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A biased random-key genetic algorithm for job-shop scheduling

José F. Gonçalves (jfgoncal***at***fep.up.pt)
Mauricio G.C. Resende (mgcr***at***research.att.com)

Abstract: 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.

Keywords: Job-shop; Scheduling; Genetic algorithm; Biased random-key genetic algorithm; Heuristics; Random keys, Graphical method.

Category 1: Applications -- OR and Management Sciences (Scheduling )

Category 2: Combinatorial Optimization

Category 3: Combinatorial Optimization (Meta Heuristics )

Citation: AT&T Labs Research Technical Report, Florham Park, NJ 07932, April 2011.

Download: [PDF]

Entry Submitted: 04/18/2011
Entry Accepted: 04/19/2011
Entry Last Modified: 02/25/2013

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