wafer fabs, and confirm that the strategy of prioritising the formation of larger batches for the downstream batch processor typically results in substantial cycle time reductions for the two-stage system.

Heuristic control of multiple batch processors with incompatible job families and future job arrivals

We analyse the problem of minimising the mean cycle time of a batch processing stage containing K > 1 batch processors in parallel with incompatible job families and future job arrivals. We provide an integer linear programming formulation and a dynamic program formulation for small problem instances. For larger problem instances, we propose an online heuristic policy MPC_REPEAT. At each instance a decision has to be made, MPC_REPEAT decomposes the problem of simultaneously assigning multiple batches to multiple processors into sequentially assigning multiple batches to multiple processors. When job families are uncorrelated, we show via simulation experiments that MPC_REPEAT has significantly lower mean cycle time than a previously proposed look-ahead method except when: (MPC_REPEAT ignores some job families AND the traffic intensity is high.) Our experiments also reveal that increasing the job family correlation of consecutive job arrivals results, with a few exceptions, in a mean cycle-time reduction, for both policies evaluated. This reduction in cycle time generally increases with: increasing number of job families, decreasing number of processors, and increasing time between job arrivals. Our findings imply that controlling the upstream processors, such that job families of consecutive job arrivals are correlated, can reduce the cycle time at the batch processing stage. Furthermore, the expected mean cycle time reduction due to this strategy can be substantially larger than that expected from switching to a more complex batch processing stage policy, under less stringent conditions.

Evaluating the performance of look-ahead policies for upstream serial processor with downstream batch processor serving incompatible job families and finite buffer sizes

Batch processors can concurrently process more than one job. In wafer fabrication, the processing time of a batch is independent of batch size, and only jobs from the same job family can be batched together. We consider a two-stage subsystem of a wafer fabrication facility (wafer fab), comprised of the diffusion furnace (a batch processor) and its upstream serial processor, with random job arrivals. We hypothesise that allowing the serial processor to anticipate the job family preference of the batch processor will reduce the overall cycle time of jobs passing through this system. To evaluate this hypothesis, we model the performance of the two-stage system under different system parameters and processor control policies as discrete state continuous time Markov chains. We characterise the system performance and show that the concept of constraining the production of the upstream processor according to the anticipated needs of the batch processor can reduce the mean cycle time of jobs being processed. We also perform simulation experiments to show that a simple heuristic based on this insight can translate to substantial cycle time reductions for systems with assumptions closer to those found in wafer fabs.

Controlling job arrivals with processing time windows into Batch Processor Buffer

We consider a two-stage manufacturing system composed of a batch processor and its upstream processor. Jobs exit the upstream processor and join a queue in front of the batch processor, where they wait to be processed. The batch processor has a finite capacity Q, and its processing time is independent of the number of jobs loaded into the batch processor. In certain manufacturing systems (including semiconductor wafer fabrication), a processing time window exists from the time instance the job exits the upstream processor till the time instance it enters the batch processor. If a job is not processed before reaching the end of its processing time window, job rework or validation is required. We model this drawback by assigning a reward R for each successfully processed job by the upstream processor, and a penalty C for each job that reaches the end of its processing time window without being processed by the batch processor. We initially assume an infinite job source in front of the serial processor and also assume that the batch processor is operated under a threshold policy. We provide a method for controlling the production of the serial processor, considering the processing time window between the upstream processor and the downstream batch processor. We then show how the serial processor control policy can be modified when the serial processor also experiences intermittent job arrival.

Control of a Single Batch Processor With Incompatible Job Families and Future Job Arrivals

We examine the problem of minimizing the mean cycle time of a batch processor with incompatible job families and future job arrivals. Batch processors frequently found in wafer fabrication include oxidation and diffusion ovens. When the problem is deterministic with a fixed number of job arrivals, the problem is NP-hard; an optimal dynamic program formulation is developed and empirically tested on small problem instances. For larger problem instances with possibly infinite job arrivals, an online heuristic based on model predictive control (MPC) is proposed. The proposed heuristic has two parameters: the number of job families considered <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> , and the number of future job arrivals <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> . Statistical analysis of simulation results shows that the MPC-based heuristic has significantly lower mean cycle time than a benchmark look-ahead method under the assumption that the arrival time is deterministic and job families of consecutive job arrivals are uncorrelated. These observations also hold true when the predicted arrival times can be erroneous, indicating that the suggested MPC-based heuristic is as robust as the benchmark with respect to errors in forecasted arrival times. When the job families of future job arrivals are positively correlated, the mean cycle time of jobs passing through the batch processor is significantly reduced, regardless of control policy. We infer that the introduction of correlation generates less improvement for policies that foresee events longer into the future. For this reason, when the correlation is high, the MPC-based heuristic, which considers events that occur farther into the future, may have worse performance than the benchmark. The results suggest two ways to improve the performance of the batch processor. First, we can use a heuristic that has better mean performance, which may entail much larger computational costs. Another option is to continue using a simple look-ahead policy at the batch processor, and focus attention on controlling the upstream processors, such that the batch processor arrival process exhibits correlated job families between consecutive arrivals. This introduction of correlation causes a significant reduction of cycle time regardless of system parameter and batch processor control policy.

Human Capital, Gender and Organisation in Transition Agriculture: Measuring and Explaining the Technical Efficiency of Bulgarian and Hungarian Farms

Based on survey data on Bulgarian and Hungarian crop and dairy farms, a double-peaked distribution of technical efficiency is observed. Several factors explain differences in efficiency. Human capital matters not only through age and education but also through gender, as farms with a higher proportion of women are more efficient. Contracting with upstream processors increases efficiency through facilitating the adoption of technology and access to credits. The superiority of family farms over corporate farms is confirmed for crops but not for dairy produce.

Human Capital, Gender and Organisation in Transition Agriculture: Measuring and Explaining the Technical Efficiency of Bulgarian and Hungarian Farms

Based on survey data on Bulgarian and Hungarian crop and dairy farms, a double-peaked distribution of technical efficiency is observed. Several factors explain differences in efficiency. Human capital matters not only through age and education but also through gender, as farms with a higher proportion of women are more efficient. Contracting with upstream processors increases efficiency through facilitating the adoption of technology and access to credits. The superiority of family farms over corporate farms is confirmed for crops but not for dairy produce.