Evolving machine-specific dispatching rules for a two-machine job shop using genetic programming

Job Shop Scheduling (JSS) involves determining a schedule for processing jobs on machines to optimise some measure of delivery speed or customer satisfaction. We investigate a genetic programming based hyper-heuristic (GPHH) approach to evolving dispatching rules for a two-machine job shop in both static and dynamic environments. In the static case the proposed GPHH method can represent and discover optimal dispatching rules. In the dynamic case we investigate two representations (using a single rule at both machines and evolving a specialised rule for each machine) and the effect of changing the training problem instances throughout evolution. Results show that relative performance of these methods is dependent on the testing instances.

[1]  Chris N. Potts,et al.  Fifty years of scheduling: a survey of milestones , 2009, J. Oper. Res. Soc..

[2]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[3]  John R. Woodward,et al.  Hyper-Heuristics , 2015, GECCO.

[4]  Jacek Blazewicz,et al.  The job shop scheduling problem: Conventional and new solution techniques , 1996 .

[5]  Mark Johnston,et al.  A coevolution genetic programming method to evolve scheduling policies for dynamic multi-objective job shop scheduling problems , 2012, 2012 IEEE Congress on Evolutionary Computation.

[6]  Nhu Binh Ho,et al.  Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems , 2008, Comput. Ind. Eng..

[7]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[8]  Graham Kendall,et al.  A Classification of Hyper-heuristic Approaches , 2010 .

[9]  Peter Ross,et al.  Evolutionary Scheduling: A Review , 2005, Genetic Programming and Evolvable Machines.

[10]  Kazuo Miyashita,et al.  Job-shop scheduling with genetic programming , 2000 .

[11]  S. M. Johnson,et al.  Optimal two- and three-stage production schedules with setup times included , 1954 .

[12]  Domagoj Jakobovic,et al.  Dynamic Scheduling with Genetic Programming , 2006, EuroGP.

[13]  Reha Uzsoy,et al.  Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach , 2006, J. Sched..

[14]  Bernd Scholz-Reiter,et al.  Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems , 2013 .

[15]  Albert Jones,et al.  Survey of Job Shop Scheduling Techniques , 1999 .

[16]  James R. Jackson,et al.  An extension of Johnson's results on job IDT scheduling , 1956 .

[17]  Domagoj Jakobovic,et al.  Evolving priority scheduling heuristics with genetic programming , 2012, Appl. Soft Comput..

[18]  Ari P. J. Vepsalainen.  Priority rules for job shops with weighted tardiness costs , 1987 .

[19]  Bernd Scholz-Reiter,et al.  Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach , 2010, GECCO '10.

[20]  Graham Kendall,et al.  Exploring Hyper-heuristic Methodologies with Genetic Programming , 2009 .