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...

This paper reviews results related to the parallel machine scheduling problem under availability constraints. The motivation of this paper comes from the fact that no survey focusing on this specific problem was published. The problems of single machine, identical, uniform, and unrelated parallel machines under different constraints and optimizing various objective functions were analyzed. For ...

The optimization of scheduling problems is based on different criteria to optimize. One of the most important criteria is the minimization of completion time of the last task on the last machine called makespan. In this paper, we present a parallel algorithm for solving the permutation flow shop problem. Our algorithm is a basic parallel distributed algorithm deployed in a grid of computer (Gri...