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In this paper, we analyze single machine scheduling problems under the following minimization objectives: the maximum completion time (makespan), the total completion time and the maximum lateness, including fundamental practical aspects, which often occur in industrial or manufacturing reality: release dates, due dates, setup times, precedence constraints, deterioration (aging) of machines, as well as maintenance activities. To solve the problems, we propose an efficient representation of a solution and a fast neighborhood search technique, which calculates an approximation of criterion values in a constant time per solution in a neighborhood. On this basis, a novel approximate/exact search technique, using exact as well as approximate criterion values during search process, is introduced and used to develop efficient metaheuristic algorithms dedicated to the considered problems. Their efficiency is verified during computational experiments. [ABSTRACT FROM AUTHOR]

This note concerns the paper [Janiak A, Kovalyov MY, Lichtenstein M. On a single machine-scheduling problem with separated position and resource effects. Optimization; 2013. doi:10.1080/02331934.2013.804077], which presents an analysis, a counterexample and a pseudocode related with our proof of optimality for a resource allocation algorithm given in [Rudek A, Rudek R. A note on optimization in deteriorating systems using scheduling problems with the aging effect and resource allocation models. Comput. Math. Appl. 2011;62:1870–1878]. We show that the discussed analysis is based only on one part of our proof omitting its integral second part, which is the source of misunderstanding. The considered counterexample is applied for an algorithm, which was not the method presented in our paper, whereas our algorithm provides the correct result for the mentioned counterexample. The provided pseudocode of the resource allocation algorithm, which is presented as the correct method, is a pseudocode of the algorithm described in our paper. Therefore, we show that the results presented in our paper are correct. [ABSTRACT FROM AUTHOR]

Abstract: The importance of the role that learning plays in manufacturing, industry and computer systems is undeniable as well as the profit that can be increased if this phenomenon is taken into consideration for short- and long-term optimization. In this paper, we focus on scheduling jobs on a single processor, where its effectiveness can increase with the number of processed jobs, to minimize one of the following objectives: the maximum completion time with the release dates, the maximum lateness and the number of late jobs. It is proved that these well known polynomially solvable problems become at least NP-hard with the considered learning models. To solve them we provide some elimination procedures that are used to construct a branch and bound algorithm. Furthermore, we propose some fast heuristics for the problem of minimizing the number of late jobs with the general model of the learning effect. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

There is a limited number of results concerning fast and efficient solution algorithms for scheduling problems with general learning models. Therefore, we develop a fast insert neighborhood search (FINS) for a group of identical parallel machine scheduling problems with general learning curves under the following minimization objectives: the maximum completion time (makespan) and the maximum lateness. Our fast search scheme allows to calculate criterion values in a constant time per solution in a neighborhood. The application is presented on the basis of Nawaz–Enscore–Ham's method (NEH), iterative local search, tabu search, particle swarm optimization and hybrid simulated annealing — tabu search algorithms, where the efficiency of their standard implementations are compared to their versions enhanced by our fast insert neighborhood search. The computational experiments confirm the theoretical analysis that our method essentially overwhelms the well known approaches. The metaheuristics augmented by our FINS are about 120 times faster than their standard versions and improve the criterion values by up to 25% for analyzed instances with 100–800 jobs and 5–80 machines. • Parallel machine scheduling problems with general learning curves are analyzed. • A fast insert neighborhood search for a group of scheduling problems is developed. • Iterative local search, NEH, tabu search, particle swarm optimization algorithms are enhanced. • The experiments confirm that our method overwhelms the standard approaches. [ABSTRACT FROM AUTHOR]