Balancing of manufacturing systems with complex configurations for delayed product differentiation

被引:29
作者
Ko, Jeonghan [1 ]
Hu, S. Jack [1 ]
机构
[1] Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA
关键词
balancing; manufacturing systems; complex asymmetric configurations; delayed product differentiation;
D O I
10.1080/00207540601078047
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In response to increasing product variety, manufacturing systems with complex asymmetric configurations have often been used for delayed product differentiation. Balancing these types of manufacturing systems, however, is a challenge, since existing line balancing methods usually have been developed for symmetric configurations, such as serial lines with parallel machines at some stages. This paper proposes a novel binary integer programming (BIP) model for task-machine assignment and workload balancing in complex asymmetric configurations for mixed-model production. The new model includes (1) mathematical representations of task relations and system configurations, (2) constraint equations for task precedence relations in asymmetric configurations, and (3) constraint equations for parallel/serial relations among tasks. This study extends the area of line balancing and task-machine assignment problems to asymmetric system configurations, and helps to select a configuration from alternatives.
引用
收藏
页码:4285 / 4308
页数:24
相关论文
共 42 条
[31]   Big size highly customised product manufacturing systems: a literature review and future research agenda [J].
Zennaro, Ilenia ;
Finco, Serena ;
Battini, Daria ;
Persona, Alessandro .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2019, 57 (15-16) :5362-5385
[32]   The Development of a Discrete-Event Simulation Model to Aid the Design of Complex Manufacturing Systems [J].
Massey, T. ;
Wang, Q. .
ENGINEERING LETTERS, 2008, 16 (01)
[33]   Dynamic optimal control and simulation for unreliable manufacturing systems under perishable product and shelf life variability [J].
Gharbi, Ali ;
Kenne, Jean-Pierre ;
Kaddachi, Rawia .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2022, 247
[34]   The application of web-based technologies in product data management and manufacturing systems interoperability and data exchange [J].
Cheung, WM ;
Maropoulos, PG ;
Gao, JX ;
Aziz, H .
ECEC 2003: 10th European Concurrent Engineering Conference 2003: CONCURRENT ENGINEERING: TEN YEARS ON, 2003, :80-84
[35]   A new data analytics framework emphasising preprocessing of data to generate insights into complex manufacturing systems [J].
Carbery, Caoimhe M. ;
Woods, Roger ;
Marshall, Adele H. .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2019, 233 (19-20) :6713-6726
[36]   Reduced order dynamical models for complex dynamics in manufacturing and natural systems using machine learning [J].
Farlessyost, William ;
Singh, Shweta .
NONLINEAR DYNAMICS, 2022, 110 (02) :1613-1631
[37]   Reduced order dynamical models for complex dynamics in manufacturing and natural systems using machine learning [J].
William Farlessyost ;
Shweta Singh .
Nonlinear Dynamics, 2022, 110 :1613-1631
[38]   A Distributed Control Approach to Automated Manufacturing Systems With Complex Routes and Operations Using Petri Nets [J].
Yang, Yan ;
Hu, Hesuan .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2020, 50 (10) :3670-3684
[39]   Modelling and controlling product manufacturing systems using bond-graphs and state equations: continuous systems and discrete systems which can be represented by continuous models [J].
Ferney, M .
PRODUCTION PLANNING & CONTROL, 2000, 11 (01) :7-19
[40]   Scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers [J].
Feng, Wei ;
Zheng, Li ;
Li, Jingshan .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2012, 50 (24) :7479-7492