We introduce the batch sequencing problem (BSP) with item and batch availability for the singlemachine and two-machine ow-shop case. We propose a genetic algorithm which solves the BSP through a decomposition into a Phase I-Batching and a Phase II-Scheduling decision. The batch sequencing problem is closely related to the discrete lotsizing and scheduling problem (DLSP). Computational experienc...

Recent deep learning models are difficult to train using a large batch size, because commodity machines may not have enough memory accommodate both the model and data size. The size is one of hyper-parameters used in training model, it dependent on limited by target machine capacity can only fit into remaining after uploaded. Moreover, item also an important factor if each larger then that beco...

A mathematical programming model for a class of single machine family scheduling problems is described in this technical report, with the aim of comparing the performance in solving the scheduling problem by means of mathematical programming with the performance obtained when using optimal control strategies, that can be derived from the application of a dynamic programming-based methodology pr...

Batch Normalization is quite effective at accelerating and improving the training of deep models. However, its effectiveness diminishes when the training minibatches are small, or do not consist of independent samples. We hypothesize that this is due to the dependence of model layer inputs on all the examples in the minibatch, and different activations being produced between training and infere...

A scheduling problem is characterized by the properties of the jobs, the properties of the machines, the properties of the processing constraints and the optimality criterion. The properties of the jobs involve the processing time of the jobs, the release dates, deadlines and priorities. The properties of the machines are the number of machines, processing speeds of the machines and machine con...

This paper considers identical parallel machines scheduling problem with past-sequence-dependent setup times, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of the processing times of the jobs already processed and its scheduled position on the corresponding machine. In addition, the setup time of a job on each machin...