In this paper, we focus on real-life settings that require the development of new models of flowshop scheduling problems, where job processing times can increase with the number of processed jobs due to the aging effect and decrease by the allocation of additional resource. We analyse the makespan minimization flowshop problem with such model and also with the aging effect only. We prove that the considered problems and their special cases are still polynomially solvable under given conditions, and on their basis, we provide optimal polynomial time solution algorithms.

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In this paper, we consider an optimal sequence of tasks for systems that improve their performances due to autonomous learning (learning-by-doing). In particular, we focus on a problem of determining sequence of performed tasks for the autonomous learning systems to minimize the total weighted completion times of tasks. Fundamental for the presented approach is that schedule (a sequence of tasks) allows to efficiently utilize learning abilities of the system to optimize its objective, but it does not affect the system itself. To solve the problem, we prove an eliminating property that is used to construct a branch and bound algorithm and present some fast heuristic and metaheuristic methods. An extensive analysis of the efficiency of the proposed algorithms is also provided.

This short paper presents a preliminary analysis of the impact of model parameter uncertainty on the accuracy of solution algorithms for the scheduling problems with the learning effect. We consider the maximum completion time minimization flowshop problem with job processing times described by the power functions dependent on the number of processed jobs. To solve the considered scheduling problem we propose heuristic (NEH based) and metaheuristic (simulated annealing) algorithms. The numerical experiments show that NEH and simulated annealing are robust for this problem with respect to model parameter uncertainty.

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