This paper presents a local search, based on a new neighborhood for the job-shop scheduling problem, and its application within a biased random-key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based on an extension of the graphical method of Akers (1956), is applied to improve the solution. The new heuristic is tested on a set of 165 standard instances taken from the job-shop scheduling literature and compared with results obtained by other approaches. The new algorithm improved the best known solution values for 57 instances.
AT&T Labs Research Technical Report, Florham Park, NJ 07932, April 2011.
View A biased random-key genetic algorithm for job-shop scheduling