Abstract
There has been extensive scheduling research relating to use of existing dispatching rules along with/without new dispatching rules and compared their performance behavior in job-shop, flow-shop, open-shop, flexible manufacturing system, and single machine with unit capacity environments using various scheduling objectives. However, it appears that there is no comparative study on analysis of dispatching rules for scheduling bottleneck batch processing machine in discrete parts manufacturing, particularly the diffusion furnace (DF) in semiconductor manufacturing (SM). This study addresses this research issue. For that, this study first, proposes the mathematical models for dynamic scheduling (DS) of DF to optimize the due-date based scheduling objectives: Total weighted tardiness, on-time delivery rate, total earliness/lateness, and maximum lateness. Due to the computational intractability of each the proposed mathematical models for large-scale problem, this study proposes greedy heuristic algorithm (GHA) based on due-date based dispatching rules (DDR). Because, dispatching rules are widely used in the SM industry. Accordingly, in this study twenty variants of GHA-DDR are proposed by considering various due-date based dispatching rules to compare the effects of due-date based dispatching rules in DS of DF. From the series of computational analysis carried out in this study, it is observed empirically that the proposed variants of GHA based on apparent tardiness cost (ATC) and batch ATC (BATC) dispatching rules yield consistently better solution for most of the scheduling objectives considered in this study. This observation is further verified by statistical analysis: Friedman test and Nemenyi multiple comparison test.
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References
Balasubramanian, H., Monch, L., Fowler, J., Pfund, M.: Genetic algorithm based scheduling of parallel batch machines with incompatible job families to minimize total weighted tardiness. Int. J. Prod. Res. 42(8), 1621–1638 (2004)
Bilyk, A., Mönch, L., Almeder, C.: Scheduling jobs with ready times and precedence constraints on parallel batch machines using meta heuristics. Comput. Ind. Eng. 78, 175–185 (2014)
Chan, F.T.S., Au, K.C., Chan, L.Y., Lau, T.L., Choy, K.L.: A genetic algorithm approach to machine flexibility problems in an ion plating cell. The International Journal of Advanced Manufacturing Technology. 31, 1127–1134 (2007)
Chen, J.C., Chen, T.-L., Pratama, B.R., Tu, Q.-F.: Capacity planning in thin film transistor—liquid crystal display cell process. Journal of Manufacturing Systems. 39, 63–78 (2016)
Cheng, H.C., Chiang, T.C., Fu, L.C.: A memetic algorithm for parallel batch machine scheduling with incompatible job families and dynamic job arrivals. In: IEEE International Conference on Systems, Man and Cybernetics Proceedings, pp. 541–546 (2008)
Chiang, T.-C., Cheng, H.-C., Fu, L.-C.: A memetic algorithm for minimizing total weighted tardiness on parallel batch machines with incompatible job families and dynamic job arrival. Comput. Oper. Res. 37(12), 2257–2269 (2010)
Chiang, T.-C., Cheng, H,-C., Fu, L.-C.: An efficient heuristic for minimizing maximum lateness on parallel batch machines. In: Eighth International Conference on Intelligent Systems Design and Applications, pp. 621–627 (2008)
Choi, Y.-C.: Dispatching rule-based scheduling algorithms in a single machine with sequence-dependent setup times and energy requirements. Procedia CIRP 41, 135–140 (2016)
Damodaran, P., Vélez-Gallego, M.C.: A simulated annealing algorithm to minimize makespan of parallel batch processing machines with unequal job ready times. Expert Syst. Appl. 39(1), 1451–1458 (2012)
Dirk, R., Monch, L.: Multiobjective scheduling of jobs with incompatible families on parallel batch machines. Lect. Notes Comput. Sci. 3906, 209–221 (2006)
Fachini, R.F., Esposto, K.F., Camargo, V.C.B.: Glass container production planning with warm-ups and furnace extraction variation losses. The International Journal of Advanced Manufacturing Technology. (2016). https://doi.org/10.1007/s00170-016-9369-7
Fanti, M.P., Maione, B., Piscitelli, G., Turchiano, B.: Heuristic scheduling of jobs on a multi-product batch-processing machine. Int. J. Prod. Res. 34(8), 2163–2186 (1996)
Farhad, G.T., Laya, O.: Development of a set of algorithms for the multi-project scheduling problems. Journal of Industrial and Systems Engineering. 1, 23–36 (2007)
Farhad, G.T., Laya, O.: Heuristic rules for tardiness problem in flow shop with intermediate due dates. Int. J. Adv. Manuf. Technol. 71, 381–393 (2014)
Geiger, C.D., Uzsoy, R., Aytu, G.H.: Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J. Sched. 9, 7–34 (2006)
Gong, H., Zhang, B., Peng, W.: Scheduling and common due date assignment on a single parallel-batching machine with batch delivery. Discrete Dynamics in Nature and Society. 1, 1–7 (2015)
Graham, R.L., Lawler, E.L., Lenstra, J.K., Rinnooy, K.A.H.G.: Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discret. Math. 5, 287–326 (1979)
Guo, C., Jiang, Z., Hu, H.: A hybrid ant colony optimization method for scheduling batch processing machine in the semiconductor manufacturing. In: Proceedings of the 2010 IEEE IEEM, pp. 1698–1701 (2010)
Hildebrandt, T., Heger, J., Scholz-Reiter, B.: Towards improved dispatching rules for complex shop floor scenarios—a genetic programming approach. In: Proceeding of the GECCO’10, July 7–11, Portland, OR, USA (2010). http://www.genetic-programming.org/hc2010/6-Hildebrandt/Hildebrandt-Paper.pdf. Accessed 28 Jan 2017
Horng, H.-C.: Comparing steady-state performance of dispatching rule-pairs in open shops. International Journal of Applied Science and Engineering. 4(3), 259–273 (2006)
Jayamohan, M.S., Rajendran, C.: New dispatching rules for shop scheduling: a step forward. Int. J. Prod. Res. 38(3), 563–586 (2000)
Jia, W., Jiang, Z.: A job-family-oriented algorithm for re-entrant batch processing machine scheduling. In: IEEE International Conference on Automation Science and Engineering, pp. 1022–1027 (2013)
Jia, Z., Wang, C., Leung, J.Y.-T.: An ACO algorithm for makespan minimization in parallel batch machines with non-identical job sizes and incompatible job families. Appl. Soft Comput. 38(10), 395–404 (2016)
Jung, C., Pabst, D., Ham, M., Stehli, M., Rothe, M.: An effective problem decomposition method for scheduling of diffusion processes based on mixed integer linear programming. IEEE Trans. Semicond. Manuf. 27(3), 357–363 (2014)
Kim, Y.-D., Joo, B.-J., Choi, S.-Y.: Scheduling wafer lots on diffusion machines in a semiconductor wafer fabrication facility. IEEE Trans. Semicond. Manuf. 23(2), 246–254 (2010)
Koh, S.-G., Koo, P.-H., Kim, D.-C., Hur, W.-S.: Scheduling a single batch processing machine with arbitrary job sizes and incompatible job families. Int. J. Prod. Econ. 98(1), 81–96 (2005)
Krishnan, M., Chinnusamy, T.R., Karthikeyan, T.: Performance study of flexible manufacturing system scheduling using dispatching rules in dynamic environment. Procedia Engineering. 38, 2793–2798 (2012)
Kurz, M.E., Mason, S.J.: Minimizing total weighted tardiness on a batch processing machine with incompatible job families and job ready times. Int. J. Prod. Res. 46(1), 131–151 (2008)
Li, L., Qiao, F.: ACO-based scheduling for a single batch processing machine in semiconductor manufacturing. In: 4th IEEE International Conference on Automation Science and Engineering, pp. 85–90 (2008)
Li, L., Pan, G., Fei, Q.: ACO-Based Multi-objective Scheduling of Identical Parallel Batch Processing Machines in Semiconductor Manufacturing. INTECH Open Access Publisher, Rejika (2010)
Li, L., Qiao, F., Wu, Q.D.: ACO-based scheduling of parallel batch processing machines to minimize the total weighted tardiness. In: 5th Annual IEEE Conference on Automation Science and Engineering, pp. 280–285 (2009)
Malapert, A., Guéret, C., Rousseau, L.-M.: A constraint programming approach for a batch processing problem with non-identical job sizes. Eur. J. Oper. Res. 221(3), 533–545 (2012)
Malve, S., Uzsoy, R.: A genetic algorithm for minimizing maximum lateness on parallel identical batch processing machines with dynamic job arrivals and incompatible job families. Comput. Oper. Res. 34(10), 3016–3028 (2007)
Manfred, M., Alexander, K.S.: Comparison of dispatching rules for semiconductor manufacturing using large facility models. In: Proceedings of the I999 Winter Simulation Conference, pp. 709–713 (1999)
Mathirajan, M., Sivakumar, A.I.: A literature review, classification and simple meta-analysis on scheduling of batch processors in semiconductor. Int. J. Adv. Manuf. Technol. 29, 990–1001 (2006)
Mathirajan, M., Gokhale, R., Ramasubramaniam, M.: Modeling of scheduling batch processor in discrete parts manufacturing. In: Ramanathan, U., Ramanathan, R. (eds.) Supply Chain Strategies, Issues and Models, pp. 153–192. Springer, London (2014)
Mehta, S.V., Uzsoy, R.: Minimizing total tardiness on a batch processing machine with incompatible job families. IIE Trans. 30, 165–178 (1998)
Monch, L., Balasubramanian, H., Fowler, J.W., Pfund, M.E.: Heuristic scheduling of jobs on parallel batch machines with incompatible job families and unequal ready times. Comput. Oper. Res. 32(11), 2731–2750 (2005)
Monch, L., Fowler, J.W., Mason, S.J.: Production Planning and Control for Semiconductor Wafer Fabrication Facilities: Modeling, Analysis, and Systems. Springer, New York (2013)
Monch, L., Zimmermann, J., Otto, P.: Machine learning techniques for scheduling jobs with incompatible families and unequal ready times on parallel batch machine. Eng. Appl. Artif. Intell. 19, 235–245 (2006)
Morton, T.E., Rachamadugu, R.V.: Myopic Heuristics for the Single Machine Weighted Tardiness Problem. The Robotics Institute, Carnegie-Mellon University, Pittsburgh, PA (1983)
Oulamara, A.: Makespan minimization in a no-wait flow shop problem with two batching machines. Comput. Oper. Res. 34(4), 1033–1050 (2007)
Ozturk, O., Espinouse, M.L, Mascolo, D., Gouin, A.: Optimizing the makespan of washing operations of medical devices in hospital sterilization services. In: IEEE Workshop on Health Care Management, pp. 1–6 (2010)
Panwalkar, S.S., Iskander, W.: A survey of scheduling rules. Oper. Res. 25(1), 45–61 (1977)
Pinedo, M.: Scheduling: Theory, Algorithms, and Systems, 3rd edn. Prentice Hall, Upper Saddle River (2008)
Rardin, R.L., Uzsoy, R.: Experimental evaluation of heuristic optimization algorithms: a tutorial. J. Heuristics 7(3), 261–304 (2001)
Ravindra, G., Mathirajan, M.: Minimizing total weighted tardiness on heterogeneous batch processors with incompatible job families. The International Journal of Advanced Manufacturing Technology. 70, 1563–1578 (2014)
Sarin, S.C., Varadarajan, A., Wang, L.: A survey of dispatching rules for operational control in wafer fabrication. Production Planning and Control. 22(1), 4–24 (2011)
Settouti, N., Bechar, M.E.A., Chikh, M.A.: Statistical comparisons of the top 10 algorithms in data mining for classification task. International Journal of Interactive Multimedia and Artificial Intelligence. 4(1), 46–51 (2016)
Sha, D.Y., Hsu, S.-Y., Lai, X.D.: Design of due-date oriented look-ahead batching rule in wafer fabrication. Int. J. Adv. Manuf. Technol. 35, 596–609 (2007)
Silva, E.B.D., Costa, M.G., Silva, M.F.D.S.D., Pereira, F.H.: Simulation study of dispatching rules in stochastic job shop dynamic scheduling. World Journal of Modelling and Simulation 10(3), 231–240 (2014)
Uzsoy, R.: Scheduling batch processing machine with incompatible job families. Int. J. Prod. Res. 33(10), 2685–2708 (1995)
Vepsalainen, A.P.J., Morton, T.E.: Priority rules for job shops with weighted tardiness costs. Manage. Sci. 33(8), 1035–1047 (1987)
Vimala Rani, M., Mathirajan, M.: Dynamic scheduling of diffusion furnace in semiconductor manufacturing with real time events. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 104–108. https://doi.org/10.1109/ieem.2015.7385617 (2015)
Vimala Rani, M., Mathirajan, M.: Multi objective dynamic real-time scheduling of batch processing machine. International Journal of Operations and Quantitative Management. 22(1), 53–73 (2016)
Vimala Rani, M., Mathirajan, M.: Performance evaluation of ATC based greedy heuristic algorithms in scheduling diffusion furnace in wafer fabrication. Journal of Information and Optimization Sciences. 37(5), 717–762 (2016)
Yaghubian, A.R., Hodgson, T.J., Joines, J.A.: Dry-or-buy decision support for dry kiln scheduling in furniture production. IIE Trans. 33(2), 131–136 (2001)
Zhang, R., Chang, P.-C., Song, S., Wu, C.: A multi-objective artificial bee colony algorithm for parallel batch-processing machine scheduling in fabric dyeing processes. Knowl.-Based Syst. 116(7), 114–129 (2017)
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Appendices
Appendix 1: A LINGO set code for the proposed mathematical model for DS-DF to minimize TWT
Appendix 2: Performance analysis of each of the twenty proposed variants of GHA-DDR in comparison with optimal TWT based on Relative Percentage Deviation (RPD)
Problem instances | Optimal TWT | RPD of each of the proposed variants of GHA-DDR in comparison with optimal TWT | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
GHA-DDR1 | GHA-DDR2 | GHA-DDR3 | GHA-DDR4 | GHA-DDR5 | GHA-DDR6 | GHA-DDR7 | GHA-DDR8 | GHA-DDR9 | GHA-DDR10 | ||
1 | 111 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 68.47 | 68.47 | 0.00 |
2 | 111 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 68.47 | 68.47 | 0.00 |
3 | 222 | 16.22 | 16.22 | 16.22 | 16.22 | 16.22 | 16.22 | 16.22 | 0.00 | 0.00 | 16.22 |
4 | 311 | 3.54 | 3.54 | 3.54 | 3.54 | 3.54 | 3.54 | 3.54 | 43.41 | 43.41 | 3.54 |
5 | 461 | 34.06 | 49.89 | 49.89 | 49.89 | 34.06 | 34.06 | 27.55 | 29.28 | 32.75 | 34.06 |
6 | 112 | 308.93 | 308.93 | 308.93 | 308.93 | 308.93 | 308.93 | 308.93 | 308.93 | 308.93 | 308.93 |
7 | 247 | 78.14 | 78.14 | 78.14 | 78.14 | 78.14 | 78.14 | 78.14 | 47.37 | 47.37 | 78.14 |
8 | 325 | 18.77 | 18.77 | 18.77 | 18.77 | 18.77 | 18.77 | 18.77 | 11.08 | 11.08 | 18.77 |
9 | 200 | 11.00 | 28.50 | 28.50 | 28.50 | 11.00 | 11.00 | 63.50 | 11.00 | 11.00 | 11.00 |
10 | 182 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 |
11 | 120 | 83.33 | 83.33 | 83.33 | 83.33 | 83.33 | 83.33 | 83.33 | 83.33 | 83.33 | 83.33 |
12 | 429 | 77.62 | 77.62 | 77.62 | 77.62 | 77.62 | 77.62 | 77.16 | 80.19 | 80.19 | 77.62 |
13 | 76 | 125.00 | 125.00 | 125.00 | 125.00 | 125.00 | 125.00 | 125.00 | 92.11 | 92.11 | 125.00 |
14 | 56 | 225.00 | 225.00 | 225.00 | 225.00 | 225.00 | 225.00 | 225.00 | 225.00 | 225.00 | 225.00 |
15 | 120 | 91.67 | 91.67 | 91.67 | 91.67 | 91.67 | 91.67 | 91.67 | 91.67 | 91.67 | 91.67 |
Problem instances | RPD of each of the proposed variants of GHA-DDR in comparison with optimal TWT | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
GHA-DDR11 | GHA-DDR12 | GHA-DDR13 | GHA-DDR14 | GHA-DDR15 | GHA-DDR16 | GHA-DDR17 | GHA-DDR18 | GHA-DDR19 | GHA-DDR20 | |
1 | 0.00 | 0.00 | 0.00 | 8.11 | 9.91 | 9.91 | 9.91 | 9.91 | 9.91 | 109.01 |
2 | 0.00 | 0.00 | 0.00 | 236.94 | 9.91 | 15.32 | 15.32 | 15.32 | 15.32 | 15.32 |
3 | 16.22 | 16.22 | 16.22 | 5.86 | 8.11 | 10.81 | 10.81 | 10.81 | 10.81 | 27.03 |
4 | 3.54 | 3.54 | 3.54 | 47.59 | 5.79 | 13.50 | 13.50 | 13.50 | 13.50 | 31.83 |
5 | 34.06 | 34.06 | 34.06 | 34.06 | 36.23 | 36.23 | 36.23 | 36.23 | 36.23 | 48.59 |
6 | 308.93 | 308.93 | 308.93 | 192.86 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 30.36 |
7 | 78.14 | 78.14 | 78.14 | 47.37 | 23.08 | 23.08 | 23.08 | 23.08 | 23.08 | 23.08 |
8 | 18.77 | 18.77 | 18.77 | 4.92 | 7.69 | 7.69 | 7.69 | 7.69 | 7.69 | 7.69 |
9 | 11.00 | 11.00 | 11.00 | 319.00 | 8.00 | 1.50 | 8.00 | 1.50 | 1.50 | 44.50 |
10 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 | 6.59 | 0.00 |
11 | 83.33 | 83.33 | 83.33 | 335.00 | 73.33 | 73.33 | 75.00 | 73.33 | 73.33 | 127.50 |
12 | 77.62 | 77.62 | 77.62 | 23.54 | 23.54 | 23.54 | 23.54 | 23.54 | 23.54 | 23.54 |
13 | 125.00 | 125.00 | 125.00 | 496.05 | 15.79 | 15.79 | 15.79 | 15.79 | 15.79 | 15.79 |
14 | 225.00 | 225.00 | 225.00 | 271.43 | 271.43 | 271.43 | 271.43 | 271.43 | 271.43 | 271.43 |
15 | 91.67 | 91.67 | 91.67 | 23.33 | 23.33 | 23.33 | 23.33 | 23.33 | 23.33 | 23.33 |
Appendix 3: Performance analysis of each of the twenty proposed variants of GHA-DDR in comparison with optimal OTD rate based on RPD
Problem instances | Optimal OTD rate | RPD of each of the proposed variants of GHA-DDR in comparison with optimal OTD rate | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
GHA-DDR1 | GHA-DDR2 | GHA-DDR3 | GHA-DDR4 | GHA-DDR5 | GHA-DDR6 | GHA-DDR7 | GHA-DDR8 | GHA-DDR9 | GHA-DDR10 | ||
1 | 0.667 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
2 | 0.714 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
3 | 0.625 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
4 | 0.670 | 17.08 | 17.08 | 17.08 | 17.08 | 17.08 | 17.08 | 17.08 | 17.08 | 17.08 | 17.08 |
5 | 0.600 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 33.33 | 16.67 |
6 | 0.700 | 71.43 | 71.43 | 71.43 | 71.43 | 71.43 | 71.43 | 71.43 | 85.71 | 85.71 | 71.43 |
7 | 0.700 | 42.86 | 42.86 | 42.86 | 42.86 | 42.86 | 42.86 | 42.86 | 28.57 | 28.57 | 42.86 |
8 | 0.700 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 14.29 | 14.29 | 28.57 |
9 | 0.900 | 44.44 | 44.44 | 44.44 | 44.44 | 44.44 | 44.44 | 44.44 | 44.44 | 44.44 | 44.44 |
10 | 0.800 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 |
11 | 0.800 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 |
12 | 0.600 | 66.67 | 66.67 | 66.67 | 66.67 | 66.67 | 66.67 | 50.00 | 50.00 | 50.00 | 66.67 |
13 | 0.800 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 37.50 | 37.50 | 25.00 |
14 | 0.700 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 |
15 | 0.800 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 |
Problem instances | RPD of each of the proposed variants of GHA-DDR in comparison with optimal OTD rate | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
GHA-DDR11 | GHA-DDR12 | GHA-DDR13 | GHA-DDR14 | GHA-DDR15 | GHA-DDR16 | GHA-DDR17 | GHA-DDR18 | GHA-DDR19 | GHA-DDR20 | |
1 | 0.00 | 0.00 | 0.00 | 25.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 25.00 |
2 | 0.00 | 0.00 | 0.00 | 60.00 | 0.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 |
3 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 |
4 | 17.08 | 17.08 | 17.08 | 17.08 | 17.08 | 33.67 | 33.67 | 33.67 | 33.67 | 17.08 |
5 | 16.67 | 16.67 | 16.67 | 16.67 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 33.33 |
6 | 71.43 | 71.43 | 71.43 | 71.43 | 42.86 | 42.86 | 42.86 | 42.86 | 42.86 | 28.57 |
7 | 42.86 | 42.86 | 42.86 | 28.57 | 42.86 | 42.86 | 42.86 | 42.86 | 42.86 | 42.86 |
8 | 28.57 | 28.57 | 28.57 | 42.86 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 |
9 | 44.44 | 44.44 | 44.44 | 66.67 | 33.33 | 11.11 | 33.33 | 11.11 | 11.11 | 44.44 |
10 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 0.00 |
11 | 25.00 | 25.00 | 25.00 | 12.50 | 25.00 | 25.00 | 25.00 | 25.00 | 25.00 | 37.50 |
12 | 66.67 | 66.67 | 66.67 | 33.33 | 33.33 | 33.33 | 33.33 | 33.33 | 33.33 | 33.33 |
13 | 25.00 | 25.00 | 25.00 | 37.50 | 37.50 | 37.50 | 37.50 | 37.50 | 37.50 | 37.50 |
14 | 28.57 | 28.57 | 28.57 | 42.86 | 42.86 | 42.86 | 42.86 | 42.86 | 42.86 | 42.86 |
15 | 25.00 | 25.00 | 25.00 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 |
Appendix 4 Performance analysis of each of the twenty proposed variants of GHA-DDR in comparison with optimal TE/L based on RPD
Problem instances | Optimal TE/L | RPD of each of the proposed variants of GHA-DDR in comparison with optimal TE/L | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
GHA-DDR1 | GHA-DDR2 | GHA-DDR3 | GHA-DDR4 | GHA-DDR5 | GHA-DDR6 | GHA-DDR7 | GHA-DDR8 | GHA-DDR9 | GHA-DDR10 | ||
1 | 40 | 55.00 | 55.00 | 55.00 | 55.00 | 55.00 | 55.00 | 55.00 | 65.00 | 65.00 | 55.00 |
2 | 60 | 30.00 | 30.00 | 30.00 | 30.00 | 30.00 | 30.00 | 30.00 | 36.67 | 36.67 | 30.00 |
3 | 71 | 39.44 | 39.44 | 39.44 | 39.44 | 39.44 | 39.44 | 39.44 | 22.54 | 22.54 | 39.44 |
4 | 91 | 17.58 | 17.58 | 17.58 | 17.58 | 17.58 | 17.58 | 17.58 | 26.37 | 26.37 | 17.58 |
5 | 135 | 11.85 | 22.22 | 22.22 | 22.22 | 11.85 | 11.85 | 25.19 | 22.22 | 17.78 | 11.85 |
6 | 102 | 41.18 | 41.18 | 41.18 | 41.18 | 41.18 | 41.18 | 41.18 | 66.67 | 66.67 | 41.18 |
7 | 73 | 106.85 | 106.85 | 106.85 | 106.85 | 106.85 | 106.85 | 106.85 | 52.05 | 52.05 | 106.85 |
8 | 80 | 80.00 | 80.00 | 80.00 | 80.00 | 80.00 | 80.00 | 80.00 | 87.50 | 87.50 | 80.00 |
9 | 54 | 148.15 | 166.67 | 166.67 | 166.67 | 148.15 | 148.15 | 248.15 | 148.15 | 148.15 | 148.15 |
10 | 59 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 |
11 | 68 | 185.29 | 185.29 | 185.29 | 185.29 | 185.29 | 185.29 | 185.29 | 185.29 | 185.29 | 185.29 |
12 | 116 | 51.72 | 51.72 | 51.72 | 51.72 | 51.72 | 51.72 | 87.93 | 101.72 | 101.72 | 51.72 |
13 | 84 | 66.67 | 66.67 | 66.67 | 66.67 | 66.67 | 66.67 | 66.67 | 78.57 | 78.57 | 66.67 |
14 | 52 | 151.92 | 151.92 | 151.92 | 151.92 | 151.92 | 151.92 | 151.92 | 151.92 | 151.92 | 151.92 |
15 | 37 | 297.30 | 297.30 | 297.30 | 297.30 | 297.30 | 297.30 | 297.30 | 297.30 | 297.30 | 297.30 |
Problem instances | RPD of each of the proposed variants of GHA-DDR in comparison with optimal TE/L | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
GHA-DDR11 | GHA-DDR12 | GHA-DDR13 | GHA-DDR14 | GHA-DDR15 | GHA-DDR16 | GHA-DDR17 | GHA-DDR18 | GHA-DDR19 | GHA-DDR20 | |
1 | 55.00 | 55.00 | 55.00 | 20.00 | 50.00 | 50.00 | 50.00 | 50.00 | 50.00 | 100.00 |
2 | 30.00 | 30.00 | 30.00 | 33.33 | 25.00 | 21.67 | 21.67 | 21.67 | 21.67 | 21.67 |
3 | 39.44 | 39.44 | 39.44 | 16.90 | 19.72 | 16.90 | 16.90 | 16.90 | 16.90 | 33.80 |
4 | 17.58 | 17.58 | 17.58 | 21.98 | 16.48 | 20.88 | 20.88 | 20.88 | 20.88 | 47.25 |
5 | 11.85 | 11.85 | 11.85 | 11.85 | 4.44 | 4.44 | 4.44 | 4.44 | 4.44 | 22.22 |
6 | 41.18 | 41.18 | 41.18 | 7.84 | 21.57 | 21.57 | 21.57 | 21.57 | 21.57 | 41.18 |
7 | 106.85 | 106.85 | 106.85 | 52.05 | 63.01 | 63.01 | 63.01 | 63.01 | 63.01 | 63.01 |
8 | 80.00 | 80.00 | 80.00 | 0.00 | 62.50 | 62.50 | 62.50 | 62.50 | 62.50 | 62.50 |
9 | 148.15 | 148.15 | 148.15 | 118.52 | 122.22 | 159.26 | 122.22 | 159.26 | 159.26 | 111.11 |
10 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 | 57.63 | 84.75 |
11 | 185.29 | 185.29 | 185.29 | 61.76 | 79.41 | 79.41 | 138.24 | 79.41 | 79.41 | 111.76 |
12 | 51.72 | 51.72 | 51.72 | 24.14 | 24.14 | 24.14 | 24.14 | 24.14 | 24.14 | 24.14 |
13 | 66.67 | 66.67 | 66.67 | 59.52 | 57.14 | 57.14 | 57.14 | 57.14 | 57.14 | 57.14 |
14 | 151.92 | 151.92 | 151.92 | 75.00 | 75.00 | 75.00 | 75.00 | 75.00 | 75.00 | 75.00 |
15 | 297.30 | 297.30 | 297.30 | 21.62 | 21.62 | 21.62 | 21.62 | 21.62 | 21.62 | 21.62 |
Appendix 5: Performance analysis of each of the twenty proposed variants of GHA-DDR in comparison with optimal Lmax based on RPD
Problem instances | Optimal Lmax | RPD of each of the proposed variants of GHA-DDR in comparison with optimal Lmax | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
GHA-DDR1 | GHA-DDR2 | GHA-DDR3 | GHA-DDR4 | GHA-DDR5 | GHA-DDR6 | GHA-DDR7 | GHA-DDR8 | GHA-DDR9 | GHA-DDR10 | ||
1 | 21 | 23.81 | 23.81 | 23.81 | 23.81 | 23.81 | 23.81 | 23.81 | 0.00 | 0.00 | 23.81 |
2 | 21 | 23.81 | 23.81 | 23.81 | 23.81 | 23.81 | 23.81 | 23.81 | 0.00 | 0.00 | 23.81 |
3 | 21 | 23.81 | 23.81 | 23.81 | 23.81 | 23.81 | 23.81 | 23.81 | 0.00 | 0.00 | 23.81 |
4 | 26 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 7.69 | 7.69 | 0.00 |
5 | 37 | 13.51 | 13.51 | 13.51 | 13.51 | 13.51 | 13.51 | 13.51 | 35.14 | 0.00 | 13.51 |
6 | 20 | 30.00 | 30.00 | 30.00 | 30.00 | 30.00 | 30.00 | 30.00 | 105.00 | 105.00 | 30.00 |
7 | 24 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 |
8 | 25 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 56.00 | 56.00 | 20.00 |
9 | 14 | 7.14 | 7.14 | 7.14 | 7.14 | 7.14 | 7.14 | 42.86 | 7.14 | 7.14 | 7.14 |
10 | 16 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 |
11 | 13 | 23.08 | 23.08 | 23.08 | 23.08 | 23.08 | 23.08 | 23.08 | 23.08 | 23.08 | 23.08 |
12 | 30 | 30.00 | 30.00 | 30.00 | 30.00 | 30.00 | 30.00 | 73.33 | 83.33 | 83.33 | 30.00 |
13 | 8 | 62.50 | 62.50 | 62.50 | 62.50 | 62.50 | 62.50 | 62.50 | 187.50 | 187.50 | 62.50 |
14 | 15 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 |
15 | 9 | 22.22 | 22.22 | 22.22 | 22.22 | 22.22 | 22.22 | 22.22 | 22.22 | 22.22 | 22.22 |
Problem instances | RPD of each of the proposed variants of GHA-DDR in comparison with optimal Lmax | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
GHA-DDR11 | GHA-DDR12 | GHA-DDR13 | GHA-DDR14 | GHA-DDR15 | GHA-DDR16 | GHA-DDR17 | GHA-DDR18 | GHA-DDR19 | GHA-DDR20 | |
1 | 23.81 | 23.81 | 23.81 | 23.81 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 71.43 |
2 | 23.81 | 23.81 | 23.81 | 0.00 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 | 28.57 |
3 | 23.81 | 23.81 | 23.81 | 0.00 | 4.76 | 4.76 | 4.76 | 4.76 | 4.76 | 28.57 |
4 | 0.00 | 0.00 | 0.00 | 7.69 | 3.85 | 3.85 | 3.85 | 3.85 | 3.85 | 46.15 |
5 | 13.51 | 13.51 | 13.51 | 13.51 | 2.70 | 2.70 | 2.70 | 2.70 | 2.70 | 2.70 |
6 | 30.00 | 30.00 | 30.00 | 15.00 | 80.00 | 80.00 | 80.00 | 80.00 | 80.00 | 80.00 |
7 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 | 16.67 |
8 | 20.00 | 20.00 | 20.00 | 20.00 | 60.00 | 60.00 | 60.00 | 60.00 | 60.00 | 60.00 |
9 | 7.14 | 7.14 | 7.14 | 271.43 | 7.14 | 50.00 | 7.14 | 50.00 | 50.00 | 157.14 |
10 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 6.25 | 68.75 |
11 | 23.08 | 23.08 | 23.08 | 130.77 | 15.38 | 15.38 | 53.85 | 15.38 | 15.38 | 38.46 |
12 | 30.00 | 30.00 | 30.00 | 36.67 | 36.67 | 36.67 | 36.67 | 36.67 | 36.67 | 36.67 |
13 | 62.50 | 62.50 | 62.50 | 400.00 | 150.00 | 150.00 | 150.00 | 150.00 | 150.00 | 150.00 |
14 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 | 20.00 |
15 | 22.22 | 22.22 | 22.22 | 11.11 | 11.11 | 11.11 | 11.11 | 11.11 | 11.11 | 11.11 |
Appendix 6: Performance analysis of each of the twenty proposed variants of GHA-DDR in comparison with estimated optimal TWT based on ARPD score
S. No. | Problem configuration | ARPD score of each of the proposed variants of GHA-DDR in comparison with estimated optimal TWT | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
GHA-DDR1 | GHA-DDR2 | GHA-DDR3 | GHA-DDR4 | GHA-DDR5 | GHA-DDR6 | GHA-DDR7 | GHA-DDR8 | GHA-DDR9 | GHA-DDR10 | ||
1 | J1.A1, D1 | 53.12 | 54.65 | 54.65 | 54.65 | 53.12 | 53.79 | 44.38 | 55.86 | 63.17 | 53.79 |
2 | J1.A1, D2 | 59.46 | 82.96 | 82.96 | 82.96 | 59.46 | 58.71 | 66.49 | 92.89 | 103.81 | 58.71 |
3 | J1, A1, D3 | 152.71 | 219.08 | 219.08 | 219.08 | 152.71 | 152.71 | 164.72 | 150.20 | 155.90 | 152.71 |
4 | J1, A2, D1 | 89.49 | 94.30 | 94.30 | 94.30 | 89.49 | 90.30 | 82.00 | 144.90 | 152.33 | 90.30 |
5 | J1, A2, D2 | 247.90 | 220.21 | 220.21 | 220.21 | 247.90 | 247.09 | 128.69 | 221.13 | 278.96 | 247.09 |
6 | J1, A2, D3 | 105.09 | 180.45 | 180.45 | 180.45 | 105.09 | 105.09 | 127.11 | 120.86 | 134.45 | 105.09 |
7 | J1, A3, D1 | 57.56 | 56.08 | 56.08 | 56.08 | 57.56 | 57.56 | 46.00 | 155.74 | 156.72 | 57.56 |
8 | J1, A3, D2 | 90.87 | 117.22 | 117.22 | 117.22 | 90.87 | 93.70 | 97.60 | 142.79 | 181.55 | 93.70 |
9 | J1, A3, D3 | 120.41 | 173.58 | 173.58 | 173.58 | 120.41 | 120.41 | 165.21 | 246.28 | 280.79 | 120.41 |
10 | J2.A1, D1 | 118.03 | 111.15 | 111.15 | 111.15 | 118.03 | 109.42 | 78.65 | 124.10 | 126.07 | 109.42 |
11 | J2.A1, D2 | 74.90 | 120.01 | 120.01 | 120.01 | 74.90 | 73.17 | 88.36 | 100.55 | 109.14 | 73.17 |
12 | J2, A1, D3 | 91.65 | 173.39 | 173.39 | 173.39 | 91.65 | 92.94 | 121.19 | 115.31 | 129.06 | 92.94 |
13 | J2, A2, D1 | 134.21 | 138.21 | 138.21 | 138.21 | 134.21 | 138.44 | 107.61 | 130.43 | 131.02 | 138.44 |
14 | J2, A2, D2 | 155.44 | 163.02 | 163.02 | 163.02 | 155.44 | 149.74 | 122.05 | 191.10 | 205.17 | 149.74 |
15 | J2, A2, D3 | 185.21 | 229.86 | 229.86 | 229.86 | 185.21 | 180.35 | 177.63 | 211.60 | 226.70 | 180.35 |
16 | J2, A3, D1 | 140.55 | 141.87 | 141.87 | 141.87 | 140.55 | 141.45 | 109.27 | 194.19 | 194.97 | 141.45 |
17 | J2, A3, D2 | 157.98 | 183.43 | 183.43 | 183.43 | 157.98 | 182.57 | 122.41 | 183.93 | 188.70 | 182.57 |
18 | J2, A3, D3 | 196.67 | 234.83 | 234.83 | 234.83 | 196.67 | 198.48 | 171.85 | 230.16 | 238.08 | 198.48 |
19 | J3, A1, D1 | 75.81 | 80.04 | 80.04 | 80.04 | 75.81 | 79.39 | 41.07 | 66.93 | 68.46 | 79.39 |
20 | J3, A1, D2 | 93.98 | 90.45 | 90.45 | 90.45 | 93.98 | 90.71 | 43.75 | 70.63 | 74.39 | 90.61 |
21 | J3, A1, D3 | 98.03 | 110.72 | 110.72 | 110.72 | 98.03 | 92.42 | 61.02 | 72.44 | 82.17 | 92.42 |
22 | J3, A2, D1 | 106.93 | 106.88 | 106.88 | 106.88 | 106.93 | 104.62 | 51.46 | 80.49 | 80.69 | 104.62 |
23 | J3, A2, D2 | 97.54 | 109.79 | 109.79 | 109.79 | 97.54 | 107.39 | 56.90 | 82.11 | 83.97 | 107.39 |
24 | J3, A2, D3 | 129.11 | 144.65 | 144.65 | 144.65 | 129.11 | 124.09 | 86.72 | 107.12 | 105.67 | 124.09 |
25 | J3, A3, D1 | 99.34 | 107.88 | 107.88 | 107.88 | 99.34 | 105.02 | 62.84 | 91.01 | 92.36 | 105.02 |
26 | J3, A3, D2 | 118.92 | 120.47 | 120.47 | 120.47 | 118.92 | 122.00 | 65.65 | 93.02 | 97.02 | 122.00 |
27 | J3, A3, D3 | 133.23 | 132.27 | 132.27 | 132.27 | 133.23 | 124.85 | 76.90 | 94.22 | 104.62 | 124.85 |
S. No. | ARPD score of each of the proposed variants of GHA-DDR in comparison with estimated optimal TWT | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
GHA-DDR11 | GHA-DDR12 | GHA-DDR13 | GHA-DDR14 | GHA-DDR15 | GHA-DDR16 | GHA-DDR17 | GHA-DDR18 | GHA-DDR19 | GHA-DDR20 | |
1 | 53.12 | 53.79 | 53.12 | 165.02 | 0.26 | 3.70 | 4.38 | 2.06 | 2.06 | 2.55 |
2 | 59.46 | 58.71 | 59.46 | 268.97 | 9.54 | 4.24 | 6.50 | 3.86 | 3.93 | 20.82 |
3 | 152.71 | 152.71 | 152.71 | 765.51 | 3.14 | 47.60 | 47.41 | 46.43 | 46.43 | 65.32 |
4 | 89.49 | 90.30 | 89.49 | 182.12 | 5.59 | 2.53 | 2.62 | 1.78 | 1.78 | 5.80 |
5 | 247.90 | 247.09 | 247.90 | 533.80 | 37.76 | 1.22 | 20.56 | 0.59 | 0.89 | 33.53 |
6 | 105.09 | 105.09 | 105.09 | 499.80 | 18.01 | 5.44 | 12.19 | 1.52 | 1.52 | 27.67 |
7 | 57.56 | 57.56 | 57.56 | 199.47 | 3.69 | 6.29 | 8.34 | 5.43 | 4.92 | 8.93 |
8 | 90.87 | 93.70 | 90.87 | 172.65 | 8.84 | 2.83 | 8.12 | 2.03 | 2.03 | 18.28 |
9 | 120.41 | 120.41 | 120.41 | 496.82 | 6.61 | 4.53 | 10.99 | 4.57 | 4.57 | 24.85 |
10 | 118.03 | 109.55 | 118.03 | 109.22 | 2.26 | 3.74 | 2.68 | 2.20 | 1.89 | 6.27 |
11 | 74.90 | 73.17 | 74.90 | 101.96 | 6.67 | 5.52 | 7.21 | 2.69 | 1.40 | 16.32 |
12 | 91.65 | 92.94 | 91.65 | 145.22 | 8.22 | 10.42 | 11.96 | 9.55 | 6.02 | 2.20 |
13 | 134.21 | 138.44 | 134.21 | 127.14 | 3.11 | 6.82 | 5.03 | 1.04 | 1.28 | 11.71 |
14 | 155.44 | 149.74 | 155.44 | 152.84 | 22.60 | 8.88 | 18.53 | 2.94 | 1.31 | 23.31 |
15 | 185.21 | 180.35 | 185.21 | 202.49 | 18.10 | 8.57 | 21.11 | 4.65 | 1.50 | 16.25 |
16 | 140.55 | 141.45 | 140.55 | 183.36 | 19.77 | 9.65 | 11.66 | 0.57 | 0.93 | 15.53 |
17 | 157.98 | 182.57 | 157.98 | 165.69 | 24.65 | 7.23 | 20.92 | 2.59 | 0.85 | 47.37 |
18 | 196.67 | 198.48 | 196.67 | 211.44 | 47.41 | 6.39 | 39.17 | 3.80 | 3.36 | 47.04 |
19 | 75.81 | 80.31 | 75.81 | 58.08 | 3.94 | 1.61 | 3.56 | 0.13 | 1.00 | 7.87 |
20 | 93.98 | 90.64 | 93.98 | 60.76 | 1.68 | 4.25 | 2.24 | 0.23 | 2.62 | 9.16 |
21 | 98.03 | 95.31 | 98.03 | 58.15 | 0.99 | 4.31 | 2.91 | 1.37 | 1.22 | 9.53 |
22 | 106.93 | 103.67 | 106.93 | 74.43 | 4.19 | 2.73 | 0.26 | 0.13 | 1.37 | 6.12 |
23 | 97.54 | 107.39 | 97.54 | 74.04 | 5.57 | 6.92 | 4.41 | 0.87 | 0.96 | 12.16 |
24 | 129.11 | 124.09 | 129.11 | 75.75 | 10.54 | 7.26 | 11.89 | 0.28 | 1.85 | 28.51 |
25 | 99.34 | 104.39 | 99.34 | 76.33 | 3.26 | 4.85 | 1.98 | 0.77 | 1.43 | 6.15 |
26 | 118.92 | 122.00 | 118.92 | 90.27 | 5.90 | 7.24 | 3.05 | 0.34 | 0.82 | 19.52 |
27 | 133.23 | 124.85 | 133.23 | 89.45 | 17.01 | 14.11 | 6.76 | 1.40 | 2.85 | 18.04 |
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Vimala Rani, M., Mathirajan, M. Performance evaluation of due-date based dispatching rules in dynamic scheduling of diffusion furnace. OPSEARCH 57, 462–512 (2020). https://doi.org/10.1007/s12597-019-00434-8
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DOI: https://doi.org/10.1007/s12597-019-00434-8