Minimizing makespan of a production batch within concurrent systems: Seru production perspective

Abstract This paper discusses the makespan minimization of a production batch within a specific concurrent system, seru production system. A seru production system consists of multiple independent serus. A seru is a compact assembly origination in which products are assembled from-the-beginning-to-the-end without disruptions. One capability of a seru production system is its responsiveness. A performance measure used to evaluate a seru system’s responsiveness is the makespan of production batches assembled within the seru system. This study addresses the makespan minimization problem through an optimal seru loading policy. The problem is formulated as a min-max integer optimization model. An exact dimension-reduction Algorithm is developed to obtain the optimal allocation that minimizes the makespan. We show that the solution space increases very quickly. In contrast, our algorithm is efficient with a polynomial computational complexity of O ( n 2 ) , where n is the total number of serus in a seru system. To verify the usefulness of the developed exact dimension-reduction algorithm, we compare it with a widely practiced greedy algorithm through experiments. We find that our optimal algorithm is robust in most cases and the greedy algorithm is efficient when variability in production efficiencies is high. This result can guide us to adopt different algorithms under different business environments. If the variability in production efficiencies is high, e.g., new employees and/or new products assembly, the greedy algorithm is efficient. For other cases, our optimal algorithm should be adopted to obtain the minimum makespan. We also extend the method to the application of a rotating seru.

[1]  Yue Wu,et al.  Cellular bucket brigades on U-lines with discrete work stations , 2014 .

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

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

[4]  Hui Ren,et al.  Analysis of the Effect of the Line-Seru Conversion on the Waiting Time with Batch Arrival , 2019, Mathematical Problems in Engineering.

[5]  Ilker Baybars,et al.  A survey of exact algorithms for the simple assembly line balancing , 1986 .

[6]  Yong Yin,et al.  Reconfiguration of assembly systems: From conveyor assembly line to serus , 2012 .

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

[8]  Zhe Zhang,et al.  Modelling and numerical analysis of seru loading problem under uncertainty , 2017 .

[9]  Éva Tardos,et al.  Algorithm design , 2005 .

[10]  Ye Wang,et al.  Cost and Service-Level-Based Model for a Seru Production System Formation Problem with Uncertain Demand , 2018, Journal of Systems Science and Systems Engineering.

[11]  Ikou Kaku,et al.  Complexity of line-seru conversion for different scheduling rules and two improved exact algorithms for the multi-objective optimization , 2016, SpringerPlus.

[12]  Li Li,et al.  Cross-trained worker assignment and comparative analysis on throughput of divisional and rotating seru , 2018, Ind. Manag. Data Syst..

[13]  Wei Sun,et al.  Line-hybrid seru system conversion: Models, complexities, properties, solutions and insights , 2017, Comput. Ind. Eng..

[14]  Richard J. Schonberger,et al.  Missing link in competitive manufacturing research and practice: Customer-responsive concurrent production , 2017 .

[15]  Wei Sun,et al.  Seru system balancing: Definition, formulation, and exact solution , 2018, Comput. Ind. Eng..

[16]  Wei Sun,et al.  Combining local search into non-dominated sorting for multi-objective line-cell conversion problem , 2013, Int. J. Comput. Integr. Manuf..

[17]  Aaya Aboelfotoh,et al.  Selection of Assembly Systems; Assembly Lines vs. Seru Systems , 2018 .

[18]  Hesham S. Ahmad,et al.  Seru production as an alternative to a traditional assembly line , 2018, Engineering Management in Production and Services.

[19]  Joaquín Bautista,et al.  A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand , 2016 .

[20]  Nils Boysen,et al.  A classification of assembly line balancing problems , 2007, Eur. J. Oper. Res..

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

[22]  Yang Yu,et al.  Reducing the total tardiness by Seru production: model, exact and cooperative coevolution solutions , 2020, Int. J. Prod. Res..

[23]  Jeffrey D. Tew,et al.  Effect of cognitive automation in a material handling system on manufacturing flexibility , 2015 .

[24]  Yong Yin,et al.  A bi-objective combination optimisation model for line-seru conversion based on queuing theory , 2016, Int. J. Manuf. Res..

[25]  Yun Fong Lim Cellular Bucket Brigades , 2011 .

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

[27]  Woo Sung Kim,et al.  On an automated material handling system design problem in cellular manufacturing systems , 2019 .

[28]  Hongbo Jin,et al.  Analytical methods for perfect partition by set theory , 2018 .

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

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

[31]  Yong Yin,et al.  A multi-skilled worker assignment problem in seru production systems considering the worker heterogeneity , 2018, Comput. Ind. Eng..

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

[33]  Yang Yu,et al.  A Cooperative Coevolution Algorithm for the Seru Production With Minimizing Makespan , 2019, IEEE Access.

[34]  Yong Yin,et al.  Reliability Analysis for a Divisional Seru Production System with Stochastic Capacity , 2018, 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[35]  Ashkan Ayough,et al.  Job rotation scheduling in the Seru system: shake enforced invasive weed optimization approach , 2020 .

[36]  Kum Khiong Yang,et al.  Maximizing Throughput of Bucket Brigades on Discrete Work Stations , 2009 .

[37]  Gürsel A. Süer,et al.  Cell loading and manpower allocation with fuzzy multiple objectives in synchronised manufacturing cells , 2016 .

[38]  Shuai Wang,et al.  Automatic Design of Intercell Scheduling Heuristics , 2019, IEEE Transactions on Automation Science and Engineering.

[39]  Yun Fong Lim Performance of Cellular Bucket Brigades with Hand-off Times , 2017 .

[40]  B. Naderi,et al.  The type E simple assembly line balancing problem: A mixed integer linear programming formulation , 2015, Comput. Oper. Res..

[41]  Ikou Kaku,et al.  A Mathematical Model for Converting Conveyor Assembly Line to Cellular Manufacturing , 2008 .

[42]  Kuo-Ching Ying,et al.  Minimising total cost for training and assigning multiskilled workers in seru production systems , 2017, Int. J. Prod. Res..

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

[44]  Ikou Kaku,et al.  Reducing worker(s) by converting assembly line into a pure cell system , 2013 .

[45]  Yong Yin,et al.  An innovative artificial bee colony algorithm and its application to a practical intercell scheduling problem , 2017 .

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

[47]  K. Yasuda *,et al.  A grouping genetic algorithm for the multi-objective cell formation problem , 2005 .

[48]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1967 .

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

[50]  Ömer Faruk Yılmaz,et al.  Attaining flexibility in seru production system by means of Shojinka: An optimization model and solution approaches , 2020, Comput. Oper. Res..

[51]  Donald D. Eisenstein,et al.  A Production Line that Balances Itself , 1996, Oper. Res..

[52]  Ikou Kaku,et al.  Line-seru conversion towards reducing worker(s) without increasing makespan: models, exact and meta-heuristic solutions , 2017, Int. J. Prod. Res..