Extracting priority rules for dynamic multi-objective flexible job shop scheduling problems using gene expression programming

In this paper, two new approaches are proposed for extracting composite priority rules for scheduling problems. The suggested approaches use simulation and gene expression programming and are able to evolve specific priority rules for all dynamic scheduling problems in accordance with their features. The methods are based on the idea that both the proper design of the function and terminal sets and the structure of the gene expression programming approach significantly affect the results. In the first proposed approach, modified and operational features of the scheduling environment are added to the terminal set, and a multigenic system is used, whereas in the second approach, priority rules are used as automatically defined functions, which are combined with the cellular system for gene expression programming. A comparison shows that the second approach generates better results than the first; however, all of the extracted rules yield better results than the rules from the literature, especially for the defined multi-objective function consisting of makespan, mean lateness and mean flow time. The presented methods and the generated priority rules are robust and can be applied to all real and large-scale dynamic scheduling problems.

[1]  Gongfa Li,et al.  A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints , 2017, Eur. J. Oper. Res..

[2]  Fevzi Onen,et al.  Development of predictive model for flood routing using genetic expression programming , 2018 .

[3]  Thomas Philip Runarsson,et al.  Evolutionary Learning of Linear Composite Dispatching Rules for Scheduling , 2014, IJCCI.

[4]  Kenneth R. Baker,et al.  Sequencing Rules and Due-Date Assignments in a Job Shop , 1984 .

[5]  Domagoj Jakobovic,et al.  Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environment , 2018, Genetic Programming and Evolvable Machines.

[6]  Xiaoming Li,et al.  A Weighted Modified Due Date Rule for Sequencing to Minimize Weighted Tardiness , 2004, J. Sched..

[7]  Li-Chen Fu,et al.  Using dispatching rules for job shop scheduling with due date-based objectives , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[8]  Domagoj Jakobovic,et al.  Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment , 2018, Genetic Programming and Evolvable Machines.

[9]  Sanja Petrovic,et al.  Dispatching rules for production scheduling: A hyper-heuristic landscape analysis , 2009, 2009 IEEE Congress on Evolutionary Computation.

[10]  Muzaffer Kapanoglu,et al.  Learning IF-THEN priority rules for dynamic job shops using genetic algorithms , 2011 .

[11]  Nasser Salmasi,et al.  Total completion time minimisation of no-wait flowshop group scheduling problem with sequence dependent setup times , 2017 .

[12]  Mario Vanhoucke,et al.  A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions , 2012 .

[13]  Hyoung-Ho Doh,et al.  A priority scheduling approach for flexible job shops with multiple process plans , 2013 .

[14]  T.C.E. Cheng,et al.  Single-machine slack due-window assignment and scheduling with past-sequence-dependent delivery times and controllable job processing times , 2016 .

[15]  Erhan Kozan,et al.  Parallel-identical-machine job-shop scheduling with different stage-dependent buffering requirements , 2016, Comput. Oper. Res..

[16]  Yi Mei,et al.  A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules , 2019, Evolutionary Computation.

[17]  Chandrasekharan Rajendran,et al.  A comparative study of dispatching rules in dynamic flowshops and jobshops , 1999, Eur. J. Oper. Res..

[18]  Taehoon Hong,et al.  An optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms , 2018, Applied Energy.

[19]  Gang Chen,et al.  An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling , 2018, Appl. Soft Comput..

[20]  S. S. Panwalkar,et al.  A Survey of Scheduling Rules , 1977, Oper. Res..

[21]  Abdelghani Bekrar,et al.  Multiple priority dispatching rules for the job shop scheduling problem , 2015, 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT).

[22]  Feng-Chang R. Chang.  A study of factors affecting due-date predictability in a simulated dynamic job shop , 1994 .

[23]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.

[24]  Randall P. Sadowski,et al.  A statistical comparison of the performance of simple dispatching rules for a particular set of job shops , 1976 .

[25]  Mark Johnston,et al.  Genetic programming for job shop scheduling , 2018, Studies in Computational Intelligence.

[26]  Fang Wang,et al.  Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops , 2017 .

[27]  Liang Gao,et al.  Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems , 2010 .

[28]  Ali Allahverdi,et al.  Heuristics for the two-machine flowshop scheduling problem to minimise makespan with bounded processing times , 2010 .

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

[30]  Yi Mei,et al.  Genetic programming for production scheduling: a survey with a unified framework , 2017, Complex & Intelligent Systems.

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

[32]  A. J. Clewett,et al.  Introduction to sequencing and scheduling , 1974 .

[33]  V. K. Manupati,et al.  Real-Time Rule-Based Scheduling System for Integrated Delivery in a Semiconductor Manufacturing Using Evolutionary Algorithm-Based Simulation Approach , 2016 .

[34]  Liang Gao,et al.  A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates , 2013, J. Intell. Manuf..

[35]  Cândida Ferreira,et al.  Automatically Defined Functions in Gene Expression Programming , 2006, Genetic Systems Programming.

[36]  Jie Wang,et al.  Learning adaptive dispatching rules for a manufacturing process system by using reinforcement learning approach , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[37]  Jürgen Branke,et al.  Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times , 2016 .

[38]  Chin-Chia Wu,et al.  Single-machine group scheduling with a general learning effect , 2013 .

[39]  Bernard Grabot,et al.  Dispatching rules in scheduling Dispatching rules in scheduling: a fuzzy approach , 1994 .

[40]  Ozgur Kisi,et al.  Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths , 2018 .

[41]  Yeong-Dae Kim,et al.  A real-time scheduling mechanism for a flexible manufacturing system: Using simulation and dispatching rules , 1998 .

[42]  H. Pierreval,et al.  Dynamic scheduling selection of dispatching rules for manufacturing system , 1997 .

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

[44]  Una-May O'Reilly,et al.  Genetic Programming II: Automatic Discovery of Reusable Programs. , 1994, Artificial Life.

[45]  Cândida Ferreira,et al.  The Entities of Gene Expression Programming , 2006 .

[46]  Ali Allahverdi,et al.  Simulation of different rules in stochastic flowshops , 1996 .

[47]  Gang Chen,et al.  Niching Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling: An Investigation into Distance Metrics , 2016, GECCO.

[48]  George Q. Huang,et al.  Integrating order review/release and dispatching rules for assembly job shop scheduling using a simulation approach , 2011 .

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

[50]  T.C.E. Cheng,et al.  Job shop scheduling for missed due-date performance , 1998 .

[51]  Edward Anderson,et al.  Two new rules to minimize tardiness in a job shop , 1990 .

[52]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.

[53]  Yoke San Wong,et al.  Job Shop Scheduling with Dynamic Fuzzy Selection of Dispatching Rules , 2000 .

[54]  Erik D. Goodman,et al.  A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problem , 1997, ICGA.

[55]  Don T. Phillips,et al.  A state-of-the-art survey of dispatching rules for manufacturing job shop operations , 1982 .

[56]  Weimin Xiao,et al.  Evolving accurate and compact classification rules with gene expression programming , 2003, IEEE Trans. Evol. Comput..

[57]  Bernd Scholz-Reiter,et al.  Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations , 2015, Evolutionary Computation.

[58]  H. M. Soroush,et al.  Single-machine scheduling with inserted idle time to minimise a weighted quadratic function of job lateness , 2010 .

[59]  Mark Johnston,et al.  Evolving Ensembles of Dispatching Rules Using Genetic Programming for Job Shop Scheduling , 2015, EuroGP.

[60]  Bernardo Almada-Lobo,et al.  Designing new heuristics for the capacitated lot sizing problem by genetic programming , 2018, Comput. Oper. Res..

[61]  Gang Chen,et al.  Proceedings in Adaptation, Learning and Optimization , 2016, IES.

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

[63]  Ajai Jain,et al.  Performance analysis of dispatching rules in a stochastic dynamic job shop manufacturing system with sequence-dependent setup times: Simulation approach , 2015 .

[64]  Ali Allahverdi,et al.  Scheduling on M parallel machines subject to random breakdowns to minimize expected mean flow time , 1994 .

[65]  Mark Johnston,et al.  Evolving machine-specific dispatching rules for a two-machine job shop using genetic programming , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[66]  Mostafa Zandieh,et al.  Multi-objective scheduling of dynamic job shop using variable neighborhood search , 2010, Expert Syst. Appl..

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

[68]  T.C.E. Cheng.  Analysis of Job Flow-Time in a Job Shop , 1985 .

[69]  Matthias Thürer,et al.  Integrating load-based order release and priority dispatching , 2014 .

[70]  Nasser Salmasi,et al.  Efficient upper and lower bounding methods for flowshop sequence-dependent group scheduling problems , 2014 .

[71]  RadosÅ‚aw Rudek.  Minimising maximum lateness in a single machine scheduling problem with processing time-based aging effects , 2013 .

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

[73]  Liang Gao,et al.  Reactive scheduling in a job shop where jobs arrive over time , 2013, Comput. Ind. Eng..

[74]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[75]  Mostafa Zandieh,et al.  Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop , 2009, J. Intell. Manuf..

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

[77]  Chandrasekharan Rajendran,et al.  Development and analysis of cost-based dispatching rules for job shop scheduling , 2004, Eur. J. Oper. Res..

[78]  Aydin Teymourifar,et al.  Extracting New Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling with Limited Buffer Spaces , 2018, Cognitive Computation.