We study parallel machine scheduling for makespan minimization with uncertain job processing times. To incorporate uncertainty and generate solutions that are, in some way, insensitive to unfolding information, three different modeling paradigms are adopted: a robust model, chance-constrained distributionally model. focus on devising generic solution methods can efficiently handle these models....

Real-life parallel machine scheduling problems can be characterized by: (i) limited information about the exact task duration at time, and (ii) an opportunity to reschedule remaining tasks each time a processing is completed becomes idle. Robust optimization natural methodology cope with first characteristic of uncertainty, yet existing literature on robust does not explicitly consider second p...

Abstract In this paper, we study an important real-life scheduling problem that can be formulated as unrelated parallel machine with sequence-dependent setup times, due dates, and eligibility constraints. The objective is to minimise total tardiness makespan. We adapt extend a mathematical model find optimal solutions for small instances. Additionally, propose several variants of simulated anne...

In the last few decades, deep learning based on neural networks has become popular for classification tasks, which combines feature extraction with tasks and always achieves satisfactory performance. Non-parallel hyperplane support vector machine (NPHSVM) aims at constructing two non-parallel hyperplanes to classify data extracted features are used be input NPHSVM. As NPHSVM, will greatly influ...