A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times

• Published in: European journal of operational research Vol. 211; no. 3; pp. 612 - 622
• Main Authors: Vallada, Eva, Ruiz, Rubén
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.06.2011; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Applied sciences; Benchmarks; Comparative analysis; Design of experiments; Exact sciences and technology; Experimental design; Genetic algorithms; Inventory control, production control. Distribution; Job shops; Makespan; Mathematics; Operational research and scientific management; Operational research. Management science; Operations research; Parallel machine; Parallel machine Scheduling Makespan Setup times; Probability and statistics; Production scheduling; Scheduling; Scheduling, sequencing; Sciences and techniques of general use; Setup times; Statistics; Studies; Parallel machine; Scheduling; Makespan; Setup times; Local search; Statistical analysis; Parallel machines; Calibration; Genetic algorithm; Experimental design; Disjunctive programming; Setup time; Fast algorithm
• ISSN: 0377-2217; 1872-6860; 1872-6860
• DOI: 10.1016/j.ejor.2011.01.011

In this work a genetic algorithm is presented for the unrelated parallel machine scheduling problem in which machine and job sequence dependent setup times are considered. The proposed genetic algorithm includes a fast local search and a local search enhanced crossover operator. Two versions of the algorithm are obtained after extensive calibrations using the Design of Experiments (DOE) approach. We review, evaluate and compare the proposed algorithm against the best methods known from the literature. We also develop a benchmark of small and large instances to carry out the computational experiments. After an exhaustive computational and statistical analysis we can conclude that the proposed method shows an excellent performance overcoming the rest of the evaluated methods in a comprehensive benchmark set of instances.