Minimizing total completion time on batch processing machines

We study the problem of minimizing total completion time on single and parallel batch processing machines. A batch processing machine is one which can process up to B jobs simultaneously. The processing time of a batch is equal to the largest processing time among all jobs in the batch. This problem is motivated by burn-in operations in the final testing stage of semiconductor manufacturing and is expected to occur in other production environments. We provide an exact solution procedure for the single-machine problem and heuristic algorithms for both single and parallel machine problems. While the exact algorithms have limited applicability due to high computational requirements, extensive experiments show that the heuristics are capable of consistently obtaining near-optimal solutions in very reasonable CPU times.

Heuristic algorithms for scheduling of a batch processor in automobile gear manufacturing

In this paper we address a scheduling problem for minimising total weighted tardiness. The motivation for the paper comes from the automobile gear manufacturing process. We consider the bottleneck operation of heat treatment stage of gear manufacturing. Real life scenarios like unequal release times, incompatible job families, non-identical job sizes and allowance for job splitting have been considered. A mathematical model taking into account dynamic starting conditions has been developed. Due to the NP-hard nature of the problem, a few heuristic algorithms have been proposed. The performance of the proposed heuristic algorithms is evaluated: (a) in comparison with optimal solution for small size problem instances, and (b) in comparison with `estimated optimal solution' for large size problem instances. Extensive computational analyses reveal that the proposed heuristic algorithms are capable of consistently obtaining near-optimal solutions (that is, statistically estimated one) in very reasonable computational time.

Modelling of a batch sonochemical reactor

Ultrasonication of aqueous KI solution is known to yield I2 due to reaction of iodide ions with hydroxyl radicals, which in turn are generated due to cavitation. Based on this conceptual framework, a model has been developed to predict the rate of iodine formation for KI solutions of various concentrations under different gas atmospheres. The model follows the growth and collapse of a gas—vapour cavity using the Rayleigh—Plesset bubble dynamics equation. The bubble is assumed to behave isothermally during its growth phase and a part of the collapse phase. Thereafter it is assumed to collapse adiabatically, yielding high temperatures and pressures. Thermodynamic equilibrium is assumed in the bubble at the end of collapse phase. The contents of the bubble are assumed to mix with the liquid, and the reactor contents are assumed to be well stirred. The model has been verified by conducting experiments with KI solutions of different concentrations and using different gas atmospheres. The model not only explains these results but also the existence of a maximum when Ar---O2 mixtures of different compositions are employed.

Modelling of a batch sonochemical reactor

Ultrasonication of aqueous KI solution is known to yield I2 due to reaction of iodide ions with hydroxyl radicals, which in turn are generated due to cavitation. Based on this conceptual framework, a model has been developed to predict the rate of iodine formation for KI solutions of various concentrations under different gas atmospheres. The model follows the growth and collapse of a gas-vapour cavity using the Rayleigh-Plesset bubble dynamics equation. The bubble is assumed to behave isothermally during its growth phase and a part of the collapse phase. Thereafter it is assumed to collapse adiabatically, yielding high temperatures and pressures. Thermodynamic equilibrium is assumed in the bubble at the end of collapse phase. The contents of the bubble are assumed to mix with the liquid, and the reactor contents are assumed to be well stirred. The model has been verified by conducting experiments with KI solutions of different concentrations and using different gas atmospheres. The model not only explains these results but also the existence of a maximum when Ar-O2 mixtures of different compositions are employed.

Executing Long-running Multi-component Applications on Batch Grids

Computational grids are increasingly being used for executing large multi-component scientific applications. The most widely reported advantages of application execution on grids are the performance benefits, in terms of speeds, problem sizes or quality of solutions, due to increased number of processors. We explore the possibility of improved performance on grids without increasing the application’s processor space. For this, we consider grids with multiple batch systems. We explore the challenges involved in and the advantages of executing long-running multi-component applications on multiple batch sites with a popular multi-component climate simulation application, CCSM, as the motivation.We have performed extensive simulation studies to estimate the single and multi-site execution rates of the applications for different system characteristics.Our experiments show that in many cases, multiple batch executions can have better execution rates than a single site execution.

Middleware for Long-running Applications on Batch Grids. Student Research Symposium poster

Computational grids with multiple batch systems (batch grids) can be powerful infrastructures for executing long-running multicomponent parallel applications. In this paper, we have constructed a middleware framework for executing such long-running applications spanning multiple submissions to the queues on multiple batch systems. We have used our framework for execution of a foremost long-running multi-component application for climate modeling, the Community Climate System Model (CCSM). Our framework coordinates the distribution, execution, migration and restart of the components of CCSM on the multiple queues where the component jobs of the different queues can have different queue waiting and startup times.

Adaptive Executions of Multi-Physics Coupled Applications on Batch Grids

Long running multi-physics coupled parallel applications have gained prominence in recent years. The high computational requirements and long durations of simulations of these applications necessitate the use of multiple systems of a Grid for execution. In this paper, we have built an adaptive middleware framework for execution of long running multi-physics coupled applications across multiple batch systems of a Grid. Our framework, apart from coordinating the executions of the component jobs of an application on different batch systems, also automatically resubmits the jobs multiple times to the batch queues to continue and sustain long running executions. As the set of active batch systems available for execution changes, our framework performs migration and rescheduling of components using a robust rescheduling decision algorithm. We have used our framework for improving the application throughput of a foremost long running multi-component application for climate modeling, the Community Climate System Model (CCSM). Our real multi-site experiments with CCSM indicate that Grid executions can lead to improved application throughput for climate models.