This paper deals with semi-online scheduling on two parallel batch processing machines with jobs’ nondecreasing processing times. Our objective is to minimize the makespan. A batch processing machine can handle up to B jobs simultaneously. We study unbounded model which means that B is sufficiently large. The jobs that are processed together construct a batch, and all jobs in a batch start and ...

This paper considers the scheduling problem of parallel batch processing machines with non-identical job size and processing time. In this paper, a new mathematical model with ready time and batch size constraints is presented to formulate the problem mathematically, in which simultaneous reduction of the makespan and earliness-tardiness is the objective function. In recent years, the nature-in...

We study the problem of minimizing makespan for part machining on a multi-spindle heads machine with a mobile table. We show that this problem can be formulated as a scheduling problem for a single batch machine with compatibility and precedence constraints between jobs. Jobs grouped in a batch are processed simultaneously, but the difference from considered in the literature max-batch machine ...

We propose Batch-Expansion Training (BET), a framework for running a batch optimizer on a gradually expanding dataset. As opposed to stochastic approaches, batches do not need to be resampled i.i.d. at every iteration, thus making BET more resource efficient in a distributed setting, and when disk-access is constrained. Moreover, BET can be easily paired with most batch optimizers, does not req...

In this paper, we discuss the “batch processing” problem, where there are multiple jobs to be processed in flow shops. These jobs can however be formed into batches and the number of jobs in a batch is limited by the capacity of the processing machines to accommodate the jobs. The processing time required by a batch in a machine is determined by the greatest processing time of the jobs included...

Apache Flink1 is an open-source system for processing streaming and batch data. Flink is built on the philosophy that many classes of data processing applications, including real-time analytics, continuous data pipelines, historic data processing (batch), and iterative algorithms (machine learning, graph analysis) can be expressed and executed as pipelined fault-tolerant dataflows. In this pape...

We analyze batch-scheduling problems that arise in connection with certain industrial applications. The models concern processing on a single max-batch machine with the additional feature that the tasks of the same batch have to be compatible. Compatibility is a symmetric binary relation—the compatible pairs are described with an undirected “compatibility graph”, which is often an interval grap...

In this research, a single batch processing machine scheduling problem with minimization of total earliness and tardiness as the objective function is investigated.We first formulate the problem as a mixed integer linear programming model. Since the research problem is shown to be NP-hard, an improved memetic algorithmis proposed to efficiently solve the problem. To further enhance the memetic ...