January  2017, 13(1): 413-428. doi: 10.3934/jimo.2016024

Multiple-stage multiple-machine capacitated lot-sizing and scheduling with sequence-dependent setup: A case study in the wheel industry

Graduate School of Decision Science and Technology, Tokyo Institute of Technology, Tokyo 152-8552, Japan

Received  May 2015 Published  March 2016

This paper studies a real-world problem of simultaneous lot-sizing and scheduling in a capacitated flow shop. The problem combines two significant characteristics in production which are multiple-stage production with heterogeneous multiple machines and sequence-dependent setup time. Setup time does not hold the triangle inequality, thus there may be a setup for a product without actual production. Consequently, a novel mixed integer programming (MIP) formulation is proposed and tested on real data sets of wheel production. Exact approaches cannot find a feasible solution for the model in a reasonable time, so MIP-based heuristics are developed to solve the model more quickly. Test results show that the formulation is able to contain the problem requirements and the heuristics are computationally effective. Moreover, the obtained solution can improve on a real practice at the plant.

Citation: Lalida Deeratanasrikul, Shinji Mizuno. Multiple-stage multiple-machine capacitated lot-sizing and scheduling with sequence-dependent setup: A case study in the wheel industry. Journal of Industrial & Management Optimization, 2017, 13 (1) : 413-428. doi: 10.3934/jimo.2016024
References:
[1]

A. AllahverdiC. NgT. Cheng and M. Y. Kovalyov, A survey of scheduling problems with setup times or costs, European Journal of Operational Research, 187 (2008), 985-1032.  doi: 10.1016/j.ejor.2006.06.060.  Google Scholar

[2]

B. Almada-loboD. KlabjanM. Antnia carravilla and J. F. Oliveira, Single machine multi-product capacitated lot sizing with sequence-dependent setups, International Journal of Production Research, 45 (2007), 4873-4894.  doi: 10.1080/00207540601094465.  Google Scholar

[3]

A. Drexl and A. Kimms, Lot sizing and scheduling survey and extensions, European Journal of Operational Research, 99 (1997), 221-235.  doi: 10.1016/S0377-2217(97)00030-1.  Google Scholar

[4]

M. GnoniR. IavagnilioG. MossaG. Mummolo and A. D. Leva, Production planning of a multisite, manufacturing system by hybrid modelling: A case study from the automotive industry, International Journal of Production Economics, 85 (2003), 251-262.   Google Scholar

[5]

K. Haase, Capacitated lot-sizing with sequence dependent setup costs, Operations-Research-Spektrum, 18 (1996), 51-59.  doi: 10.1007/BF01539882.  Google Scholar

[6]

R. J. James and B. Almada-Lobo, Single and parallel machine capacitated lotsizing and scheduling: New iterative mip-based neighborhood search heuristics, Computers & Operations Research, 38 (2011), 1816-1825.  doi: 10.1016/j.cor.2011.02.005.  Google Scholar

[7]

R. Jans and Z. Degraeve, Meta-heuristics for dynamic lot sizing: A review and comparison of solution approaches, European Journal of Operational Research, 177 (2007), 1855-1875.  doi: 10.1016/j.ejor.2005.12.008.  Google Scholar

[8]

M. GnoniR. IavagnilioG. MossaG. Mummolo and A. D. Leva, Fix-and-Optimize heuristics for capacitated lot-sizing with sequence-dependent setups and substitutions, European Journal of Operational Research, 214 (2011), 595-605.   Google Scholar

[9]

A. MenezesA. Clark and B. Almada-Lobo, Capacitated lot-sizing and scheduling with sequencedependent, period-overlapping and non-triangular setups, Journal of Scheduling, 14 (2011), 209-219.  doi: 10.1007/s10951-010-0197-6.  Google Scholar

[10]

C. E. MillerA. W. Tucker and R. A. Zemlin, Integer programming formulation of traveling salesman problems, Journal of the ACM, 7 (1960), 326-329.  doi: 10.1145/321043.321046.  Google Scholar

[11]

OICA Production statistics, Report of International Organization of Motor Vehicle Manufacturers, 2014. Available from: http://www.oica.net/category/production-statistics. Google Scholar

[12]

D. Quadt and H. Kuhn, Capacitated lot-sizing with extensions: A review, 4OR, 6 (2008), 61-83.  doi: 10.1007/s10288-007-0057-1.  Google Scholar

[13]

F. SeeannerB. Almada-Lobo and H. Meyr, Combining the principles of variable neighborhood decomposition search and the fix & optimize heuristic to solve multi-level lot-sizing and scheduling problems, Computers & Operations Research, 40 (2003), 303-317.  doi: 10.1016/j.cor.2012.07.002.  Google Scholar

[14]

F. Seeanner and H. Meyr, Multi-stage simultaneous lot-sizing and scheduling for flow line production, OR Spectrum, 35 (2013), 33-73.  doi: 10.1007/s00291-012-0296-1.  Google Scholar

[15]

F. Seeanner, Multi-Stage Simultaneous Lot-Sizing and Scheduling: Planning of Flow Lines with Shifting Bottlenecks, Damstadt: Springer Fachmedien Wiesbaden, 2013. doi: 10.1007/978-3-658-02089-7.  Google Scholar

[16]

H. Stadtler and F. Sahling, A lot-sizing and scheduling model for multi-stage flow lines with zero lead times, European Journal of Operational Research, 225 (2013), 404-419.  doi: 10.1016/j.ejor.2012.10.011.  Google Scholar

[17]

J. XiaoC. ZhangL. Zheng and J. N. D. Gupta, Mip-based Fix-and-Optimize algorithms for the parallel machine capacitated lot-sizing and scheduling problem, International Journal of Production Research, 51 (2013), 5011-5028.   Google Scholar

[18]

X. Zhu and W. E. Wilhelm, Scheduling and lot sizing with sequence-dependent setup: A literature review, IIE Transactions, 38 (2006), 987-1007.  doi: 10.1080/07408170600559706.  Google Scholar

show all references

References:
[1]

A. AllahverdiC. NgT. Cheng and M. Y. Kovalyov, A survey of scheduling problems with setup times or costs, European Journal of Operational Research, 187 (2008), 985-1032.  doi: 10.1016/j.ejor.2006.06.060.  Google Scholar

[2]

B. Almada-loboD. KlabjanM. Antnia carravilla and J. F. Oliveira, Single machine multi-product capacitated lot sizing with sequence-dependent setups, International Journal of Production Research, 45 (2007), 4873-4894.  doi: 10.1080/00207540601094465.  Google Scholar

[3]

A. Drexl and A. Kimms, Lot sizing and scheduling survey and extensions, European Journal of Operational Research, 99 (1997), 221-235.  doi: 10.1016/S0377-2217(97)00030-1.  Google Scholar

[4]

M. GnoniR. IavagnilioG. MossaG. Mummolo and A. D. Leva, Production planning of a multisite, manufacturing system by hybrid modelling: A case study from the automotive industry, International Journal of Production Economics, 85 (2003), 251-262.   Google Scholar

[5]

K. Haase, Capacitated lot-sizing with sequence dependent setup costs, Operations-Research-Spektrum, 18 (1996), 51-59.  doi: 10.1007/BF01539882.  Google Scholar

[6]

R. J. James and B. Almada-Lobo, Single and parallel machine capacitated lotsizing and scheduling: New iterative mip-based neighborhood search heuristics, Computers & Operations Research, 38 (2011), 1816-1825.  doi: 10.1016/j.cor.2011.02.005.  Google Scholar

[7]

R. Jans and Z. Degraeve, Meta-heuristics for dynamic lot sizing: A review and comparison of solution approaches, European Journal of Operational Research, 177 (2007), 1855-1875.  doi: 10.1016/j.ejor.2005.12.008.  Google Scholar

[8]

M. GnoniR. IavagnilioG. MossaG. Mummolo and A. D. Leva, Fix-and-Optimize heuristics for capacitated lot-sizing with sequence-dependent setups and substitutions, European Journal of Operational Research, 214 (2011), 595-605.   Google Scholar

[9]

A. MenezesA. Clark and B. Almada-Lobo, Capacitated lot-sizing and scheduling with sequencedependent, period-overlapping and non-triangular setups, Journal of Scheduling, 14 (2011), 209-219.  doi: 10.1007/s10951-010-0197-6.  Google Scholar

[10]

C. E. MillerA. W. Tucker and R. A. Zemlin, Integer programming formulation of traveling salesman problems, Journal of the ACM, 7 (1960), 326-329.  doi: 10.1145/321043.321046.  Google Scholar

[11]

OICA Production statistics, Report of International Organization of Motor Vehicle Manufacturers, 2014. Available from: http://www.oica.net/category/production-statistics. Google Scholar

[12]

D. Quadt and H. Kuhn, Capacitated lot-sizing with extensions: A review, 4OR, 6 (2008), 61-83.  doi: 10.1007/s10288-007-0057-1.  Google Scholar

[13]

F. SeeannerB. Almada-Lobo and H. Meyr, Combining the principles of variable neighborhood decomposition search and the fix & optimize heuristic to solve multi-level lot-sizing and scheduling problems, Computers & Operations Research, 40 (2003), 303-317.  doi: 10.1016/j.cor.2012.07.002.  Google Scholar

[14]

F. Seeanner and H. Meyr, Multi-stage simultaneous lot-sizing and scheduling for flow line production, OR Spectrum, 35 (2013), 33-73.  doi: 10.1007/s00291-012-0296-1.  Google Scholar

[15]

F. Seeanner, Multi-Stage Simultaneous Lot-Sizing and Scheduling: Planning of Flow Lines with Shifting Bottlenecks, Damstadt: Springer Fachmedien Wiesbaden, 2013. doi: 10.1007/978-3-658-02089-7.  Google Scholar

[16]

H. Stadtler and F. Sahling, A lot-sizing and scheduling model for multi-stage flow lines with zero lead times, European Journal of Operational Research, 225 (2013), 404-419.  doi: 10.1016/j.ejor.2012.10.011.  Google Scholar

[17]

J. XiaoC. ZhangL. Zheng and J. N. D. Gupta, Mip-based Fix-and-Optimize algorithms for the parallel machine capacitated lot-sizing and scheduling problem, International Journal of Production Research, 51 (2013), 5011-5028.   Google Scholar

[18]

X. Zhu and W. E. Wilhelm, Scheduling and lot sizing with sequence-dependent setup: A literature review, IIE Transactions, 38 (2006), 987-1007.  doi: 10.1080/07408170600559706.  Google Scholar

Figure 1.  Production process flow
Figure 2.  Example of bill of materials from one type of first-stage product
Figure 3.  A disconnected subtour and a main sequence
Figure 4.  A subtour connected to a main sequence at the beginning of period
Figure 5.  Relax and fix heuristic on multi-stage and over the periods
Figure 6.  Comparison of total setup time between the company planning and our model
Figure 7.  Comparison of total inventory level between the company planning and our model
Figure 8.  Comparison of total overtime between the company planning and our model
Table 1.  Average objective values in detailed
q Setup time (sec) Inventory level (pieces) Overtime (sec)
W=1000 W=100 W=10 W=1000 W=100 W=10 W=1000 W=100 W=10
20573,750511,500407,8503,84311,43713,9062,247,8578,0297,712
100529,500521,100404,40010,35611,55714,13317,1007,7137,712
200539,100521,250395,28011,53511,40913,6807,8687,7137,712
300545,250506,850398,45011,44311,25713,3808,2457,7127,712
400559,350519,300404,85011,75711,57913,7377,7257,7127,712
q Setup time (sec) Inventory level (pieces) Overtime (sec)
W=1000 W=100 W=10 W=1000 W=100 W=10 W=1000 W=100 W=10
20573,750511,500407,8503,84311,43713,9062,247,8578,0297,712
100529,500521,100404,40010,35611,55714,13317,1007,7137,712
200539,100521,250395,28011,53511,40913,6807,8687,7137,712
300545,250506,850398,45011,44311,25713,3808,2457,7127,712
400559,350519,300404,85011,75711,57913,7377,7257,7127,712
Table 2.  Numerical results of small problems
Problem size($N \times M \times T$) $<$1000 1000—4000 4000—6000
MIP Heu. MIP Heu. MIP Heu.
Avg. Time (sec)8716958352261090816461774
Avg. Gap (%)3.945.675.448.636.719.22
StDev. Gap1.814.061.886.192.733.36
Problem size($N \times M \times T$) $<$1000 1000—4000 4000—6000
MIP Heu. MIP Heu. MIP Heu.
Avg. Time (sec)8716958352261090816461774
Avg. Gap (%)3.945.675.448.636.719.22
StDev. Gap1.814.061.886.192.733.36
Table 3.  Numerical results of real problems by our heuristics
Avg. Time(sec)Avg. LBDev(%)
High variant of products family833018.54
Low variant of products family27561.47
Avg. Time(sec)Avg. LBDev(%)
High variant of products family833018.54
Low variant of products family27561.47
Table 4.  Total objective value between the company solutions and our model solutions
Week1234
Company1,473,4001,973,4052,008,30015,855,500
Model1,209,1001,294,4001,885,50011,345,500
Week1234
Company1,473,4001,973,4052,008,30015,855,500
Model1,209,1001,294,4001,885,50011,345,500
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