An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling

J.F. Gonçalves and M.G.C. Resende

International Transactions in Operational Research,  vol. 21, pp. 215-246, 2014


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 intital 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.

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