Shop floor simulation optimization using machine learning to improve parallel metaheuristics

Abstract Simulation optimization is a tool commonly used as a decision-making support system on industrial problems in order to find the best resource allocation, which has a direct influence on costs and revenues. The present study proposed an open-source framework developed on Python, integrating different strategies for a novel optimization algorithm. The framework includes multicore parallelism (tested on two different types of computer sets), (two) population-based metaheuristics, and 33 machine learning methods. Moreover, the study tested the framework to optimize resource allocation on a theoretical shop floor case study, evaluating 12 optimization scenarios. The use of metaheuristic with parallelism reduced 88.3% the processing time compared with the serial metaheuristic, while the integration of metaheuristic with the selected machine learning generated an additional reduction of 59.0% on the necessary processing time. The combination of the optimization methods created a solution of 95.3% near the global optimum and time reduction of 95.2%.

[1]  Chi Wai Hui,et al.  Improved exact and meta-heuristic methods for minimizing makespan of large-size SMSP , 2017 .

[2]  Fiorenzo Franceschini,et al.  Scientific journal publishers and omitted citations in bibliometric databases: Any relationship? , 2014, J. Informetrics.

[3]  Stefano Lucidi,et al.  A Simulation-Based Multiobjective Optimization Approach for Health Care Service Management , 2016, IEEE Transactions on Automation Science and Engineering.

[4]  Andreas Krause,et al.  A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions , 2016, bioRxiv.

[5]  Gamal M. Nawara,et al.  Solving the Job-Shop Scheduling Problem by Arena Simulation Software , 2013 .

[6]  Graham Kendall,et al.  Choice function based hyper-heuristics for multi-objective optimization , 2015, Appl. Soft Comput..

[7]  Kenneth Sörensen,et al.  Metaheuristics - the metaphor exposed , 2015, Int. Trans. Oper. Res..

[8]  Shiqi Li,et al.  A discrete-event simulation approach with multiple-comparison procedure for stochastic resource-constrained project scheduling , 2012 .

[9]  Ashutosh Tiwari,et al.  State of the art in simulation-based optimisation for maintenance systems , 2015, Comput. Ind. Eng..

[10]  Patrick Charpentier,et al.  Generation of an adaptive simulation driven by product trajectories , 2012, J. Intell. Manuf..

[11]  Randall P. Sadowski,et al.  Simulation with Arena , 1998 .

[12]  Anna Syberfeldt,et al.  Integrating simulation-based optimization, lean, and the concepts of industry 4.0 , 2017, 2017 Winter Simulation Conference (WSC).

[13]  George-Christopher Vosniakos,et al.  Neural network simulation metamodels and genetic algorithms in analysis and design of manufacturing cells , 2006 .

[14]  Edson Emílio Scalabrin,et al.  Process mining techniques and applications - A systematic mapping study , 2019, Expert Syst. Appl..

[15]  Djamel Djenouri,et al.  How to exploit high performance computing in population-based metaheuristics for solving association rule mining problem , 2018, Distributed and Parallel Databases.

[16]  Pavel Raska,et al.  Comparison of Modified Downhill Simplex and Differential Evolution with other Selected Optimization Methods Used for Discrete Event Simulation Models , 2015 .

[17]  Parham Azimi,et al.  A new optimization via simulation approach for dynamic facility layout problem with budget constraints , 2012 .

[18]  Oliver Avalos-Rosales,et al.  Efficient metaheuristic algorithm and re-formulations for the unrelated parallel machine scheduling problem with sequence and machine-dependent setup times , 2015 .

[19]  Manuel Chica,et al.  Why Simheuristics? Benefits, Limitations, and Best Practices When Combining Metaheuristics with Simulation , 2017, SSRN Electronic Journal.

[20]  Branislav Dragović,et al.  Simulation modelling in ports and container terminals: literature overview and analysis by research field, application area and tool , 2017 .

[21]  Michele Sonnessa,et al.  Multiobjective bed management considering emergency and elective patient flows , 2018, Int. Trans. Oper. Res..

[22]  Taho Yang,et al.  A genetic algorithms simulation approach for the multi-attribute combinatorial dispatching decision problem , 2007, Eur. J. Oper. Res..

[23]  Moacir Godinho Filho,et al.  Using Genetic Algorithms to solve scheduling problems on flexible manufacturing systems (FMS): a literature survey, classification and analysis , 2014 .

[24]  José Arnaldo Barra Montevechi,et al.  Integrating soft systems methodology to aid simulation conceptual modeling , 2015, Int. Trans. Oper. Res..

[25]  Gilles Louppe,et al.  Independent consultant , 2013 .

[26]  Lars Mönch,et al.  Simulation-based performance assessment of master planning approaches in semiconductor manufacturing , 2014 .

[27]  José Arnaldo Barra Montevechi,et al.  Discrete simulation-based optimization methods for industrial engineering problems: A systematic literature review , 2019, Comput. Ind. Eng..

[28]  Klaus-Dieter Thoben,et al.  Machine learning in manufacturing: advantages, challenges, and applications , 2016 .

[29]  Tillal Eldabi,et al.  Simulation in manufacturing and business: A review , 2010, Eur. J. Oper. Res..

[30]  Angel A. Juan,et al.  A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands , 2020, Int. Trans. Oper. Res..

[31]  Cathal Heavey,et al.  A review of open source discrete event simulation software for operations research , 2016, J. Simulation.

[32]  Enrique Alba,et al.  Parallel metaheuristics: recent advances and new trends , 2012, Int. Trans. Oper. Res..

[33]  Richard M. Fujimoto,et al.  Research Challenges in Parallel and Distributed Simulation , 2016, ACM Trans. Model. Comput. Simul..

[34]  Ali Azadeh,et al.  Optimization of operator allocation in a large multi product assembly shop through unique integration of simulation and genetic algorithm , 2015 .

[35]  Ayse Tugba Dosdogru,et al.  Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach , 2012, Neural Computing and Applications.

[36]  Alexandre Tadeu Simon,et al.  Integrating value stream mapping and discrete events simulation as decision making tools in operation management , 2015 .

[37]  Michael C. Fu,et al.  Feature Article: Optimization for simulation: Theory vs. Practice , 2002, INFORMS J. Comput..

[38]  Lorenzo Tiacci,et al.  Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times , 2015 .

[39]  J. Bertrand,et al.  Operations management research methodologies using quantitative modeling , 2002 .

[40]  Hyunbo Cho,et al.  Hybrid algorithm for discrete event simulation based supply chain optimization , 2010, Expert Syst. Appl..

[41]  Cathal Heavey,et al.  A demonstration of machine learning for explicit functions for cycle time prediction using MES data , 2016, 2016 Winter Simulation Conference (WSC).

[42]  Angel A. Juan,et al.  A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems , 2015 .

[43]  Celso C. Ribeiro,et al.  Optimization by GRASP , 2016 .

[44]  Amr Arisha,et al.  An analytical representation of flexible resource allocation in hospitals , 2016 .

[45]  Miroslav Kubat,et al.  An Introduction to Machine Learning , 2015, Springer International Publishing.

[46]  Enrique del Castillo,et al.  Statitical Testing of Optimality Conditions in Multiresponse Simulation-Based Optimization , 2005, Eur. J. Oper. Res..

[47]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[48]  Berna Dengiz,et al.  A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry , 2016, Math. Comput. Simul..

[49]  Loo Hay Lee,et al.  Simulation optimization in the era of Industrial 4.0 and the Industrial Internet , 2016, J. Simulation.

[50]  Alexandre Plastino,et al.  Hybridization of GRASP Metaheuristic with Data Mining Techniques , 2006, J. Math. Model. Algorithms.

[51]  Lazhar Homri,et al.  Review of data mining applications for quality assessment in manufacturing industry: support vector machines , 2015 .

[52]  Mohammad Asif Salam,et al.  Simulation based decision support system for optimization: A case of Thai logistics service provider , 2016, Ind. Manag. Data Syst..

[53]  Long-Fei Wang,et al.  Simulation Optimization: A Review on Theory and Applications , 2013 .

[54]  Michel Bierlaire,et al.  Simulation and optimization: a short review , 2015 .

[55]  David M. Nicol,et al.  Parallel discrete event simulation: The making of a field , 2017, 2017 Winter Simulation Conference (WSC).

[56]  James T. Lin,et al.  Simulation-based optimization approach for simultaneous scheduling of vehicles and machines with processing time uncertainty in FMS , 2017, Flexible Services and Manufacturing Journal.

[57]  R. Caire,et al.  Modeling cyber and physical interdependencies - Application in ICT and power grids , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[58]  Jack P. C. Kleijnen,et al.  Regression and Kriging metamodels with their experimental designs in simulation: A review , 2017, Eur. J. Oper. Res..

[59]  Jonathan F. Bard,et al.  Improving performance of dispatch rules for daily scheduling of assembly and test operations , 2015, Comput. Ind. Eng..

[60]  Landir Saviniec,et al.  Parallel local search algorithms for high school timetabling problems , 2018, Eur. J. Oper. Res..

[61]  Tobias Reggelin,et al.  The Combination of Discrete-Event Simulation and Genetic Algorithm for Solving the Stochastic Multi-Product Inventory Optimization Problem , 2018, Transport and Telecommunication Journal.

[62]  Jeffrey S. Smith,et al.  Simulation for manufacturing system design and operation: Literature review and analysis , 2014 .

[63]  Alan Bryman,et al.  Research Methods and Organization Studies , 1989 .

[64]  Loo Hay Lee,et al.  Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data , 2015, Asia Pac. J. Oper. Res..

[65]  Thyago P. Carvalho,et al.  A systematic literature review of machine learning methods applied to predictive maintenance , 2019, Comput. Ind. Eng..

[66]  Linda Ann Riley.  Discrete-event simulation optimization: a review of past approaches and propositions for future direction , 2013, SummerSim.

[67]  Michael J. North,et al.  Parallel agent-based simulation with Repast for High Performance Computing , 2013, Simul..

[68]  Ender Özcan,et al.  Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation , 2017 .

[69]  Daqiang Zhang,et al.  Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination , 2016, Comput. Networks.

[70]  Jie Song,et al.  Integrating Optimal Simulation Budget Allocation and Genetic Algorithm to Find the Approximate Pareto Patient Flow Distribution , 2016, IEEE Transactions on Automation Science and Engineering.

[71]  Michael C. Fu,et al.  A tutorial review of techniques for simulation optimization , 1994, Proceedings of Winter Simulation Conference.

[72]  Tag Gon Kim,et al.  DEXSim: an experimental environment for distributed execution of replicated simulators using a concept of single simulation multiple scenarios , 2014, Simul..

[73]  Michael C. Fu,et al.  Optimization for Simulation: Theory vs. Practice , 2002 .

[74]  Frank Herrmann,et al.  Simulation Based Priority Rules For Scheduling Of A Flow Shop With Simultaneously Loaded Stations , 2013, ECMS.

[75]  Okba Taouali,et al.  Online fault detection and isolation of an AIR quality monitoring network based on machine learning and metaheuristic methods , 2018, The International Journal of Advanced Manufacturing Technology.

[76]  Tobias Viere,et al.  Resource Efficiency-oriented Optimization of Material Flow Networks in Chemical Process Engineering , 2014 .

[77]  Michel Gendreau,et al.  Hyper-heuristics: a survey of the state of the art , 2013, J. Oper. Res. Soc..

[78]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[79]  Alberto Regattieri,et al.  On the use of machine learning methods to predict component reliability from data-driven industrial case studies , 2017, The International Journal of Advanced Manufacturing Technology.

[80]  Cathal Heavey,et al.  A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models , 2012, Comput. Oper. Res..

[81]  Anna Syberfeldt,et al.  The Internet of Things, Factory of Things and Industry 4.0 in Manufacturing : Current and Future Implementations , 2017 .

[82]  Andreas Muller.  Introduction to Machine Learning with Python , 2016 .

[83]  Ali Azadeh,et al.  A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times , 2014 .

[84]  Beata Czarnacka-Chrobot,et al.  An effective approach for software project effort and duration estimation with machine learning algorithms , 2018, J. Syst. Softw..

[85]  Laura Calvet,et al.  Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs , 2017 .