A comprehensive analysis on reusability of GP-evolved job shop dispatching rules

Genetic Programming (GP) has been extensively used to automatically design dispatching rules for job shop scheduling problems. However, the previous studies only focus on the performance on the training instances. So far, there is no systematic investigation of the reusability of the GP-evolved rules on unseen instances. In practice, it is desirable to train the rules on smaller job shop instances, and apply them to larger instances with more jobs and machines to save training time. In this case, the reusability of the GP-evolved rules under different numbers of jobs and machines is an important issue. In this paper, a comprehensive investigation is conducted to analyse how the variation in the numbers of jobs and machines from the training set to the test set affects the reusability of the GP-evolved rules. It is found that in terms of minimizing makespan, the reusability of the GP-evolved rules highly depends on variation in the numbers of jobs and machines. A better reusability can be achieved by choosing training instances whose numbers of jobs and machines (or at least the ratio between the numbers of jobs and machines) are closer to that of the test instances. Furthermore, the ratio between the numbers of jobs and machines is demonstrated to be an important factor to reflect the complexity of an instance for dispatching rules. This study is the first systematic investigation on the reusability of GP-evolved dispatching rules.

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

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

[3]  Mark Johnston,et al.  Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming , 2014, IEEE Transactions on Evolutionary Computation.

[4]  Chandrasekharan Rajendran,et al.  New dispatching rules for shop scheduling: A step forward , 2000 .

[5]  T. C. Edwin Cheng,et al.  A tabu search/path relinking algorithm to solve the job shop scheduling problem , 2014, Comput. Oper. Res..

[6]  Mehmet Emin Aydin,et al.  Dynamic job-shop scheduling using reinforcement learning agents , 2000, Robotics Auton. Syst..

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

[8]  Douglas A. Elvers,et al.  The Sensitivity of the Relative Effectiveness of Job Shop Dispatching Rules with Respect to Various Arrival Distributions , 1974 .

[9]  Chandrasekharan Rajendran,et al.  Efficient dispatching rules for scheduling in a job shop , 1997 .

[10]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[11]  William J. Cook,et al.  A Computational Study of the Job-Shop Scheduling Problem , 1991, INFORMS Journal on Computing.

[12]  Ali S. Kiran,et al.  Simulation studies in job shop sheduling—I a survey , 1984 .

[13]  Larry R. Taube,et al.  Time completion for various dispatching rules in job shops , 1983 .

[14]  Milton L. Smith,et al.  Simulation studies in job shop scheduling—II: Performance of priority rules , 1984 .

[15]  C. T. Baker,et al.  Simulation of a Simplified Job Shop , 1960 .

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

[17]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

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

[19]  Ranga V. Ramasesh.  Dynamic job shop scheduling: A survey of simulation research , 1990 .

[20]  Yoke San Wong,et al.  Dynamic selection of dispatching rules for job shop scheduling , 2000 .

[21]  Mark Johnston,et al.  A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem , 2013, IEEE Transactions on Evolutionary Computation.

[22]  Ali M. S. Zalzala,et al.  Investigating the use of genetic programming for a classic one-machine scheduling problem , 2001 .

[23]  Mengjie Zhang,et al.  Automated Design of Production Scheduling Heuristics: A Review , 2016, IEEE Transactions on Evolutionary Computation.

[24]  Mark Johnston,et al.  Automatic Programming via Iterated Local Search for Dynamic Job Shop Scheduling , 2015, IEEE Transactions on Cybernetics.

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

[26]  Christopher D. Geiger,et al.  Learning effective dispatching rules for batch processor scheduling , 2008 .

[27]  Chandrasekharan Rajendran,et al.  Efficient jobshop dispatching rules: Further developments , 2000 .