Intelligent controllers for bi-objective dynamic scheduling on a single machine with sequence-dependent setups

This article addresses the problem of dynamic job scheduling on a single machine with Poisson arrivals, stochastic processing times and due dates, in the presence of sequence-dependent setups. The objectives of minimizing mean earliness and mean tardiness are considered. Two approaches for dynamic scheduling are proposed, a Reinforcement Learning-based and one based on Fuzzy Logic and multi-objective evolutionary optimization. The performance of the two scheduling approaches is tested against the performance of 15 dispatching rules in four simulation scenarios with different workload and due date pressure conditions. The scheduling methods are compared in terms of Pareto optimal-oriented metrics, as well as in terms of minimizing mean earliness and mean tardiness independently. The experimental results demonstrate the merits of the proposed methods.

[1]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[2]  Emin Gundogar,et al.  Fuzzy priority rule for job shop scheduling , 2004, J. Intell. Manuf..

[3]  Napsiah Ismail,et al.  Development of genetic fuzzy logic controllers for complex production systems , 2009, Comput. Ind. Eng..

[4]  Yu-Wang Chen,et al.  Development of hybrid evolutionary algorithms for production scheduling of hot strip mill , 2012, Comput. Oper. Res..

[5]  Reza Tavakkoli-Moghaddam,et al.  The use of a fuzzy multi-objective linear programming for solving a multi-objective single-machine scheduling problem , 2010, Appl. Soft Comput..

[6]  Tong Heng Lee,et al.  Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing) , 2005 .

[7]  Yi-Chi Wang,et al.  Application of reinforcement learning for agent-based production scheduling , 2005, Eng. Appl. Artif. Intell..

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

[9]  Anton Schwartz,et al.  A Reinforcement Learning Method for Maximizing Undiscounted Rewards , 1993, ICML.

[10]  Savas Balin,et al.  Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation , 2011, Information Sciences.

[11]  V. Vinod,et al.  Scheduling a dynamic job shop production system with sequence-dependent setups: An experimental study , 2008 .

[12]  V. Vinod,et al.  Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system , 2011 .

[13]  Yeong-Dae Kim,et al.  Adaptive inventory control models for supply chain management , 2005 .

[14]  Yi-Chi Wang,et al.  Learning policies for single machine job dispatching , 2004 .

[15]  Deming Lei,et al.  Multi-objective production scheduling: a survey , 2009 .

[16]  Manuel Mucientes,et al.  Machine scheduling in custom furniture industry through neuro-evolutionary hybridization , 2011, Appl. Soft Comput..

[17]  Georges Habchi,et al.  Application of a continuous supervisory fuzzy control on a discrete scheduling of manufacturing systems , 2011, Eng. Appl. Artif. Intell..

[18]  Jürgen Branke,et al.  Evolutionary search for difficult problem instances to support the design of job shop dispatching rules , 2011, Eur. J. Oper. Res..

[19]  Carsten Franke,et al.  Development of scheduling strategies with Genetic Fuzzy systems , 2008, Appl. Soft Comput..

[20]  Pierre Borne,et al.  Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic , 2002, Math. Comput. Simul..

[21]  Reza Tavakkoli-Moghaddam,et al.  Solving a multi-objective multi-skilled manpower scheduling model by a fuzzy goal programming approach , 2013 .

[22]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[23]  Appa Iyer Sivakumar,et al.  Criteria selection and analysis for single machine dynamic on-line scheduling with multiple objectives and sequence-dependent setups , 2009, Comput. Ind. Eng..

[24]  A. S. Xanthopoulos,et al.  Reinforcement learning and evolutionary algorithms for non-stationary multi-armed bandit problems , 2008, Appl. Math. Comput..

[25]  Kimon P. Valavanis,et al.  Fuzzy supervisory control of manufacturing systems , 2004, IEEE Transactions on Robotics and Automation.

[26]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[27]  Hsin-Yi Lin,et al.  Self-organizing state aggregation for architecture design of Q-learning , 2011, Inf. Sci..

[28]  Li Zheng,et al.  Dynamic parallel machine scheduling with mean weighted tardiness objective by Q-Learning , 2007 .

[29]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .

[30]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[31]  Chandrasekharan Rajendran,et al.  Scheduling rules for dynamic shops that manufacture multi-level jobs , 2003 .

[32]  A. I. Sivakumar,et al.  Multiobjective dynamic scheduling using discrete event simulation , 2001, Int. J. Comput. Integr. Manuf..

[33]  Satinder P. Singh,et al.  Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes , 1994, AAAI.

[34]  Damien Trentesaux,et al.  Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach , 2009, Eng. Appl. Artif. Intell..

[35]  Kazem Abhary,et al.  Efficient Scheduling Rule for Robotic Flexible Assembly Cells Based on Fuzzy Approach , 2012 .

[36]  Sidney Addelman,et al.  trans-Dimethanolbis(1,1,1-trifluoro-5,5-dimethylhexane-2,4-dionato)zinc(II) , 2008, Acta crystallographica. Section E, Structure reports online.

[37]  Frank Klawonn,et al.  Foundations of fuzzy systems , 1994 .

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

[39]  Tapas K. Das,et al.  Intelligent dynamic control policies for serial production lines , 2001 .

[40]  Sanja Petrovic,et al.  SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS , 2006 .

[41]  Ernesto Martinez,et al.  Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing , 2010, Comput. Ind. Eng..

[42]  Tapas K. Das,et al.  A multi-agent reinforcement learning approach to obtaining dynamic control policies for stochastic lot scheduling problem , 2005, Simul. Model. Pract. Theory.

[43]  Pandian Vasant,et al.  Hybrid pattern search and simulated annealing for fuzzy production planning problems , 2010, Comput. Math. Appl..

[44]  Vittaldas V. Prabhu,et al.  Distributed Reinforcement Learning Control for Batch Sequencing and Sizing in Just-In-Time Manufacturing Systems , 2004, Applied Intelligence.

[45]  Pandian Vasant,et al.  Application of a Fuzzy Programming Technique to Production Planning in the Textile Industry , 2009, ArXiv.

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

[47]  Yi-Chi Wang,et al.  A reinforcement learning approach for developing routing policies in multi-agent production scheduling , 2007 .

[48]  Chandrasekharan Rajendran,et al.  Scheduling in dynamic assembly job-shops with jobs having different holding and tardiness costs , 2003 .

[49]  Key K. Lee,et al.  Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment , 2008, Appl. Soft Comput..

[50]  Dimitrios E. Koulouriotis,et al.  Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms , 2010 .

[51]  Nikos Tsourveloudis.  On the evolutionary-fuzzy control of WIP in manufacturing systems , 2010, Neurocomputing.