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• Study the unrelated parallel machines scheduling do not restricted to specific criteria. • An immune-inspired algorithm with self-adaptive search space exploitation operators. • Evaluation of the influence of operators in the algorithm using a Component Analysis. • Results are compatible with the state-of-art approaches designed for specific criteria. In the last few years, a lot of researchers addresses scheduling problems considering unrelated parallel machines to bridge theoretical problems and real-world manufacture environments. Most of these works consider objective function's characteristics to assist the algorithms in exploiting the search space. This assistance usually leads to a better convergence of the algorithms, but it tends to leave these algorithms restricted only to the own optimization criterion studied. This feature hinders to extend these works to optimization criteria with less theoretical visibility. This work proposes an immuno-inspired algorithm for the resolution of scheduling problems under unrelated parallel environments, evaluating the hypothesis that it is possible to construct a robust approach that is extensible to different optimization criteria, achieving results similar to the state-of-art approaches in the literature for specific criteria. Four case studies are proposed, each one addressing an optimization criterion, which is a benchmark for sequencing problems. The criteria are Total Weighted Tardiness, Makespan, Total Tardiness, and Total Weighted Completion Time. The experiments indicate that the results achieved by the proposed approach are compatible and, in many cases, superior to the state-of-art approaches for the optimization criteria studied, using computational resources compatible with those presented by these approaches. [ABSTRACT FROM AUTHOR]

This paper proposes an immune-inspired algorithm to the problem of minimising the makespan on unrelated parallel machines, with sequence dependent setup times. The initial population is generated through the construction phase of the Greedy Randomised Adaptive Search Procedure (GRASP). An evaluation function is proposed to help the algorithm escape from local optima. A Variable Neighbourhood Descent (VND) local search heuristic, which makes significant use of the characteristics of the problem, is proposed as a somatic hypermutation operator to accelerate the convergence of the algorithm. A population re-selection operator, which strategically keeps good quality solutions with a high level of dispersion in the search space, is also proposed. The experiments performed show that the proposed algorithm enables better results than those reported in recent literature studies. [ABSTRACT FROM AUTHOR]