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The loss-averse newsvendor problem with random supply capacity
Rescheduling optimization of steelmaking-continuous casting process based on the Lagrangian heuristic algorithm
1. | Department of Information and Control Engineering, Shenyang Jianzhu University, No. 9, Hunnan East Road, Hunnan New District, Shenyang City, Liaoning 110168, China |
2. | Department of Information Science and Engineering, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang City, Liaoning 110004, China |
This study investigates a challenging problem of rescheduling a hybrid flow shop in the steelmaking-continuous casting (SCC) process, which is a major bottleneck in the production of iron and steel. In consideration of uncertain disturbance during SCC process, we develop a time-indexed formulation to model the SCC rescheduling problem. The performances of the rescheduling problem consider not only the efficiency measure, which includes the total weighted completion time and the total waiting time, but also the stability measure, which refers to the difference in the number of operations processed on different machines for the different stage in the original schedule and revised schedule. With these objectives, this study develops a Lagrangian heuristic algorithm to solve the SCC rescheduling problem. The algorithm could provide a realizable termination criterion without having information about the problem, such as the distance between the initial iterative point and the optimal point. This study relaxes machine capacity constraints to decompose the relaxed problem into charge-level subproblems that can be solved using a polynomial dynamic programming algorithm. A heuristic based on the solution of the relaxed problem is presented for obtaining a feasible reschedule. An improved efficient subgradient algorithm is introduced for solving Lagrangian dual problems. Numerical results for different events and problem scales show that the proposed approach can generate high-quality reschedules within acceptable computational times.
References:
[1] |
A. Atighehchian, M. Bijari and H. Tarkesh, A novel hybrid algorithm for scheduling steelmaking continuous casting production, Computers and Operations Research, 36 (2009), 2450-2461. Google Scholar |
[2] |
H. Aytug, M. Lawley, K. McKay, S. Mohan and R. Uzsoy,
Executing production schedules in the face of uncertainties: A review and some future directions, European Journal of Operational Research, 161 (2005), 86-110.
doi: 10.1016/j.ejor.2003.08.027. |
[3] |
A. Bellabdaoui and J. Teghem,
A mixed-integer linear programming model for the continuous casting planning, International Journal of Production Economics, 104 (2006), 260-270.
doi: 10.1016/j.ijpe.2004.10.016. |
[4] |
D. Bertsekas,
Nonlinear Programming, 2$^{nd}$ edition, Athena Scientific, Massachusetts, 1999.
doi: 10.1007/978-1-4612-0873-0. |
[5] |
U. Brannlund,
On Relaxation Methods for Nonsmooth Convex Optimization, Ph. D thesis, Royal Institute of Technology in Stockholm, 1993. |
[6] |
P. Camerini, L. Fratta and F. Maffioli,
On improving relaxation methods by modified gradient techniques, Mathematical Programming Study, 3 (1975), 26-34.
|
[7] |
H. Chen and P. Luh,
An alternative framework to Lagrangian relaxation approach for job shop scheduling, European Journal of Operational Research, 149 (2003), 499-512.
doi: 10.1016/S0377-2217(02)00470-8. |
[8] |
P. Cowling, D. Ouelhadj and S. Petrovic,
Dynamic scheduling of steel casting and milling using multi-agents, Production Planning and Control, 15 (2004), 178-188.
doi: 10.1080/09537280410001662466. |
[9] |
V. Demjanov and V. Somesova,
Conditional subdifferentials of convex functions, Soviet Mathematics Doklady, 19 (1978), 1181-1185.
|
[10] |
J. Goffin and K. Kiwiel,
Convergence of a simple subgradient level method, Mathematical Programming, 85 (1999), 207-211.
doi: 10.1007/s101070050053. |
[11] |
B. Guta, Subgradient Optimization Methods in Integer Programming with an Application to a Radiation Therapy Problem, Ph. D thesis, Teknishe Universitat Kaiserlautern in Kaiserlauter, 2003. Google Scholar |
[12] |
I. Harjunkoski and I. Grossmann,
A decomposition approach for the scheduling of a steel plant production, Computers and Chemical Engineering, 25 (2001), 1647-1660.
doi: 10.1016/S0098-1354(01)00729-3. |
[13] |
T. Larsson, M. Patriksson and A. Stromberg,
Conditional subgradient optimization --theory and applications, European Journal of Operational Research, 88 (1996), 382-403.
doi: 10.1016/0377-2217(94)00200-2. |
[14] |
J. Li, X. Xiao, Q. Tang and C. Floudas,
Production scheduling of a Large-scale steelmaking continuous casting process via unit-specific event-based continuous-time models: Short-term and medium-term scheduling, Industrial and Engineering Chemistry Research, 51 (2012), 7300-7319.
doi: 10.1021/ie2015944. |
[15] |
P. Luh and D. Hoitomt,
Scheduling of manufacturing systems using the Lagrangian relaxation technique, IEEE Transactions on Automatic Control, 38 (1993), 1066-1079.
doi: 10.1109/9.231461. |
[16] |
P. Luh, D. Hoitomt, E. Max and K. Pattipati, Scheduling generation and reconfiguration for parallel machines, IEEE Transactions on Robotics and Automation, 6 (1990), 687-696. Google Scholar |
[17] |
K. Mao, Q. Pan, X. Pang and T. Chai,
A novel Lagrangian relaxation approach for the hybrid flowshop scheduling problem in a steelmaking-continuous casting process, European Journal of Operational Research, 236 (2014), 51-60.
doi: 10.1016/j.ejor.2013.11.010. |
[18] |
K. Mao, Q. Pan, X. Pang and T. Chai,
An effective Lagrangian relaxation approach for rescheduling a steelmaking-continuous casting process, Control Engineering Practice, 30 (2014), 67-77.
doi: 10.1016/j.conengprac.2014.06.003. |
[19] |
K. Mao, Q. Pan, X. Pang, T. Chai and P. Luh,
An Effective Subgradient Method for Scheduling a Steelmaking-Continuous Casting Process, IEEE Transactions on Automation Science and Engineering, 12 (2014), 1-13.
doi: 10.1109/TASE.2014.2332511. |
[20] |
H. Missbauer, W. Hauber and W. Werner Stadler,
A scheduling system for the steelmaking-continuous casting process: A case study from the steelmaking industry, International Journal of Production Research, 47 (2009), 4147-4172.
doi: 10.1080/00207540801950136. |
[21] |
A. Nedic and D. Bertsekas,
Incremental Subgradient Methods for Nondifferentiable Optimization, SIAM Journal on Optimization, 12 (2001), 109-138.
doi: 10.1137/S1052623499362111. |
[22] |
T. Nishi, Y. Hiranaka and M. Inuiguchi,
Lagrangian relaxation with cut generation for hybrid flowshop scheduling problems to minimize the total weighted tardiness, Computers and Operations Research, 37 (2010), 189-198.
doi: 10.1016/j.cor.2009.04.008. |
[23] |
T. Nishi, Y. Isoya Y and M. Inuiguchi,
An integrated column generation and lagrangian relaxation for flowshop scheduling problems, Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics, (2009), 209-304.
doi: 10.1109/ICSMC.2009.5346159. |
[24] |
D. Ouelhadj, P. Cowling and S. Petrovic,
Utility and stability measures for agent-based dynamic scheduling of steel continuous casting, Journal of Scheduling, 12 (2009), 417-431.
doi: 10.1109/ROBOT.2003.1241592. |
[25] |
D. Ouelhadj and S. Petrovic,
A survey of dynamic scheduling in manufacturing systems, Journal of Scheduling, 12 (2009), 417-431.
doi: 10.1007/s10951-008-0090-8. |
[26] |
D. Ouelhadj, S. Petrovic, P. Cowling and A. Meisels,
Inter-agent cooperation and communication for agent-based robust dynamic scheduling in steel production, Advanced Engineering Informatics, 18 (2004), 161-172.
doi: 10.1016/j.aei.2004.10.003. |
[27] |
D. Pacciarelli and M. Pranzo,
Production scheduling in a steelmaking-continuous casting plant, Computers and Chemical Engineering, 28 (2004), 2823-2835.
doi: 10.1016/j.compchemeng.2004.08.031. |
[28] |
Q. Pan, L. Wang, K. Mao, J. Zhao and M. Zhang,
An Effective Artificial Bee Colony Algorithm for a Real-World Hybrid Flowshop Problem in Steelmaking Process, IEEE Transactions on Automation Science and Engineering, 10 (2013), 307-322.
doi: 10.1109/TASE.2012.2204874. |
[29] |
H. Sherali, G. Choi and C. Tuncbilek,
A Variable Target Value Method for Nondifferentiable Optimization, Operation Research Letters, 26 (2000), 1-8.
doi: 10.1016/S0167-6377(99)00063-2. |
[30] |
L. Sun, Research on the Optimal Scheduling Method for the productive Process of Steelmaking-Refining-Continuous Casting, Ph. D thesis, Northeastern University in Shenyang, 2015. Google Scholar |
[31] |
L. Tang, J. Liu, A. Rong and Z. Yang,
A review of planning and scheduling systems and methods for integrated steel production, European Journal of Operational Research, 133 (2001), 1-20.
doi: 10.1016/S0377-2217(00)00240-X. |
[32] |
L. Tang, P. Luh, J. Liu and L. Fang, Steelmaking process scheduling using Lagrangian relaxation, International Journal of Production Research, 40 (2002), 55-70. Google Scholar |
[33] |
L. Tang, G. Wang and Z. Chen,
Integrated charge batching and casting width selection at Baosteel, Operations Research, 62 (2014), 772-787.
doi: 10.1287/opre.2014.1278. |
[34] |
L. Tang, Y. Zhao and J. Liu,
An Improved Differential Evolution Algorithm for Practical Dynamic Scheduling in Steelmaking-continuous Casting Production, IEEE Transactions on Evolutionary Computation, 18 (2014), 209-213.
doi: 10.1109/TEVC.2013.2250977. |
[35] |
G. Vieira, J. Hermann and E. Lin,
Rescheduling manufacturing systems: a framework of strategies, policies and methods, Journal of Scheduling, 6 (2003), 36-92.
doi: 10.1023/A:1022235519958. |
[36] |
R. Xiong, Y. Fan and C. Wu, A dynamic job shop scheduling method based on Lagrangian relaxation, Tsinghua Science and Technology, 4 (1999), 1297-1302. Google Scholar |
[37] |
H. Xuan and L. Tang,
Scheduling a hybrid flowshop with batch production at the last stage, Computers and Operations Research, 34 (2007), 2718-2733.
doi: 10.1016/j.cor.2005.10.014. |
[38] |
S. Yu and Q. Pan,
A Rescheduling Method for Operation Time Delay Disturbance in Steelmaking and Continuous Casting Production Process, International Journal of Iron and Steel Research, 19 (2012), 33-41.
doi: 10.1016/S1006-706X(13)60029-1. |
[39] |
H. Zhong, X Dong and H. Shi, Research on the load balancing scheduling problem of reentrant hybrid flowshops, Chinese High Technology Letters, 25 (2015), 70-81. Google Scholar |
[40] |
H. Zhong, Y Zhu and S. Lin, A dynamic co-evolution compact genetic algorithm for E/T problem, The 17th IFAC Symposium on System Identification, (2015), 1433-1437. Google Scholar |
show all references
References:
[1] |
A. Atighehchian, M. Bijari and H. Tarkesh, A novel hybrid algorithm for scheduling steelmaking continuous casting production, Computers and Operations Research, 36 (2009), 2450-2461. Google Scholar |
[2] |
H. Aytug, M. Lawley, K. McKay, S. Mohan and R. Uzsoy,
Executing production schedules in the face of uncertainties: A review and some future directions, European Journal of Operational Research, 161 (2005), 86-110.
doi: 10.1016/j.ejor.2003.08.027. |
[3] |
A. Bellabdaoui and J. Teghem,
A mixed-integer linear programming model for the continuous casting planning, International Journal of Production Economics, 104 (2006), 260-270.
doi: 10.1016/j.ijpe.2004.10.016. |
[4] |
D. Bertsekas,
Nonlinear Programming, 2$^{nd}$ edition, Athena Scientific, Massachusetts, 1999.
doi: 10.1007/978-1-4612-0873-0. |
[5] |
U. Brannlund,
On Relaxation Methods for Nonsmooth Convex Optimization, Ph. D thesis, Royal Institute of Technology in Stockholm, 1993. |
[6] |
P. Camerini, L. Fratta and F. Maffioli,
On improving relaxation methods by modified gradient techniques, Mathematical Programming Study, 3 (1975), 26-34.
|
[7] |
H. Chen and P. Luh,
An alternative framework to Lagrangian relaxation approach for job shop scheduling, European Journal of Operational Research, 149 (2003), 499-512.
doi: 10.1016/S0377-2217(02)00470-8. |
[8] |
P. Cowling, D. Ouelhadj and S. Petrovic,
Dynamic scheduling of steel casting and milling using multi-agents, Production Planning and Control, 15 (2004), 178-188.
doi: 10.1080/09537280410001662466. |
[9] |
V. Demjanov and V. Somesova,
Conditional subdifferentials of convex functions, Soviet Mathematics Doklady, 19 (1978), 1181-1185.
|
[10] |
J. Goffin and K. Kiwiel,
Convergence of a simple subgradient level method, Mathematical Programming, 85 (1999), 207-211.
doi: 10.1007/s101070050053. |
[11] |
B. Guta, Subgradient Optimization Methods in Integer Programming with an Application to a Radiation Therapy Problem, Ph. D thesis, Teknishe Universitat Kaiserlautern in Kaiserlauter, 2003. Google Scholar |
[12] |
I. Harjunkoski and I. Grossmann,
A decomposition approach for the scheduling of a steel plant production, Computers and Chemical Engineering, 25 (2001), 1647-1660.
doi: 10.1016/S0098-1354(01)00729-3. |
[13] |
T. Larsson, M. Patriksson and A. Stromberg,
Conditional subgradient optimization --theory and applications, European Journal of Operational Research, 88 (1996), 382-403.
doi: 10.1016/0377-2217(94)00200-2. |
[14] |
J. Li, X. Xiao, Q. Tang and C. Floudas,
Production scheduling of a Large-scale steelmaking continuous casting process via unit-specific event-based continuous-time models: Short-term and medium-term scheduling, Industrial and Engineering Chemistry Research, 51 (2012), 7300-7319.
doi: 10.1021/ie2015944. |
[15] |
P. Luh and D. Hoitomt,
Scheduling of manufacturing systems using the Lagrangian relaxation technique, IEEE Transactions on Automatic Control, 38 (1993), 1066-1079.
doi: 10.1109/9.231461. |
[16] |
P. Luh, D. Hoitomt, E. Max and K. Pattipati, Scheduling generation and reconfiguration for parallel machines, IEEE Transactions on Robotics and Automation, 6 (1990), 687-696. Google Scholar |
[17] |
K. Mao, Q. Pan, X. Pang and T. Chai,
A novel Lagrangian relaxation approach for the hybrid flowshop scheduling problem in a steelmaking-continuous casting process, European Journal of Operational Research, 236 (2014), 51-60.
doi: 10.1016/j.ejor.2013.11.010. |
[18] |
K. Mao, Q. Pan, X. Pang and T. Chai,
An effective Lagrangian relaxation approach for rescheduling a steelmaking-continuous casting process, Control Engineering Practice, 30 (2014), 67-77.
doi: 10.1016/j.conengprac.2014.06.003. |
[19] |
K. Mao, Q. Pan, X. Pang, T. Chai and P. Luh,
An Effective Subgradient Method for Scheduling a Steelmaking-Continuous Casting Process, IEEE Transactions on Automation Science and Engineering, 12 (2014), 1-13.
doi: 10.1109/TASE.2014.2332511. |
[20] |
H. Missbauer, W. Hauber and W. Werner Stadler,
A scheduling system for the steelmaking-continuous casting process: A case study from the steelmaking industry, International Journal of Production Research, 47 (2009), 4147-4172.
doi: 10.1080/00207540801950136. |
[21] |
A. Nedic and D. Bertsekas,
Incremental Subgradient Methods for Nondifferentiable Optimization, SIAM Journal on Optimization, 12 (2001), 109-138.
doi: 10.1137/S1052623499362111. |
[22] |
T. Nishi, Y. Hiranaka and M. Inuiguchi,
Lagrangian relaxation with cut generation for hybrid flowshop scheduling problems to minimize the total weighted tardiness, Computers and Operations Research, 37 (2010), 189-198.
doi: 10.1016/j.cor.2009.04.008. |
[23] |
T. Nishi, Y. Isoya Y and M. Inuiguchi,
An integrated column generation and lagrangian relaxation for flowshop scheduling problems, Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics, (2009), 209-304.
doi: 10.1109/ICSMC.2009.5346159. |
[24] |
D. Ouelhadj, P. Cowling and S. Petrovic,
Utility and stability measures for agent-based dynamic scheduling of steel continuous casting, Journal of Scheduling, 12 (2009), 417-431.
doi: 10.1109/ROBOT.2003.1241592. |
[25] |
D. Ouelhadj and S. Petrovic,
A survey of dynamic scheduling in manufacturing systems, Journal of Scheduling, 12 (2009), 417-431.
doi: 10.1007/s10951-008-0090-8. |
[26] |
D. Ouelhadj, S. Petrovic, P. Cowling and A. Meisels,
Inter-agent cooperation and communication for agent-based robust dynamic scheduling in steel production, Advanced Engineering Informatics, 18 (2004), 161-172.
doi: 10.1016/j.aei.2004.10.003. |
[27] |
D. Pacciarelli and M. Pranzo,
Production scheduling in a steelmaking-continuous casting plant, Computers and Chemical Engineering, 28 (2004), 2823-2835.
doi: 10.1016/j.compchemeng.2004.08.031. |
[28] |
Q. Pan, L. Wang, K. Mao, J. Zhao and M. Zhang,
An Effective Artificial Bee Colony Algorithm for a Real-World Hybrid Flowshop Problem in Steelmaking Process, IEEE Transactions on Automation Science and Engineering, 10 (2013), 307-322.
doi: 10.1109/TASE.2012.2204874. |
[29] |
H. Sherali, G. Choi and C. Tuncbilek,
A Variable Target Value Method for Nondifferentiable Optimization, Operation Research Letters, 26 (2000), 1-8.
doi: 10.1016/S0167-6377(99)00063-2. |
[30] |
L. Sun, Research on the Optimal Scheduling Method for the productive Process of Steelmaking-Refining-Continuous Casting, Ph. D thesis, Northeastern University in Shenyang, 2015. Google Scholar |
[31] |
L. Tang, J. Liu, A. Rong and Z. Yang,
A review of planning and scheduling systems and methods for integrated steel production, European Journal of Operational Research, 133 (2001), 1-20.
doi: 10.1016/S0377-2217(00)00240-X. |
[32] |
L. Tang, P. Luh, J. Liu and L. Fang, Steelmaking process scheduling using Lagrangian relaxation, International Journal of Production Research, 40 (2002), 55-70. Google Scholar |
[33] |
L. Tang, G. Wang and Z. Chen,
Integrated charge batching and casting width selection at Baosteel, Operations Research, 62 (2014), 772-787.
doi: 10.1287/opre.2014.1278. |
[34] |
L. Tang, Y. Zhao and J. Liu,
An Improved Differential Evolution Algorithm for Practical Dynamic Scheduling in Steelmaking-continuous Casting Production, IEEE Transactions on Evolutionary Computation, 18 (2014), 209-213.
doi: 10.1109/TEVC.2013.2250977. |
[35] |
G. Vieira, J. Hermann and E. Lin,
Rescheduling manufacturing systems: a framework of strategies, policies and methods, Journal of Scheduling, 6 (2003), 36-92.
doi: 10.1023/A:1022235519958. |
[36] |
R. Xiong, Y. Fan and C. Wu, A dynamic job shop scheduling method based on Lagrangian relaxation, Tsinghua Science and Technology, 4 (1999), 1297-1302. Google Scholar |
[37] |
H. Xuan and L. Tang,
Scheduling a hybrid flowshop with batch production at the last stage, Computers and Operations Research, 34 (2007), 2718-2733.
doi: 10.1016/j.cor.2005.10.014. |
[38] |
S. Yu and Q. Pan,
A Rescheduling Method for Operation Time Delay Disturbance in Steelmaking and Continuous Casting Production Process, International Journal of Iron and Steel Research, 19 (2012), 33-41.
doi: 10.1016/S1006-706X(13)60029-1. |
[39] |
H. Zhong, X Dong and H. Shi, Research on the load balancing scheduling problem of reentrant hybrid flowshops, Chinese High Technology Letters, 25 (2015), 70-81. Google Scholar |
[40] |
H. Zhong, Y Zhu and S. Lin, A dynamic co-evolution compact genetic algorithm for E/T problem, The 17th IFAC Symposium on System Identification, (2015), 1433-1437. Google Scholar |



Cast vs. charge(SLLRA) | LB | UB | Gap (%) | Time (s) |
2 vs.5 | 937456 | 983672 | 4.70 | 299.5 |
2 vs.6 | 1182728 | 1398892 | 15.45 | 313.3 |
2 vs.7 | 1497377 | 1732913 | 13.59 | 327.8 |
2 vs.8 | 1837244 | 2252386 | 18.43 | 323.7 |
3 vs.5 | 1923113 | 2183725 | 11.93 | 309.4 |
3 vs.6 | 2487753 | 2711245 | 8.24 | 355.2 |
3 vs.7 | 3294573 | 3690245 | 10.72 | 377.2 |
3 vs.8 | 3999272 | 5294742 | 24.47 | 466.1 |
4 vs.5 | 3100023 | 3274848 | 5.34 | 378.5 |
4 vs.6 | 4134749 | 4591234 | 9.94 | 449.2 |
4 vs.7 | 5368271 | 7545422 | 28.85 | 598.4 |
4 vs.8 | 6650012 | 9082765 | 26.78 | 739.6 |
5 vs.5 | 4648823 | 5119374 | 9.19 | 504.7 |
5 vs.6 | 6168391 | 9998116 | 38.30 | 663.2 |
5 vs.7 | 8102927 | 11924753 | 32.05 | 2199.5 |
5 vs.8 | 10373752 | 13583721 | 23.63 | 7824 |
Average | 4106654 | 7532570 | 17.60 | 1008.08 |
Cast vs. charge(SLLRA) | LB | UB | Gap (%) | Time (s) |
2 vs.5 | 937456 | 983672 | 4.70 | 299.5 |
2 vs.6 | 1182728 | 1398892 | 15.45 | 313.3 |
2 vs.7 | 1497377 | 1732913 | 13.59 | 327.8 |
2 vs.8 | 1837244 | 2252386 | 18.43 | 323.7 |
3 vs.5 | 1923113 | 2183725 | 11.93 | 309.4 |
3 vs.6 | 2487753 | 2711245 | 8.24 | 355.2 |
3 vs.7 | 3294573 | 3690245 | 10.72 | 377.2 |
3 vs.8 | 3999272 | 5294742 | 24.47 | 466.1 |
4 vs.5 | 3100023 | 3274848 | 5.34 | 378.5 |
4 vs.6 | 4134749 | 4591234 | 9.94 | 449.2 |
4 vs.7 | 5368271 | 7545422 | 28.85 | 598.4 |
4 vs.8 | 6650012 | 9082765 | 26.78 | 739.6 |
5 vs.5 | 4648823 | 5119374 | 9.19 | 504.7 |
5 vs.6 | 6168391 | 9998116 | 38.30 | 663.2 |
5 vs.7 | 8102927 | 11924753 | 32.05 | 2199.5 |
5 vs.8 | 10373752 | 13583721 | 23.63 | 7824 |
Average | 4106654 | 7532570 | 17.60 | 1008.08 |
Cast vs. charge(DCSLA) | LB | UB | Gap (%) | Time (s) |
2 vs.5 | 937456 | 954151 | 1.75 | 1.6 |
2 vs.6 | 1182728 | 1294621 | 8.64 | 2.2 |
2 vs.7 | 1497377 | 1519847 | 1.48 | 3.2 |
2 vs.8 | 1837244 | 1997636 | 8.03 | 1.9 |
3 vs.5 | 1923113 | 2003743 | 4.02 | 2 |
3 vs.6 | 2487753 | 2505632 | 0.71 | 3.2 |
3 vs.7 | 3294573 | 3349425 | 1.64 | 1.5 |
3 vs.8 | 3999272 | 4186443 | 4.47 | 1.5 |
4 vs.5 | 3100023 | 3153846 | 1.71 | 2.1 |
4 vs.6 | 4134749 | 4200474 | 1.56 | 2.7 |
4 vs.7 | 5368271 | 5438362 | 1.29 | 3.8 |
4 vs.8 | 6650012 | 6739436 | 1.33 | 3.1 |
5 vs.5 | 4648823 | 4753628 | 2.20 | 2.4 |
5 vs.6 | 6168391 | 6374522 | 3.23 | 3.9 |
5 vs.7 | 8102927 | 9193736 | 11.86 | 5.9 |
5 vs.8 | 10373752 | 11376463 | 8.81 | 8.8 |
Average | 4106654 | 4315122 | 3.92 | 3.11 |
Cast vs. charge(DCSLA) | LB | UB | Gap (%) | Time (s) |
2 vs.5 | 937456 | 954151 | 1.75 | 1.6 |
2 vs.6 | 1182728 | 1294621 | 8.64 | 2.2 |
2 vs.7 | 1497377 | 1519847 | 1.48 | 3.2 |
2 vs.8 | 1837244 | 1997636 | 8.03 | 1.9 |
3 vs.5 | 1923113 | 2003743 | 4.02 | 2 |
3 vs.6 | 2487753 | 2505632 | 0.71 | 3.2 |
3 vs.7 | 3294573 | 3349425 | 1.64 | 1.5 |
3 vs.8 | 3999272 | 4186443 | 4.47 | 1.5 |
4 vs.5 | 3100023 | 3153846 | 1.71 | 2.1 |
4 vs.6 | 4134749 | 4200474 | 1.56 | 2.7 |
4 vs.7 | 5368271 | 5438362 | 1.29 | 3.8 |
4 vs.8 | 6650012 | 6739436 | 1.33 | 3.1 |
5 vs.5 | 4648823 | 4753628 | 2.20 | 2.4 |
5 vs.6 | 6168391 | 6374522 | 3.23 | 3.9 |
5 vs.7 | 8102927 | 9193736 | 11.86 | 5.9 |
5 vs.8 | 10373752 | 11376463 | 8.81 | 8.8 |
Average | 4106654 | 4315122 | 3.92 | 3.11 |
ET | Events | EV-1 (s) | EV-2 (s) | EV-3 (min) | DG (%) | Time (s) | IN |
1 | R1-T2-M1 | 0 | 0 | 22 | 12.94 | 76.63 | 162 |
2 | R1-T3-M1 | 0 | 0 | 15 | 11.85 | 66.31 | 115 |
3 | R1-T1-M2 | 0 | 0 | 16 | 13.64 | 75.92 | 141 |
4 | R1-T2-M2 | 0 | 0 | 19 | 13.09 | 58.7 | 128 |
5 | R1-T3-M2 | 0 | 0 | 21 | 11.55 | 44.76 | 103 |
6 | R1-T1-M3 | 0 | 0 | 18 | 8.68 | 121.91 | 196 |
7 | R1-T2-M3 | 0 | 0 | 11 | 12.97 | 134.73 | 187 |
8 | R1-T3-M3 | 0 | 0 | 10 | 11.32 | 83.66 | 165 |
9 | R2-T2-M1 | 0 | 0 | 19 | 7.74 | 9.32 | 63 |
10 | R2-T3-M1 | 0 | 0 | 12 | 8.31 | 8.69 | 60 |
11 | R2-T1-M2 | 0 | 0 | 16 | 9.22 | 12.81 | 52 |
12 | R2-T2-M2 | 0 | 0 | 19 | 7.93 | 9.33 | 61 |
13 | R2-T3-M2 | 0 | 0 | 22 | 8.88 | 8.89 | 68 |
14 | R2-T1-M3 | 0 | 0 | 14 | 9.12 | 15.9 | 59 |
15 | R2-T2-M3 | 0 | 0 | 16 | 8.45 | 7.63 | 42 |
16 | R2-T3-M3 | 0 | 0 | 11 | 8.67 | 8.69 | 74 |
Average | 0 | 0 | 16.31 | 10.27 | 46.49 | 104 | |
(ET: Event Type, IN: Number of Iterations, DG: Duality Gap, EV: Evaluation Values) |
ET | Events | EV-1 (s) | EV-2 (s) | EV-3 (min) | DG (%) | Time (s) | IN |
1 | R1-T2-M1 | 0 | 0 | 22 | 12.94 | 76.63 | 162 |
2 | R1-T3-M1 | 0 | 0 | 15 | 11.85 | 66.31 | 115 |
3 | R1-T1-M2 | 0 | 0 | 16 | 13.64 | 75.92 | 141 |
4 | R1-T2-M2 | 0 | 0 | 19 | 13.09 | 58.7 | 128 |
5 | R1-T3-M2 | 0 | 0 | 21 | 11.55 | 44.76 | 103 |
6 | R1-T1-M3 | 0 | 0 | 18 | 8.68 | 121.91 | 196 |
7 | R1-T2-M3 | 0 | 0 | 11 | 12.97 | 134.73 | 187 |
8 | R1-T3-M3 | 0 | 0 | 10 | 11.32 | 83.66 | 165 |
9 | R2-T2-M1 | 0 | 0 | 19 | 7.74 | 9.32 | 63 |
10 | R2-T3-M1 | 0 | 0 | 12 | 8.31 | 8.69 | 60 |
11 | R2-T1-M2 | 0 | 0 | 16 | 9.22 | 12.81 | 52 |
12 | R2-T2-M2 | 0 | 0 | 19 | 7.93 | 9.33 | 61 |
13 | R2-T3-M2 | 0 | 0 | 22 | 8.88 | 8.89 | 68 |
14 | R2-T1-M3 | 0 | 0 | 14 | 9.12 | 15.9 | 59 |
15 | R2-T2-M3 | 0 | 0 | 16 | 8.45 | 7.63 | 42 |
16 | R2-T3-M3 | 0 | 0 | 11 | 8.67 | 8.69 | 74 |
Average | 0 | 0 | 16.31 | 10.27 | 46.49 | 104 | |
(ET: Event Type, IN: Number of Iterations, DG: Duality Gap, EV: Evaluation Values) |
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