An assemble-to-order production planning with the integration of order scheduling and mixed-model sequencing

Baoxi WANG, Zailin GUAN, Yarong CHEN, Xinyu SHAO, Ming JIN, Chaoyong ZHANG

PDF(242 KB)
PDF(242 KB)
Front. Mech. Eng. ›› 2013, Vol. 8 ›› Issue (2) : 137-145. DOI: 10.1007/s11465-013-0251-0
RESEARCH ARTICLE

An assemble-to-order production planning with the integration of order scheduling and mixed-model sequencing

Author information +
History +

Abstract

For assemble-to-order enterprises, both order scheduling and mixed-model sequencing need to be taken into consideration in the formulation of order-oriented assembly plan. First, determining production priority for the received orders, and then conducting assembly sequence to the mixed-model products in each order. Order scheduling is aimed to ensure order delivery with the optimization goal of minimal total overdue time, while product sequencing is aimed to minimize the makespan so as to meet the requirement on completion time of the order. In the end, the paper establishes a mixed integer programming model based on an industrial case, and makes programming calculation with Xpress-MP to accomplish an order-oriented assembly plan conforming to actual production.

Keywords

mixed-model assembly line / assemble-to-order / order scheduling / sequencing

Cite this article

Download citation ▾
Baoxi WANG, Zailin GUAN, Yarong CHEN, Xinyu SHAO, Ming JIN, Chaoyong ZHANG. An assemble-to-order production planning with the integration of order scheduling and mixed-model sequencing. Front Mech Eng, 2013, 8(2): 137‒145 https://doi.org/10.1007/s11465-013-0251-0

References

[1]
Neidigh R O, Harrison T P. Optimizing lot sizing and order scheduling with non-linear production rates. International Journal of Production Research, 2010, 48(8): 2279–2295
CrossRef Google scholar
[2]
Hazır O, Gunalay Y, Erel E. Customer order scheduling problem: a comparative metaheuristics study. International Journal of Advanced Manufacturing Technology, 2008, 37(5-6): 589–598
CrossRef Google scholar
[3]
Erel E, Ghosh J B. Customer order scheduling on a single machine with family setup times: complexity and algorithms. Applied Mathematics and Computation, 2007, 185(1): 11–18
CrossRef Google scholar
[4]
Hsu S Y, Liu C H. Improving the delivery efficiency of the customer order scheduling problem in a job shop. Computers and Industrial Engineering, 2009, 57(3): 856–866
[5]
Finch B J, Luebbe R L. Response to 'theory of constraints and linear programming: a re-examination'. International Journal of Production Research, 2000, 38(6): 1465–1466
CrossRef Google scholar
[6]
Mosadegh H, Zandieh M, Ghomi S. Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines. Applied Soft Computing, 2012, 12(4): 1359–1370
CrossRef Google scholar
[7]
Fattahi P, Beitollahi Tavakoli N, Fathollah M, Roshani A, Salehi M. Sequencing mixed-model assembly lines by considering feeding lines. International Journal of Advanced Manufacturing Technology, 2012, 61(5-8): 677–690
CrossRef Google scholar
[8]
Bautista J, Cano A. Solving mixed model sequencing problem in assembly lines with serial workstations with work overload minimisation and interruption rules. European Journal of Operational Research, 2011, 210(3): 495–513
CrossRef Google scholar
[9]
Giard V, Jeunet J. Optimal sequencing of mixed models with sequence-dependent setups and utility workers on an assembly line. International Journal of Production Economics, 2010, 123(2): 290–300
CrossRef Google scholar
[10]
Drexl A, Kimms A, Matthiessen L. Algorithms for the car sequencing and the level scheduling problem. Journal of Scheduling, 2006, 9(2): 153–176
CrossRef Google scholar
[11]
Mansouri S A. A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines. European Journal of Operational Research, 2005, 167(3): 696–716
CrossRef Google scholar
[12]
De Lit P, Latinne P, Rekiek B, Delchambre A. Assembly planning with an ordering genetic algorithm. International Journal of Production Research, 2001, 39(16): 3623–3640
CrossRef Google scholar

Acknowledgements

This work has been supported by MOST( the Ministry of Science & Technology of China) under the Grants No.2012AA040909 & 2012BAH08F04, and by the National Natural Science Foundation of China( Grants No. 51035001, 50825503, & 71271156).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(242 KB)

Accesses

Citations

Detail

Sections
Recommended

/