American Institute of Mathematical Sciences

Advanced
September  2020, 16(5): 2439-2457. doi: 10.3934/jimo.2019062

Minimizing total completion time in a two-machine no-wait flowshop with uncertain and bounded setup times

Muberra Allahverdi 1,, and  Ali Allahverdi 2,

1. 

Department of Mathematical Sciences, Kean University, New Jersey, USA
2. 

Department of Industrial and Management Systems Engineering, Kuwait University, Kuwait

1Corresponding Author: Muberra Allahverdi

Received  October 2018 Revised  February 2019 Published  September 2020 Early access  May 2019

We address a two-machine no-wait flowshop scheduling problem with respect to the performance measure of total completion time. Minimizing total completion time is important when inventory cost is of concern. Setup times are treated separately from processing times. Furthermore, setup times are uncertain with unknown distributions and are within some lower and upper bounds. We develop a dominance relation and propose eight algorithms to solve the problem. The proposed algorithms, which assign different weights to the processing and setup times on both machines, convert the two-machine problem into a single-machine one for which an optimal solution is known. We conduct computational experiments to evaluate the proposed algorithms. Computational experiments reveal that one of the proposed algorithms, which assigns the same weight to setup and processing times, is superior to the rest of the algorithms. The results are statistically verified by constructing confidence intervals and test of hypothesis.

Keywords: Scheduling, no-wait flowshop, total completion time, uncerain setup times.
Mathematics Subject Classification: Primary: 90; Secondary: 90B36.
Citation: Muberra Allahverdi, Ali Allahverdi. Minimizing total completion time in a two-machine no-wait flowshop with uncertain and bounded setup times. Journal of Industrial & Management Optimization, 2020, 16 (5) : 2439-2457. doi: 10.3934/jimo.2019062
References:
[1]

A. Allahverdi, The third comprehensive survey on scheduling problems with setup times/costs, European Journal of Operational Research, 246 (2015), 345-378.  doi: 10.1016/j.ejor.2015.04.004.  Google Scholar

[2]

A. Allahverdi, A survey of scheduling problems with no-wait in process, European Journal of Operational Research, 255 (2016), 665-686.  doi: 10.1016/j.ejor.2016.05.036.  Google Scholar

[3]

A. Allahverdi, Two-machine flowshop scheduling problem to minimize makespan with bounded setup and processing times, Int. Journal of Agile Manufacturing, 8 (2005), 145-153.   Google Scholar

[4]

A. Allahverdi, Two-machine flowshop scheduling problem to minimize total completion time with bounded setup and processing times, Int. Journal of Production Economics, 103 (2006a), 386-400.   Google Scholar

[5]

A. Allahverdi, Two-machine flowshop scheduling problem to minimize maximum lateness with bounded setup and processing times, Kuwait Journal of Science and Engineering, 33 (2006), 233-251.   Google Scholar

[6]

A. Allahverdi, T. Aldowaisan and Y. N. Sotskov, Two-machine flowshop scheduling problem to minimize makespan or total completion time with random and bounded setup times, Int. Journal of Mathematics and Mathematical Sciences, 39 (2003), 2475-2486.  doi: 10.1155/S016117120321019X.  Google Scholar

[7]

A. Allahverdi and M. Allahverdi, Two-machine no-wait flowshop scheduling problem with uncertain setup times to minimize maximum lateness, Computational and Applied Mathematics, 37 (2018), 6774-6794.  doi: 10.1007/s40314-018-0694-3.  Google Scholar

[8]

A. Allahverdi and H. Aydilek, Heuristics for two-machine flowshop scheduling problem to minimize maximum lateness with bounded processing times, Computers and Mathematics with Applications, 60 (2010), 1374-1384.  doi: 10.1016/j.camwa.2010.06.019.  Google Scholar

[9]

A. Aydilek, H. Aydilek and A. Allahverdi, Increasing the profitability and competitiveness in a production environment with random and bounded setup times, Int. Journal of Production Research, 51 (2013), 106-117.  doi: 10.1080/00207543.2011.652263.  Google Scholar

[10]

A. Aydilek, H. Aydilek and A. Allahverdi, Production in a two-machine flowshop scheduling environment with uncertain processing and setup times to minimize makespan, Int. Journal of Production Research, 53 (2015), 2803-2819.  doi: 10.1080/00207543.2014.997403.  Google Scholar

[11]

A. Aydilek, H. Aydilek and A. Allahverdi, Algorithms for minimizing the number of tardy jobs for reducing production cost with uncertain processing times, Applied Mathematical Modelling, 45 (2017), 982-996.  doi: 10.1016/j.apm.2017.01.039.  Google Scholar

[12]

O. Braun, T. C. Lai, G. Schmidt and Y. N. Sotskov, Stability of Johnson's schedule with respect to limited machine availability, Int. Journal of Production Research, 40 (2002), 4381-4400.   Google Scholar

[13]

A. A. Cunningham and S. K. Dutta, Scheduling jobs with exponentially distributed processing times on two machines of a flow shop, Naval Research Logistics Quarterly, 20 (1973), 69-81.  doi: 10.1002/nav.3800200107.  Google Scholar

[14]

O. Engin and A. Güçlü, A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems, Applied Soft Computing Journal, 72 (2018), 166-176.  doi: 10.1016/j.asoc.2018.08.002.  Google Scholar

[15]

O. Engin and C. Günaydin, An adaptive learning approach for no-wait flowshop scheduling problems to minimize makespan, International Journal of Computational Intelligence Systems, 4 (2011), 521-529.  doi: 10.1080/18756891.2011.9727810.  Google Scholar

[16]

E. M. Gonzalez-Neira, D. Ferone, S. Hatami and A. A. Juan, A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times, Simulation Modelling Practice and Theory, 79 (2017), 23-36.  doi: 10.1016/j.simpat.2017.09.001.  Google Scholar

[17]

N. G. Hall and C. Sriskandarajah, A survey of machine scheduling problems with blocking and no-wait in process, Operations Research, 44 (1996), 510-525.  doi: 10.1287/opre.44.3.510.  Google Scholar

[18]

P. J. Kalczynski and J. Kamburowski, A heuristic for minimizing the expected makespan in two-machine flowshops with consistent coefficients of variation, European Journal of Operational Research, 169 (2006), 742-750.  doi: 10.1016/j.ejor.2004.08.045.  Google Scholar

[19]

I. H. Karacizmeli and S. N. Ogulata, Energy consumption management in textile finishing plants: A cost effective and sequence dependent scheduling model, Textile and Apparel, 27 (2017), 145-152.   Google Scholar

[20]

S. C. Kim and P. M. Bobrowski, Scheduling jobs with uncertain setup times and sequence dependency, Omega Int. Journal of Management Science, 25 (1997), 437-447.  doi: 10.1016/S0305-0483(97)00013-3.  Google Scholar

[21]

G. M. Kopanos, J. Miguel Lainez and L. Puigjaner, An efficient mixed-integer linear programming scheduling framework for addressing sequence-dependent setup issues in batch plants, Industrial & Engineering Chemistry Research, 48 (2009), 6346-6357.  doi: 10.1021/ie801127t.  Google Scholar

[22]

P. S. Ku and S. C. Niu, On Johnson's two-machine flow shop with random processing times, Operations Research, 34 (1986), 130-136.  doi: 10.1287/opre.34.1.130.  Google Scholar

[23]

T. C. Lai, Y. N. Sotskov, N. Y. Sotskova and F. Werner, Optimal makespan scheduling with given bounds of processing times, Mathematical and Computer Modelling, 26 (1997), 67-86.  doi: 10.1016/S0895-7177(97)00132-5.  Google Scholar

[24]

X. Li, Z. Yang, R. Ruiz, T. Chen and S. Sui, An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects, Information Sciences, 453 (2018), 408–425. doi: 10.1016/j.ins.2018.04.038.  Google Scholar

[25]

R. Macchiaroli, S. Molè and S. Riemma, Modelling and optimization of industrial manufacturing processes subject to no-wait constraints, Int. Journal of Production Research, 37 (1999), 2585-2607.  doi: 10.1080/002075499190671.  Google Scholar

[26]

N. M. Matsveichuk, Y. N. Sotskov, N. G. Egorova and T. C. Lai, Schedule execution for two-machine flow-shop with interval processing times, Mathematical and Computer Modelling, 49 (2009), 991-1011.  doi: 10.1016/j.mcm.2008.02.004.  Google Scholar

[27]

N. M. Matsveichuk, Y. N. Sotskov and F. Werner, Partial job order for solving the two-machine flow-shop minimum-length problem with uncertain processing times, Optimization, 60 (2011), 1493-1517.  doi: 10.1080/02331931003657691.  Google Scholar

[28]

M. Pinedo, Stochastic scheduling with release dates and due dates, Operations Research, 31 (1983), 559-572.  doi: 10.1287/opre.31.3.559.  Google Scholar

[29]

M. Pinedo, Scheduling Theory, Algorithms, and Systems, Prentice Hall, Englewood Cliffs, New Jersey, Page 349, 1995. Google Scholar

[30]

V. Portougal and D. Trietsch, Johnson's problem with stochastic processing times and optimal service level, European Journal of Operational Research, 169 (2006), 751-760.  doi: 10.1016/j.ejor.2004.09.056.  Google Scholar

[31]

V. Riahi and M. Kazemi, A new hybrid ant colony algorithm for scheduling of no-wait flowshop, Operational Research, 18 (2018), 55-74.  doi: 10.1007/s12351-016-0253-x.  Google Scholar

[32]

D. K. Seo, C. M. Klein and W. Jang, Single machine stochastic scheduling to minimize the expected number of tardy jobs using mathematical programming models, Computers and Industrial Engineering, 48 (2005), 153-161.  doi: 10.1016/j.cie.2005.01.002.  Google Scholar

[33]

H. M. Soroush, Sequencing and due-date determination in the stochastic single machine problem with earliness and tardiness costs, European Journal of Operational Research, 113 (1999), 450-468.   Google Scholar

[34]

H. M. Soroush, Minimizing the weighted number of early and tardy jobs in a stochastic single machine scheduling problem., European Journal of Operational Research, 181 (2007), 266-287.  doi: 10.1016/j.ejor.2006.05.036.  Google Scholar

[35]

Y. N. Sotskov and N. M. Matsveichuk, Uncertainty measure for the Bellman-Johnson problem with interval processing times, Cybernetics and System Analysis, 48 (2012), 641-652.  doi: 10.1007/s10559-012-9445-4.  Google Scholar

[36]

Y. N. Sotskov, N. G. Egorova and T. C. Lai, Minimizing total weighted flow time of a set of jobs with interval processing times, Mathematical and Computer Modelling, 50 (2009), 556-573.  doi: 10.1016/j.mcm.2009.03.006.  Google Scholar

[37]

Y. N. Sotskov and T. C. Lai, Minimizing total weighted flow under uncertainty using dominance and a stability box, Computers and Operations Research, 39 (2012), 1271-1289.  doi: 10.1016/j.cor.2011.02.001.  Google Scholar

[38]

K. Wang and S. H. Choi, A decomposition-based approach to flexible flow shop scheduling under machine breakdown, Int. Journal of Production Research, 50 (2012), 215-234.  doi: 10.1080/00207543.2011.571456.  Google Scholar

[39]

Y. Wang, X. Li, R. Ruiz and S. Sui, An iterated greedy heuristic for mixed no-wait flowshop problems, IEEE Transactions on Cybernetics, 48 (2018), 1553-1566.  doi: 10.1109/TCYB.2017.2707067.  Google Scholar

[40]

K. C. Ying and S. W. Lin, Minimizing makespan for no-wait flowshop scheduling problems with setup times, Computers and Industrial Engineering, 121 (2018), 73-81.  doi: 10.1016/j.cie.2018.05.030.  Google Scholar

show all references

References:
[1]

A. Allahverdi, The third comprehensive survey on scheduling problems with setup times/costs, European Journal of Operational Research, 246 (2015), 345-378.  doi: 10.1016/j.ejor.2015.04.004.  Google Scholar

[2]

A. Allahverdi, A survey of scheduling problems with no-wait in process, European Journal of Operational Research, 255 (2016), 665-686.  doi: 10.1016/j.ejor.2016.05.036.  Google Scholar

[3]

A. Allahverdi, Two-machine flowshop scheduling problem to minimize makespan with bounded setup and processing times, Int. Journal of Agile Manufacturing, 8 (2005), 145-153.   Google Scholar

[4]

A. Allahverdi, Two-machine flowshop scheduling problem to minimize total completion time with bounded setup and processing times, Int. Journal of Production Economics, 103 (2006a), 386-400.   Google Scholar

[5]

A. Allahverdi, Two-machine flowshop scheduling problem to minimize maximum lateness with bounded setup and processing times, Kuwait Journal of Science and Engineering, 33 (2006), 233-251.   Google Scholar

[6]

A. Allahverdi, T. Aldowaisan and Y. N. Sotskov, Two-machine flowshop scheduling problem to minimize makespan or total completion time with random and bounded setup times, Int. Journal of Mathematics and Mathematical Sciences, 39 (2003), 2475-2486.  doi: 10.1155/S016117120321019X.  Google Scholar

[7]

A. Allahverdi and M. Allahverdi, Two-machine no-wait flowshop scheduling problem with uncertain setup times to minimize maximum lateness, Computational and Applied Mathematics, 37 (2018), 6774-6794.  doi: 10.1007/s40314-018-0694-3.  Google Scholar

[8]

A. Allahverdi and H. Aydilek, Heuristics for two-machine flowshop scheduling problem to minimize maximum lateness with bounded processing times, Computers and Mathematics with Applications, 60 (2010), 1374-1384.  doi: 10.1016/j.camwa.2010.06.019.  Google Scholar

[9]

A. Aydilek, H. Aydilek and A. Allahverdi, Increasing the profitability and competitiveness in a production environment with random and bounded setup times, Int. Journal of Production Research, 51 (2013), 106-117.  doi: 10.1080/00207543.2011.652263.  Google Scholar

[10]

A. Aydilek, H. Aydilek and A. Allahverdi, Production in a two-machine flowshop scheduling environment with uncertain processing and setup times to minimize makespan, Int. Journal of Production Research, 53 (2015), 2803-2819.  doi: 10.1080/00207543.2014.997403.  Google Scholar

[11]

A. Aydilek, H. Aydilek and A. Allahverdi, Algorithms for minimizing the number of tardy jobs for reducing production cost with uncertain processing times, Applied Mathematical Modelling, 45 (2017), 982-996.  doi: 10.1016/j.apm.2017.01.039.  Google Scholar

[12]

O. Braun, T. C. Lai, G. Schmidt and Y. N. Sotskov, Stability of Johnson's schedule with respect to limited machine availability, Int. Journal of Production Research, 40 (2002), 4381-4400.   Google Scholar

[13]

A. A. Cunningham and S. K. Dutta, Scheduling jobs with exponentially distributed processing times on two machines of a flow shop, Naval Research Logistics Quarterly, 20 (1973), 69-81.  doi: 10.1002/nav.3800200107.  Google Scholar

[14]

O. Engin and A. Güçlü, A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems, Applied Soft Computing Journal, 72 (2018), 166-176.  doi: 10.1016/j.asoc.2018.08.002.  Google Scholar

[15]

O. Engin and C. Günaydin, An adaptive learning approach for no-wait flowshop scheduling problems to minimize makespan, International Journal of Computational Intelligence Systems, 4 (2011), 521-529.  doi: 10.1080/18756891.2011.9727810.  Google Scholar

[16]

E. M. Gonzalez-Neira, D. Ferone, S. Hatami and A. A. Juan, A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times, Simulation Modelling Practice and Theory, 79 (2017), 23-36.  doi: 10.1016/j.simpat.2017.09.001.  Google Scholar

[17]

N. G. Hall and C. Sriskandarajah, A survey of machine scheduling problems with blocking and no-wait in process, Operations Research, 44 (1996), 510-525.  doi: 10.1287/opre.44.3.510.  Google Scholar

[18]

P. J. Kalczynski and J. Kamburowski, A heuristic for minimizing the expected makespan in two-machine flowshops with consistent coefficients of variation, European Journal of Operational Research, 169 (2006), 742-750.  doi: 10.1016/j.ejor.2004.08.045.  Google Scholar

[19]

I. H. Karacizmeli and S. N. Ogulata, Energy consumption management in textile finishing plants: A cost effective and sequence dependent scheduling model, Textile and Apparel, 27 (2017), 145-152.   Google Scholar

[20]

S. C. Kim and P. M. Bobrowski, Scheduling jobs with uncertain setup times and sequence dependency, Omega Int. Journal of Management Science, 25 (1997), 437-447.  doi: 10.1016/S0305-0483(97)00013-3.  Google Scholar

[21]

G. M. Kopanos, J. Miguel Lainez and L. Puigjaner, An efficient mixed-integer linear programming scheduling framework for addressing sequence-dependent setup issues in batch plants, Industrial & Engineering Chemistry Research, 48 (2009), 6346-6357.  doi: 10.1021/ie801127t.  Google Scholar

[22]

P. S. Ku and S. C. Niu, On Johnson's two-machine flow shop with random processing times, Operations Research, 34 (1986), 130-136.  doi: 10.1287/opre.34.1.130.  Google Scholar

[23]

T. C. Lai, Y. N. Sotskov, N. Y. Sotskova and F. Werner, Optimal makespan scheduling with given bounds of processing times, Mathematical and Computer Modelling, 26 (1997), 67-86.  doi: 10.1016/S0895-7177(97)00132-5.  Google Scholar

[24]

X. Li, Z. Yang, R. Ruiz, T. Chen and S. Sui, An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects, Information Sciences, 453 (2018), 408–425. doi: 10.1016/j.ins.2018.04.038.  Google Scholar

[25]

R. Macchiaroli, S. Molè and S. Riemma, Modelling and optimization of industrial manufacturing processes subject to no-wait constraints, Int. Journal of Production Research, 37 (1999), 2585-2607.  doi: 10.1080/002075499190671.  Google Scholar

[26]

N. M. Matsveichuk, Y. N. Sotskov, N. G. Egorova and T. C. Lai, Schedule execution for two-machine flow-shop with interval processing times, Mathematical and Computer Modelling, 49 (2009), 991-1011.  doi: 10.1016/j.mcm.2008.02.004.  Google Scholar

[27]

N. M. Matsveichuk, Y. N. Sotskov and F. Werner, Partial job order for solving the two-machine flow-shop minimum-length problem with uncertain processing times, Optimization, 60 (2011), 1493-1517.  doi: 10.1080/02331931003657691.  Google Scholar

[28]

M. Pinedo, Stochastic scheduling with release dates and due dates, Operations Research, 31 (1983), 559-572.  doi: 10.1287/opre.31.3.559.  Google Scholar

[29]

M. Pinedo, Scheduling Theory, Algorithms, and Systems, Prentice Hall, Englewood Cliffs, New Jersey, Page 349, 1995. Google Scholar

[30]

V. Portougal and D. Trietsch, Johnson's problem with stochastic processing times and optimal service level, European Journal of Operational Research, 169 (2006), 751-760.  doi: 10.1016/j.ejor.2004.09.056.  Google Scholar

[31]

V. Riahi and M. Kazemi, A new hybrid ant colony algorithm for scheduling of no-wait flowshop, Operational Research, 18 (2018), 55-74.  doi: 10.1007/s12351-016-0253-x.  Google Scholar

[32]

D. K. Seo, C. M. Klein and W. Jang, Single machine stochastic scheduling to minimize the expected number of tardy jobs using mathematical programming models, Computers and Industrial Engineering, 48 (2005), 153-161.  doi: 10.1016/j.cie.2005.01.002.  Google Scholar

[33]

H. M. Soroush, Sequencing and due-date determination in the stochastic single machine problem with earliness and tardiness costs, European Journal of Operational Research, 113 (1999), 450-468.   Google Scholar

[34]

H. M. Soroush, Minimizing the weighted number of early and tardy jobs in a stochastic single machine scheduling problem., European Journal of Operational Research, 181 (2007), 266-287.  doi: 10.1016/j.ejor.2006.05.036.  Google Scholar

[35]

Y. N. Sotskov and N. M. Matsveichuk, Uncertainty measure for the Bellman-Johnson problem with interval processing times, Cybernetics and System Analysis, 48 (2012), 641-652.  doi: 10.1007/s10559-012-9445-4.  Google Scholar

[36]

Y. N. Sotskov, N. G. Egorova and T. C. Lai, Minimizing total weighted flow time of a set of jobs with interval processing times, Mathematical and Computer Modelling, 50 (2009), 556-573.  doi: 10.1016/j.mcm.2009.03.006.  Google Scholar

[37]

Y. N. Sotskov and T. C. Lai, Minimizing total weighted flow under uncertainty using dominance and a stability box, Computers and Operations Research, 39 (2012), 1271-1289.  doi: 10.1016/j.cor.2011.02.001.  Google Scholar

[38]

K. Wang and S. H. Choi, A decomposition-based approach to flexible flow shop scheduling under machine breakdown, Int. Journal of Production Research, 50 (2012), 215-234.  doi: 10.1080/00207543.2011.571456.  Google Scholar

[39]

Y. Wang, X. Li, R. Ruiz and S. Sui, An iterated greedy heuristic for mixed no-wait flowshop problems, IEEE Transactions on Cybernetics, 48 (2018), 1553-1566.  doi: 10.1109/TCYB.2017.2707067.  Google Scholar

[40]

K. C. Ying and S. W. Lin, Minimizing makespan for no-wait flowshop scheduling problems with setup times, Computers and Industrial Engineering, 121 (2018), 73-81.  doi: 10.1016/j.cie.2018.05.030.  Google Scholar

Figure 1.  Flowchart of the algorithms
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  The distributions used for generating $ s_{i, k} $ within $ Ls_{i, k} $ and $ Us_{i, k} $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  Overall Avg. Error of Algorithms
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 4.  Avg. Std. of Algorithms
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 5.  Overall Avg. Error of Algorithms with respect to $H$
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 6.  Overall Avg. Error of Algorithms with respect to $H$
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 7.  Overall Avg. Error of Algorithm $ALG-6$ with respect to $H$
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 8.  Overall Avg. Error of Algorithms with respect to distributions
Figure Options

Download full-size image

Download as PowerPoint slide

Table 1.  Error of Algorithms for Positive Linear Distribution
$n$
Algorithm $H$ 100 200 300 400 500 Avg.
$ALG-1$ 20 9.08 9.11 9.17 9.17 9.27 9.16
$ALG-2$ 9.38 9.37 9.28 9.34 9.3 9.33
$ALG-3$ 3.05 2.98 2.95 2.96 2.97 2.98
$ALG-4$ 4.99 4.93 4.88 4.9 4.9 4.92
$ALG-5$ 4.85 4.81 4.76 4.81 4.83 4.79
$ALG-6$ 0.1 0.02 0 0 0 0.02
$ALG-7$ 4.23 4.1 4.02 4 3.95 4.06
$ALG-8$ 2.44 2.35 2.44 2.44 2.44 2.42
$ALG-1$ 30 9.17 9.33 9.42 9.4 9.4 9.34
$ALG-2$ 9.5 9.56 9.48 9.47 9.47 9.50
$ALG-3$ 3.04 3.1 3.03 3.03 3 3.04
$ALG-4$ 5.04 5.07 4.97 4.98 4.98 5.01
$ALG-5$ 4.81 4.93 4.95 4.95 4.95 4.92
$ALG-6$ 0.08 0.01 0 0 0 0.02
$ALG-7$ 4.2 4.12 4.02 4.04 3.96 4.07
$ALG-8$ 2.48 2.45 2.46 2.49 2.49 2.47
$ALG-1$ 40 9.37 9.47 9.49 9.54 9.56 9.49
$ALG-2$ 9.65 9.69 9.64 9.63 9.67 9.66
$ALG-3$ 3.01 3.07 3.06 3.05 3.08 3.05
$ALG-4$ 5.06 5.06 5.06 5.05 5.06 5.06
$ALG-5$ 4.93 5.01 4.96 5 5.05 4.99
$ALG-6$ 0.09 0.02 0 0 0 0.02
$ALG-7$ 4.24 4.17 4.03 4.1 4.12 4.13
$ALG-8$ 2.47 2.51 2.53 2.51 2.57 2.52
Avg. 4.80 4.80 4.78 4.79 4.79
Table Options

Download as excel

$n$
Algorithm $H$ 100 200 300 400 500 Avg.
$ALG-1$ 20 9.08 9.11 9.17 9.17 9.27 9.16
$ALG-2$ 9.38 9.37 9.28 9.34 9.3 9.33
$ALG-3$ 3.05 2.98 2.95 2.96 2.97 2.98
$ALG-4$ 4.99 4.93 4.88 4.9 4.9 4.92
$ALG-5$ 4.85 4.81 4.76 4.81 4.83 4.79
$ALG-6$ 0.1 0.02 0 0 0 0.02
$ALG-7$ 4.23 4.1 4.02 4 3.95 4.06
$ALG-8$ 2.44 2.35 2.44 2.44 2.44 2.42
$ALG-1$ 30 9.17 9.33 9.42 9.4 9.4 9.34
$ALG-2$ 9.5 9.56 9.48 9.47 9.47 9.50
$ALG-3$ 3.04 3.1 3.03 3.03 3 3.04
$ALG-4$ 5.04 5.07 4.97 4.98 4.98 5.01
$ALG-5$ 4.81 4.93 4.95 4.95 4.95 4.92
$ALG-6$ 0.08 0.01 0 0 0 0.02
$ALG-7$ 4.2 4.12 4.02 4.04 3.96 4.07
$ALG-8$ 2.48 2.45 2.46 2.49 2.49 2.47
$ALG-1$ 40 9.37 9.47 9.49 9.54 9.56 9.49
$ALG-2$ 9.65 9.69 9.64 9.63 9.67 9.66
$ALG-3$ 3.01 3.07 3.06 3.05 3.08 3.05
$ALG-4$ 5.06 5.06 5.06 5.05 5.06 5.06
$ALG-5$ 4.93 5.01 4.96 5 5.05 4.99
$ALG-6$ 0.09 0.02 0 0 0 0.02
$ALG-7$ 4.24 4.17 4.03 4.1 4.12 4.13
$ALG-8$ 2.47 2.51 2.53 2.51 2.57 2.52
Avg. 4.80 4.80 4.78 4.79 4.79
Table 2.  Error of Algorithms for Negative Linear Distribution
$n$
Algorithm $H$ 100 200 300 400 500 Avg.
$ALG-1$ 20 9.08 9.13 9.22 9.26 9.27 9.19
$ALG-2$ 9.38 9.26 9.33 9.35 9.27 9.32
$ALG-3$ 3.05 2.97 3.04 3.02 2.99 3.01
$ALG-4$ 4.99 4.85 4.92 4.9 4.87 4.91
$ALG-5$ 4.75 4.73 4.89 4.92 4.86 4.83
$ALG-6$ 0.1 0.02 0 0 0 0.02
$ALG-7$ 4.23 4.1 4.06 4.07 3.95 4.08
$ALG-8$ 2.44 2.41 2.44 2.5 2.43 2.44
$ALG-1$ 30 9.17 9.37 9.37 9.37 9.41 9.34
$ALG-2$ 9.5 9.51 9.53 9.5 9.51 9.51
$ALG-3$ 3.04 3.05 3.04 3.04 3.05 3.04
$ALG-4$ 5.04 5 5.03 5 4.99 5.01
$ALG-5$ 4.81 4.92 4.9 4.94 4.93 4.90
$ALG-6$ 0.08 0.01 0 0 0 0.02
$ALG-7$ 4.2 4.15 4.1 4.05 4.01 4.10
$ALG-8$ 2.48 2.54 2.46 2.55 2.53 2.51
$ALG-1$ 40 9.37 9.37 9.5 9.51 9.55 9.46
$ALG-2$ 9.65 9.54 9.59 9.64 9.62 9.61
$ALG-3$ 3.01 2.96 3.04 3.09 3.07 3.03
$ALG-4$ 5.06 4.97 5.01 5.06 5.05 5.03
$ALG-5$ 4.93 4.85 4.95 4.97 5.01 4.94
$ALG-6$ 0.09 0.01 0 0 0 0.02
$ALG-7$ 4.24 4.12 4.08 4.07 4.08 4.12
$ALG-8$ 2.47 2.44 2.58 2.54 2.57 2.52
Avg. 4.80 4.76 4.80 4.81 4.79
Table Options

Download as excel

$n$
Algorithm $H$ 100 200 300 400 500 Avg.
$ALG-1$ 20 9.08 9.13 9.22 9.26 9.27 9.19
$ALG-2$ 9.38 9.26 9.33 9.35 9.27 9.32
$ALG-3$ 3.05 2.97 3.04 3.02 2.99 3.01
$ALG-4$ 4.99 4.85 4.92 4.9 4.87 4.91
$ALG-5$ 4.75 4.73 4.89 4.92 4.86 4.83
$ALG-6$ 0.1 0.02 0 0 0 0.02
$ALG-7$ 4.23 4.1 4.06 4.07 3.95 4.08
$ALG-8$ 2.44 2.41 2.44 2.5 2.43 2.44
$ALG-1$ 30 9.17 9.37 9.37 9.37 9.41 9.34
$ALG-2$ 9.5 9.51 9.53 9.5 9.51 9.51
$ALG-3$ 3.04 3.05 3.04 3.04 3.05 3.04
$ALG-4$ 5.04 5 5.03 5 4.99 5.01
$ALG-5$ 4.81 4.92 4.9 4.94 4.93 4.90
$ALG-6$ 0.08 0.01 0 0 0 0.02
$ALG-7$ 4.2 4.15 4.1 4.05 4.01 4.10
$ALG-8$ 2.48 2.54 2.46 2.55 2.53 2.51
$ALG-1$ 40 9.37 9.37 9.5 9.51 9.55 9.46
$ALG-2$ 9.65 9.54 9.59 9.64 9.62 9.61
$ALG-3$ 3.01 2.96 3.04 3.09 3.07 3.03
$ALG-4$ 5.06 4.97 5.01 5.06 5.05 5.03
$ALG-5$ 4.93 4.85 4.95 4.97 5.01 4.94
$ALG-6$ 0.09 0.01 0 0 0 0.02
$ALG-7$ 4.24 4.12 4.08 4.07 4.08 4.12
$ALG-8$ 2.47 2.44 2.58 2.54 2.57 2.52
Avg. 4.80 4.76 4.80 4.81 4.79
Table 3.  Error of Algorithm for Uniform Distribution
$n$
Algorithm $H$ 100 200 300 400 500 Avg.
$ALG-1$ 20 9.05 9.32 9.34 9.37 9.41 9.30
$ALG-2$ 9.39 9.45 9.43 9.49 9.43 9.44
$ALG-3$ 3.09 3.02 2.97 3.04 3.04 3.03
$ALG-4$ 4.98 4.96 4.92 4.98 4.96 4.96
$ALG-5$ 4.86 4.88 4.87 4.94 4.95 4.90
$ALG-6$ 0.1 0.02 0 0 0 0.02
$ALG-7$ 4.24 4.08 4.09 4.09 4.05 4.11
$ALG-8$ 2.42 4.08 4.09 4.09 4.05 4.11
$ALG-1$ 30 9.09 9.47 9.52 9.64 9.56 9.46
$ALG-2$ 9.59 9.61 9.63 9.67 9.59 9.63
$ALG-3$ 3.1 3.04 3.04 3.09 3.03 3.06
$ALG-4$ 5.13 5.03 5.02 5.06 5 5.05
$ALG-5$ 4.86 4.94 4.96 5.05 5.02 4.97
$ALG-6$ 0.1 0.02 0 0 0 0.02
$ALG-7$ 4.17 4.17 4.08 4.13 4.06 4.12
$ALG-8$ 2.44 2.49 2.53 2.59 2.56 2.52
$ALG-1$ 40 9.32 9.68 9.68 9.7 9.79 9.63
$ALG-2$ 9.67 9.89 9.85 9.87 9.89 9.83
$ALG-3$ 3.05 3.06 3.06 3.08 3.1 3.07
$ALG-4$ 5.07 5.14 5.13 5.13 5.13 5.12
$ALG-5$ 4.86 5.09 5.04 5.07 5.09 5.03
$ALG-6$ 0.09 0.01 0 0 0 0.02
$ALG-7$ 4.31 4.28 4.23 4.19 4.21 4.24
$ALG-8$ 2.43 2.61 2.64 2.68 2.68 2.61
Avg. 4.81 4.86 4.86 4.89 4.88
Table Options

Download as excel

$n$
Algorithm $H$ 100 200 300 400 500 Avg.
$ALG-1$ 20 9.05 9.32 9.34 9.37 9.41 9.30
$ALG-2$ 9.39 9.45 9.43 9.49 9.43 9.44
$ALG-3$ 3.09 3.02 2.97 3.04 3.04 3.03
$ALG-4$ 4.98 4.96 4.92 4.98 4.96 4.96
$ALG-5$ 4.86 4.88 4.87 4.94 4.95 4.90
$ALG-6$ 0.1 0.02 0 0 0 0.02
$ALG-7$ 4.24 4.08 4.09 4.09 4.05 4.11
$ALG-8$ 2.42 4.08 4.09 4.09 4.05 4.11
$ALG-1$ 30 9.09 9.47 9.52 9.64 9.56 9.46
$ALG-2$ 9.59 9.61 9.63 9.67 9.59 9.63
$ALG-3$ 3.1 3.04 3.04 3.09 3.03 3.06
$ALG-4$ 5.13 5.03 5.02 5.06 5 5.05
$ALG-5$ 4.86 4.94 4.96 5.05 5.02 4.97
$ALG-6$ 0.1 0.02 0 0 0 0.02
$ALG-7$ 4.17 4.17 4.08 4.13 4.06 4.12
$ALG-8$ 2.44 2.49 2.53 2.59 2.56 2.52
$ALG-1$ 40 9.32 9.68 9.68 9.7 9.79 9.63
$ALG-2$ 9.67 9.89 9.85 9.87 9.89 9.83
$ALG-3$ 3.05 3.06 3.06 3.08 3.1 3.07
$ALG-4$ 5.07 5.14 5.13 5.13 5.13 5.12
$ALG-5$ 4.86 5.09 5.04 5.07 5.09 5.03
$ALG-6$ 0.09 0.01 0 0 0 0.02
$ALG-7$ 4.31 4.28 4.23 4.19 4.21 4.24
$ALG-8$ 2.43 2.61 2.64 2.68 2.68 2.61
Avg. 4.81 4.86 4.86 4.89 4.88
Table 4.  Error of Algorithm for Normal Distribution
$n$
Algorithm $H$ 100 200 300 400 500 Avg.
$ALG-1$ 20 9.04 9.3 9.32 9.35 9.38 9.28
$ALG-2$ 9.5 9.46 9.45 9.46 9.5 9.47
$ALG-3$ 3.03 3 3 3.03 3.06 3.02
$ALG-4$ 5.01 4.97 4.92 4.96 4.95 4.90
$ALG-5$ 4.81 4.88 4.9 4.94 4.95 4.90
$ALG-6$ 0.08 0.01 0 0 0 0.02
$ALG-7$ 4.27 4.09 4.13 4.08 4.06 4.13
$ALG-8$ 2.47 2.46 2.45 2.5 2.55 2.49
$ALG-1$ 30 9.43 9.43 9.54 9.57 9.71 9.54
$ALG-2$ 9.8 9.71 9.66 9.7 9.72 9.72
$ALG-3$ 3.11 3 3.09 3.08 3.12 3.08
$ALG-4$ 5.16 5.06 5.06 5.05 5.11 5.09
$ALG-5$ 4.99 4.92 5 4.99 5.08 5.00
$ALG-6$ 0.1 0.02 0 0 0 0.02
$ALG-7$ 4.38 4.15 4.17 4.17 4.17 4.21
$ALG-8$ 2.52 2.52 2.59 2.57 2.62 2.56
$ALG-1$ 40 9.57 9.7 9.77 9.78 9.86 9.74
$ALG-2$ 10.04 9.96 9.9 9.86 9.86 9.92
$ALG-3$ 3.12 3.03 3.05 3.07 3.05 3.06
$ALG-4$ 5.25 5.17 5.14 5.11 5.12 5.16
$ALG-5$ 5.01 5.03 5.05 5.11 5.1 5.06
$ALG-6$ 0.08 0.01 0 0 0 0.02
$ALG-7$ 4.47 4.2 4.21 4.24 4.19 4.26
$ALG-8$ 2.69 2.6 2.73 2.71 2.7 2.69
Avg. 4.91 4.86 4.88 4.89 4.91
Table Options

Download as excel

$n$
Algorithm $H$ 100 200 300 400 500 Avg.
$ALG-1$ 20 9.04 9.3 9.32 9.35 9.38 9.28
$ALG-2$ 9.5 9.46 9.45 9.46 9.5 9.47
$ALG-3$ 3.03 3 3 3.03 3.06 3.02
$ALG-4$ 5.01 4.97 4.92 4.96 4.95 4.90
$ALG-5$ 4.81 4.88 4.9 4.94 4.95 4.90
$ALG-6$ 0.08 0.01 0 0 0 0.02
$ALG-7$ 4.27 4.09 4.13 4.08 4.06 4.13
$ALG-8$ 2.47 2.46 2.45 2.5 2.55 2.49
$ALG-1$ 30 9.43 9.43 9.54 9.57 9.71 9.54
$ALG-2$ 9.8 9.71 9.66 9.7 9.72 9.72
$ALG-3$ 3.11 3 3.09 3.08 3.12 3.08
$ALG-4$ 5.16 5.06 5.06 5.05 5.11 5.09
$ALG-5$ 4.99 4.92 5 4.99 5.08 5.00
$ALG-6$ 0.1 0.02 0 0 0 0.02
$ALG-7$ 4.38 4.15 4.17 4.17 4.17 4.21
$ALG-8$ 2.52 2.52 2.59 2.57 2.62 2.56
$ALG-1$ 40 9.57 9.7 9.77 9.78 9.86 9.74
$ALG-2$ 10.04 9.96 9.9 9.86 9.86 9.92
$ALG-3$ 3.12 3.03 3.05 3.07 3.05 3.06
$ALG-4$ 5.25 5.17 5.14 5.11 5.12 5.16
$ALG-5$ 5.01 5.03 5.05 5.11 5.1 5.06
$ALG-6$ 0.08 0.01 0 0 0 0.02
$ALG-7$ 4.47 4.2 4.21 4.24 4.19 4.26
$ALG-8$ 2.69 2.6 2.73 2.71 2.7 2.69
Avg. 4.91 4.86 4.88 4.89 4.91
Table 5.  Error of Algoithms with respect to $ n $
n
Algorithm 100 200 300 400 500 Avg.
$ALG-1$ 9.27 9.39 9.45 9.47 9.51 9.42
$ALG-2$ 9.64 9.58 9.56 9.58 9.57 9.59
$ALG-3$ 3.07 3.02 3.03 3.05 3.05 3.04
$ALG-4$ 5.09 5.02 5.01 5.02 5.01 5.03
$ALG-5$ 4.88 4.92 4.94 4.97 4.99 4.94
$ALG-6$ 0.09 0.02 0.00 0.00 0.00 0.02
$ALG-7$ 4.28 4.14 4.10 4.10 4.07 4.14
$ALG-8$ 2.49 2.49 2.53 2.55 2.56 2.52
Table Options

Download as excel

n
Algorithm 100 200 300 400 500 Avg.
$ALG-1$ 9.27 9.39 9.45 9.47 9.51 9.42
$ALG-2$ 9.64 9.58 9.56 9.58 9.57 9.59
$ALG-3$ 3.07 3.02 3.03 3.05 3.05 3.04
$ALG-4$ 5.09 5.02 5.01 5.02 5.01 5.03
$ALG-5$ 4.88 4.92 4.94 4.97 4.99 4.94
$ALG-6$ 0.09 0.02 0.00 0.00 0.00 0.02
$ALG-7$ 4.28 4.14 4.10 4.10 4.07 4.14
$ALG-8$ 2.49 2.49 2.53 2.55 2.56 2.52
Table 6.  Error of Algorithms with respect to $ H $
$H$
Algorithm 20 30 40 Avg.
$ALG-1$ 9.24 9.42 9.59 9.42
$ALG-2$ 9.40 9.60 9.77 9.59
$ALG-3$ 3.01 3.06 3.06 3.04
$ALG-4$ 4.94 5.04 5.10 5.03
$ALG-5$ 4.86 4.94 5.01 4.94
$ALG-6$ 0.02 0.02 0.02 0.02
$ALG-7$ 4.10 4.13 4.19 4.14
$ALG-8$ 2.46 2.52 2.59 2.52
Table Options

Download as excel

$H$
Algorithm 20 30 40 Avg.
$ALG-1$ 9.24 9.42 9.59 9.42
$ALG-2$ 9.40 9.60 9.77 9.59
$ALG-3$ 3.01 3.06 3.06 3.04
$ALG-4$ 4.94 5.04 5.10 5.03
$ALG-5$ 4.86 4.94 5.01 4.94
$ALG-6$ 0.02 0.02 0.02 0.02
$ALG-7$ 4.10 4.13 4.19 4.14
$ALG-8$ 2.46 2.52 2.59 2.52
Table 7.  Confidence Intervals for Average Errors
Algorithm 95$\%$ Confidence Interval on the Avg. Error
$ALG-1$ (09.33-9.51)
$ALG-2$ (9.50-9.68)
$ALG-3$ (2.98-3.11)
$ALG-4$ (4.95-5.10)
$ALG-5$ (4.86-5.01)
$ALG-6$ (0.02-0.03)
$ALG-7$ (4.07-4.21)
$ALG-8$ (2.45-2.59)
Table Options

Download as excel

Algorithm 95$\%$ Confidence Interval on the Avg. Error
$ALG-1$ (09.33-9.51)
$ALG-2$ (9.50-9.68)
$ALG-3$ (2.98-3.11)
$ALG-4$ (4.95-5.10)
$ALG-5$ (4.86-5.01)
$ALG-6$ (0.02-0.03)
$ALG-7$ (4.07-4.21)
$ALG-8$ (2.45-2.59)
[1]

P. Liu, Xiwen Lu. Online scheduling of two uniform machines to minimize total completion times. Journal of Industrial & Management Optimization, 2009, 5 (1) : 95-102. doi: 10.3934/jimo.2009.5.95
[2]

Hongtruong Pham, Xiwen Lu. The inverse parallel machine scheduling problem with minimum total completion time. Journal of Industrial & Management Optimization, 2014, 10 (2) : 613-620. doi: 10.3934/jimo.2014.10.613
[3]

Ran Ma, Jiping Tao. An improved 2.11-competitive algorithm for online scheduling on parallel machines to minimize total weighted completion time. Journal of Industrial & Management Optimization, 2018, 14 (2) : 497-510. doi: 10.3934/jimo.2017057
[4]

Jinjiang Yuan, Weiping Shang. A PTAS for the p-batch scheduling with pj = p to minimize total weighted completion time. Journal of Industrial & Management Optimization, 2005, 1 (3) : 353-358. doi: 10.3934/jimo.2005.1.353
[5]

Chengxin Luo. Single machine batch scheduling problem to minimize makespan with controllable setup and jobs processing times. Numerical Algebra, Control & Optimization, 2015, 5 (1) : 71-77. doi: 10.3934/naco.2015.5.71
[6]

Xingong Zhang. Single machine and flowshop scheduling problems with sum-of-processing time based learning phenomenon. Journal of Industrial & Management Optimization, 2020, 16 (1) : 231-244. doi: 10.3934/jimo.2018148
[7]

Reza Alizadeh Foroutan, Javad Rezaeian, Milad Shafipour. Bi-objective unrelated parallel machines scheduling problem with worker allocation and sequence dependent setup times considering machine eligibility and precedence constraints. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021190
[8]

Muberra Allahverdi, Harun Aydilek, Asiye Aydilek, Ali Allahverdi. A better dominance relation and heuristics for Two-Machine No-Wait Flowshops with Maximum Lateness Performance Measure. Journal of Industrial & Management Optimization, 2021, 17 (4) : 1973-1991. doi: 10.3934/jimo.2020054
[9]

Shuang Zhao. Resource allocation flowshop scheduling with learning effect and slack due window assignment. Journal of Industrial & Management Optimization, 2021, 17 (5) : 2817-2835. doi: 10.3934/jimo.2020096
[10]

Wen-Hung Wu, Yunqiang Yin, Wen-Hsiang Wu, Chin-Chia Wu, Peng-Hsiang Hsu. A time-dependent scheduling problem to minimize the sum of the total weighted tardiness among two agents. Journal of Industrial & Management Optimization, 2014, 10 (2) : 591-611. doi: 10.3934/jimo.2014.10.591
[11]

Ping Yan, Ji-Bo Wang, Li-Qiang Zhao. Single-machine bi-criterion scheduling with release times and exponentially time-dependent learning effects. Journal of Industrial & Management Optimization, 2019, 15 (3) : 1117-1131. doi: 10.3934/jimo.2018088
[12]

Feng Zhang, Jinting Wang, Bin Liu. Equilibrium joining probabilities in observable queues with general service and setup times. Journal of Industrial & Management Optimization, 2013, 9 (4) : 901-917. doi: 10.3934/jimo.2013.9.901
[13]

Tuan Phung-Duc. Single server retrial queues with setup time. Journal of Industrial & Management Optimization, 2017, (3) : 1329-1345. doi: 10.3934/jimo.2016075
[14]

Ji-Bo Wang, Bo Zhang, Hongyu He. A unified analysis for scheduling problems with variable processing times. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021008
[15]

Tuan Phung-Duc, Ken'ichi Kawanishi. Multiserver retrial queue with setup time and its application to data centers. Journal of Industrial & Management Optimization, 2019, 15 (1) : 15-35. doi: 10.3934/jimo.2018030
[16]

Güvenç Şahin, Ravindra K. Ahuja. Single-machine scheduling with stepwise tardiness costs and release times. Journal of Industrial & Management Optimization, 2011, 7 (4) : 825-848. doi: 10.3934/jimo.2011.7.825
[17]

Mehmet Duran Toksari, Emel Kizilkaya Aydogan, Berrin Atalay, Saziye Sari. Some scheduling problems with sum of logarithm processing times based learning effect and exponential past sequence dependent delivery times. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021044
[18]

A. Azhagappan, T. Deepa. Transient analysis of N-policy queue with system disaster repair preventive maintenance re-service balking closedown and setup times. Journal of Industrial & Management Optimization, 2020, 16 (6) : 2843-2856. doi: 10.3934/jimo.2019083
[19]

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
[20]

Hideaki Takagi. Times until service completion and abandonment in an M/M/$ m$ preemptive-resume LCFS queue with impatient customers. Journal of Industrial & Management Optimization, 2018, 14 (4) : 1701-1726. doi: 10.3934/jimo.2018028

2020 Impact Factor: 1.801

Tools

Metrics

  • PDF downloads (578)
  • HTML views (772)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy