Search

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]

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]