Iterated Local Search Based Heuristic for Scheduling Jobs on Unrelated Parallel Machines with Machine Deterioration Effect

Santos, Vívian Ludimila Aguiar; Arroyo, José Elias Claudio; Carvalho, Thales Francisco Mota

doi:10.1145/2908961.2909053

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Motivated by the computational complexity and the practical relevance of the 1|ST sd, | ∑ problem, in this work we test the applicability of the metaheuristic Iterated Local Search (ILS) to find good quality solutions for realistic size problem instances. We use ILS because it is a simple and generally applicable heuristic that has proved to be very effective for solving a wide range of difficult combinatorial optimization problems, especially vehicle routing problems [35][36][37][38][39] and scheduling problems [40][41][42][43][44][45]. Furthermore, ILS has very few control parameters and it does not require specific knowledge of the problem as in sophisticated heuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

ILS Heuristics for the Single-Machine Scheduling Problem with Sequence-Dependent Family Setup Times to Minimize Total Tardiness

Jacob

Arroyo

2016

Journal of Applied Mathematics

Self Cite

View full text Add to dashboard Cite

This paper addresses a single-machine scheduling problem with sequence-dependent family setup times. In this problem the jobs are classified into families according to their similarity characteristics. Setup times are required on each occasion when the machine switches from processing jobs in one family to jobs in another family. The performance measure to be minimized is the total tardiness with respect to the given due dates of the jobs. The problem is classified asNP-hard in the ordinary sense. Since the computational complexity associated with the mathematical formulation of the problem makes it difficult for optimization solvers to deal with large-sized instances in reasonable solution time, efficient heuristic algorithms are needed to obtain near-optimal solutions. In this work we propose three heuristics based on the Iterated Local Search (ILS) metaheuristic. The first heuristic is a basic ILS, the second uses a dynamic perturbation size, and the third uses a Path Relinking (PR) technique as an intensification strategy. We carry out comprehensive computational and statistical experiments in order to analyze the performance of the proposed heuristics. The computational experiments show that the ILS heuristics outperform a genetic algorithm proposed in the literature. The ILS heuristic with dynamic perturbation size and PR intensification has a superior performance compared to other heuristics.

show abstract