Solving the rotating seru production problem with dynamic multi-objective evolutionary algorithms

[1]  Ling Wang,et al.  A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Kathryn E. Stecke,et al.  An implementation framework for seru production , 2014, Int. Trans. Oper. Res..

[3]  Joshua D. Knowles,et al.  Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects , 2004 .

[4]  Lamjed Ben Said,et al.  A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy , 2015, Soft Computing.

[5]  Ikou Kaku,et al.  Comparison of two typical scheduling rules of line-seru conversion problem , 2015 .

[6]  Christopher S. Tang,et al.  Knowledge Creation and Dissemination in Operations and Supply Chain Management , 2016 .

[7]  Ikou Kaku,et al.  How to carry out assembly line–cell conversion? A discussion based on factor analysis of system performance improvements , 2012 .

[8]  Ikou Kaku,et al.  Modeling and numerical analysis of line-cell conversion problems , 2009 .

[9]  Ikou Kaku,et al.  Mathematical analysis and solutions for multi-objective line-cell conversion problem , 2014, Eur. J. Oper. Res..

[10]  Ikou Kaku,et al.  Management of overlapped cross-training: with or without a supervisor? , 2016 .

[11]  Yang Yu,et al.  Review of seru production , 2019, Frontiers of Engineering Management.

[12]  K. Stecke,et al.  The evolution of production systems from Industry 2.0 through Industry 4.0 , 2018, Int. J. Prod. Res..

[13]  Feng Liu,et al.  A multi-objective evolutionary algorithm guided by directed search for dynamic scheduling , 2017, Comput. Oper. Res..

[14]  Converting assembly lines to assembly cells at sheet metal products: insights on performance improvements , 2005 .

[15]  Qingfu Zhang,et al.  MOEA/D for flowshop scheduling problems , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[16]  Shengxiang Yang,et al.  A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization , 2017, IEEE Transactions on Evolutionary Computation.

[17]  Inyong Ham,et al.  A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem , 1983 .

[18]  Wallace J. Hopp,et al.  Benefits of Skill Chaining in Serial Production Lines with Cross-Trained Workers , 2004, Manag. Sci..

[19]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[20]  Jing Huang,et al.  Comparison of layered cellular manufacturing system design approaches , 2015, Comput. Ind. Eng..

[21]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[22]  Ikou Kaku,et al.  Lessons from seru production on manufacturing competitively in a high cost environment , 2017 .

[23]  Xin Yao,et al.  Dynamic Multiobjectives Optimization With a Changing Number of Objectives , 2016, IEEE Transactions on Evolutionary Computation.

[24]  Yue Zhao,et al.  A dynamic differential evolution algorithm for the dynamic single-machine scheduling problem with sequence-dependent setup times , 2020, J. Oper. Res. Soc..

[25]  Hisao Ishibuchi,et al.  Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling , 2003, IEEE Trans. Evol. Comput..

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

[27]  Steve Evans,et al.  Effects of key enabling technologies for seru production on sustainable performance , 2017 .

[28]  Yong Yin,et al.  The Evolution of Seru Production Systems Throughout Canon , 2008 .

[29]  Liang Gao,et al.  A multi-start variable neighbourhood descent algorithm for hybrid flowshop rescheduling , 2019, Swarm Evol. Comput..

[30]  Ikou Kaku,et al.  Seru: The Organizational Extension of JIT for a Super-Talent Factory , 2012, Int. J. Strateg. Decis. Sci..

[31]  Fernando A. Tohmé,et al.  A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem , 2010, Ann. Oper. Res..

[32]  Kai Wang,et al.  A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown , 2016, J. Oper. Res. Soc..

[33]  Kalyanmoy Deb,et al.  Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling , 2007, EMO.