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January  2020, 7(1): 21-35. doi: 10.3934/jdg.2020002

Sequencing grey games

Serap Ergün 1, , Osman Palanci 2, , Sirma Zeynep Alparslan Gök 3,, , Şule Nizamoğlu 3, and  Gerhard Wilhelm Weber 4,

1. 

Isparta University of Applied Sciences, Faculty of Technology, Department of Computer Engineering, Isparta, Turkey
2. 

Süleyman Demirel University, Faculty of Economics and Business Administration, Department of Business Administration, Isparta, Turkey
3. 

Süleyman Demirel University, Faculty of Arts and Sciences, Department of Mathematics, Isparta, Turkey
4. 

Poznan University of Technology, Chair of Marketing and Economic Engineering, Poznan, Poland

* Corresponding author: zeynepalparslan@yahoo.com

Received  December 2018 Published  December 2019

The job scheduling problem is a notoriously difficult problem in combinatorial optimization and Operational Research. In this study, we handle the job scheduling problem by using a cooperative game theoretical approach. In the sequel, sequencing situations arising grom grey uncertainty are considered. Cooperative grey game theory is applied to analyze these situations. Further, grey sequencing games are constructed and grey equal gain splitting (GEGS) rule is introduced. It is shown that cooperative grey games are convex. An application is given based on Priority Based Scheduling Algorithm. The paper ends with a conclusion.

Keywords: Sequencing games, grey data, grey games, cooperative game theory, scheduling algorithm.
Mathematics Subject Classification: Primary: 91A12.
Citation: Serap Ergün, Osman Palanci, Sirma Zeynep Alparslan Gök, Şule Nizamoğlu, Gerhard Wilhelm Weber. Sequencing grey games. Journal of Dynamics & Games, 2020, 7 (1) : 21-35. doi: 10.3934/jdg.2020002
References:
[1]

S. Z. Alparslan Gök, R. Branzei, V. Fragnelli and S. Tijs, Sequencing interval situations and related games, CEJOR Cent. Eur. J. Oper. Res., 21 (2013), 225-236.  doi: 10.1007/s10100-011-0226-3.  Google Scholar

[2]

P. Borm, H. Hamers and R. Hendrickx, Operations research games: A survey, Top, 9 (2001), 139-216.  doi: 10.1007/BF02579075.  Google Scholar

[3]

M. E. Bruni, L. D. P. Pugliese, P. Beraldi and F. Guerriero, An adjustable robust optimization model for the resource-constrained project scheduling problem with uncertain activity durations, Omega, (2016), in press. Google Scholar

[4]

P. Calleja, M. A. Estevez-Fernandez, P. Borm and H. Hamers, Job scheduling, cooperation, and control, Operations Research Letters, 34 (2006), 22-28.  doi: 10.1016/j.orl.2005.01.007.  Google Scholar

[5]

I. Curiel, G. Pederzoli and S. Tijs, Sequencing games, European Journal of Operational Research, 40 (1989), 344-351.  doi: 10.1016/0377-2217(89)90427-X.  Google Scholar

[6]

I. Curiel, H. Hamers and F. Klijn, Sequencing games: A survey, Chapters in Game Theory, Theory Decis. Lib. Ser. C Game Theory Math. Program. Oper. Res., Kluwer Acad. Publ., Boston, MA, 31 (2002), 27-50.  doi: 10.1007/0-306-47526-X_2.  Google Scholar

[7]

J.-L. Deng, Control problems of grey systems, Systems and Control Letters, 1 (1981/82), 288-294. doi: 10.1016/S0167-6911(82)80025-X.  Google Scholar

[8]

D. G. Feitelson, L. Rudolph, U. Schwiegelshohn, K. C. Sevcik and P. Wong, Theory and practice in parallel job scheduling, Workshop on Job Scheduling Strategies for Parallel Processing, Springer Berlin Heidelberg, (1997), 1–34. Google Scholar

[9]

E. Köse and J. Y.-L. Forrest, N-person grey game, Kybernetes, 44 (2015), 271-282.  doi: 10.1108/K-04-2014-0073.  Google Scholar

[10]

E. L. Lawler, J. K. Lenstra, A. H. R. Kan and D. B. Shmoys, Sequencing and scheduling: Algorithms and complexity, Handbooks in Operations Research and Management Science, 4 (1993), 445-522.   Google Scholar

[11]

I. S. Lee and S. H. Yoon, Coordinated scheduling of production and delivery stages with stage-dependent inventory holding costs, Omega, 38 (2010), 509-521.   Google Scholar

[12]

R. E. Moore, Methods and Applications of Interval Analysis, SIAM Studies in Applied Mathematics, 2. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, Pa., 1979.  Google Scholar

[13]

M. O. Olgun, S. Z. Alparslan Gök and G. Özdemir, Cooperative grey games and an application on economic order quantity model, Kybernetes, 45 (2016), 828-838.  doi: 10.1108/K-06-2015-0160.  Google Scholar

[14]

O. Palancı, S. Z. Alparslan Gök, S. Ergün and G.-W. Weber, Cooperative grey games and grey Shapley value, Optimization, 64 (2015), 1657-1668.  doi: 10.1080/02331934.2014.956743.  Google Scholar

[15]

R. Ramasesh, Dynamic job shop scheduling: A survey of simulation research, Omega, 18 (1990), 43-57.   Google Scholar

[16]

S. K. Roy, G. Maity and G.-W. Weber, Multi-objective two-stage grey transportation problem using utility function with goals, CEJOR Cent. Eur. J. Oper. Res., 25 (2017), 417-439.  doi: 10.1007/s10100-016-0464-5.  Google Scholar

[17]

W. E. Smith, Various optimizer for single-stage production, Naval Research Logistics Quarterly, 3 (1956), 59-66.  doi: 10.1002/nav.3800030106.  Google Scholar

[18]

W. Stallings and G. K. Paul, Operating systems: Internals and design principles, Upper Saddle River, NJ: Prentice Hall, 3 (1998). Google Scholar

[19]

H. Wu and Z. Fang, The graphical solution of zero-sum two-person grey games, Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, 1/2 (2007), 1617-1620.   Google Scholar

show all references

References:
[1]

S. Z. Alparslan Gök, R. Branzei, V. Fragnelli and S. Tijs, Sequencing interval situations and related games, CEJOR Cent. Eur. J. Oper. Res., 21 (2013), 225-236.  doi: 10.1007/s10100-011-0226-3.  Google Scholar

[2]

P. Borm, H. Hamers and R. Hendrickx, Operations research games: A survey, Top, 9 (2001), 139-216.  doi: 10.1007/BF02579075.  Google Scholar

[3]

M. E. Bruni, L. D. P. Pugliese, P. Beraldi and F. Guerriero, An adjustable robust optimization model for the resource-constrained project scheduling problem with uncertain activity durations, Omega, (2016), in press. Google Scholar

[4]

P. Calleja, M. A. Estevez-Fernandez, P. Borm and H. Hamers, Job scheduling, cooperation, and control, Operations Research Letters, 34 (2006), 22-28.  doi: 10.1016/j.orl.2005.01.007.  Google Scholar

[5]

I. Curiel, G. Pederzoli and S. Tijs, Sequencing games, European Journal of Operational Research, 40 (1989), 344-351.  doi: 10.1016/0377-2217(89)90427-X.  Google Scholar

[6]

I. Curiel, H. Hamers and F. Klijn, Sequencing games: A survey, Chapters in Game Theory, Theory Decis. Lib. Ser. C Game Theory Math. Program. Oper. Res., Kluwer Acad. Publ., Boston, MA, 31 (2002), 27-50.  doi: 10.1007/0-306-47526-X_2.  Google Scholar

[7]

J.-L. Deng, Control problems of grey systems, Systems and Control Letters, 1 (1981/82), 288-294. doi: 10.1016/S0167-6911(82)80025-X.  Google Scholar

[8]

D. G. Feitelson, L. Rudolph, U. Schwiegelshohn, K. C. Sevcik and P. Wong, Theory and practice in parallel job scheduling, Workshop on Job Scheduling Strategies for Parallel Processing, Springer Berlin Heidelberg, (1997), 1–34. Google Scholar

[9]

E. Köse and J. Y.-L. Forrest, N-person grey game, Kybernetes, 44 (2015), 271-282.  doi: 10.1108/K-04-2014-0073.  Google Scholar

[10]

E. L. Lawler, J. K. Lenstra, A. H. R. Kan and D. B. Shmoys, Sequencing and scheduling: Algorithms and complexity, Handbooks in Operations Research and Management Science, 4 (1993), 445-522.   Google Scholar

[11]

I. S. Lee and S. H. Yoon, Coordinated scheduling of production and delivery stages with stage-dependent inventory holding costs, Omega, 38 (2010), 509-521.   Google Scholar

[12]

R. E. Moore, Methods and Applications of Interval Analysis, SIAM Studies in Applied Mathematics, 2. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, Pa., 1979.  Google Scholar

[13]

M. O. Olgun, S. Z. Alparslan Gök and G. Özdemir, Cooperative grey games and an application on economic order quantity model, Kybernetes, 45 (2016), 828-838.  doi: 10.1108/K-06-2015-0160.  Google Scholar

[14]

O. Palancı, S. Z. Alparslan Gök, S. Ergün and G.-W. Weber, Cooperative grey games and grey Shapley value, Optimization, 64 (2015), 1657-1668.  doi: 10.1080/02331934.2014.956743.  Google Scholar

[15]

R. Ramasesh, Dynamic job shop scheduling: A survey of simulation research, Omega, 18 (1990), 43-57.   Google Scholar

[16]

S. K. Roy, G. Maity and G.-W. Weber, Multi-objective two-stage grey transportation problem using utility function with goals, CEJOR Cent. Eur. J. Oper. Res., 25 (2017), 417-439.  doi: 10.1007/s10100-016-0464-5.  Google Scholar

[17]

W. E. Smith, Various optimizer for single-stage production, Naval Research Logistics Quarterly, 3 (1956), 59-66.  doi: 10.1002/nav.3800030106.  Google Scholar

[18]

W. Stallings and G. K. Paul, Operating systems: Internals and design principles, Upper Saddle River, NJ: Prentice Hall, 3 (1998). Google Scholar

[19]

H. Wu and Z. Fang, The graphical solution of zero-sum two-person grey games, Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, 1/2 (2007), 1617-1620.   Google Scholar

Figure 1.  An illustration of our application
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Figure 2.  Gantt charts of D1
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Figure 3.  Gantt charts of D2
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Figure 4.  Gantt charts of D3
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Table 1.  The properties of each jobs of D1
Job Arrival Time Execute Time Priority Service Time
J1 $\left[ 0, 1\right] $ $\left[ 2, 2\right] $ 1 $\left[ 95, 101\right] $
J2 $\left[ 1, 3\right] $ $\left[ 3, 3\right] $ 2 $\left[ 191, 198\right] $
J3 $\left[ 3, 4\right] $ $\left[ 5, 5\right] $ 3 $\left[ 288, 294\right] $
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Job Arrival Time Execute Time Priority Service Time
J1 $\left[ 0, 1\right] $ $\left[ 2, 2\right] $ 1 $\left[ 95, 101\right] $
J2 $\left[ 1, 3\right] $ $\left[ 3, 3\right] $ 2 $\left[ 191, 198\right] $
J3 $\left[ 3, 4\right] $ $\left[ 5, 5\right] $ 3 $\left[ 288, 294\right] $
Table 2.  The properties of each jobs of D2
Job Arrival Time Execute Time Priority Service Time
J1 $\left[ 3, 5\right] $ $\left[ 3, 5\right] $ 2 $\left[ 153, 160\right] $
J2 $\left[ 0, 2\right] $ $\left[ 4, 6\right] $ 1 $\left[ 120, 127\right] $
J3 $\left[ 6, 8\right] $ $\left[ 7, 9\right] $ 3 $\left[ 186, 193\right] $
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Job Arrival Time Execute Time Priority Service Time
J1 $\left[ 3, 5\right] $ $\left[ 3, 5\right] $ 2 $\left[ 153, 160\right] $
J2 $\left[ 0, 2\right] $ $\left[ 4, 6\right] $ 1 $\left[ 120, 127\right] $
J3 $\left[ 6, 8\right] $ $\left[ 7, 9\right] $ 3 $\left[ 186, 193\right] $
Table 3.  The properties of each jobs of D3
Job Arrival Time Execute Time Priority Service Time
J1 $\left[ 2, 4\right] $ $\left[ 2, 2\right] $ 2 $\left[ 124, 132\right] $
J2 $\left[ 4, 7\right] $ $\left[ 3, 3\right] $ 3 $\left[ 152, 160\right] $
J3 $\left[ 0, 3\right] $ $\left[ 4, 4\right] $ 1 $\left[ 90, 98\right] $
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Job Arrival Time Execute Time Priority Service Time
J1 $\left[ 2, 4\right] $ $\left[ 2, 2\right] $ 2 $\left[ 124, 132\right] $
J2 $\left[ 4, 7\right] $ $\left[ 3, 3\right] $ 3 $\left[ 152, 160\right] $
J3 $\left[ 0, 3\right] $ $\left[ 4, 4\right] $ 1 $\left[ 90, 98\right] $
Table 4.  The wait time t of each jobs of D1, D2 and D3
$ \textbf{Job (Process)}$ $\textbf{Wait Time}$
J1 of D1 $t_{11} = \left[ 95, 100\right] $
J2 of D1 $t_{12} = \left[ 180, 195\right] $
J3 of D1 $t_{13} = \left[ 285, 290\right] $
J1 of D2 $t_{21} = \left[ 150, 155\right] $
J2 of D2 $t_{22} = \left[ 120, 125\right] $
J3 of D2 $t_{23} = \left[ 180, 185\right] $
J1 of D3 $t_{31} = \left[ 120, 125\right] $
J2 of D3 $t_{32} = \left[ 150, 155\right] $
J3 of D3 $t_{33} = \left[ 90, 95\right] $
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$ \textbf{Job (Process)}$ $\textbf{Wait Time}$
J1 of D1 $t_{11} = \left[ 95, 100\right] $
J2 of D1 $t_{12} = \left[ 180, 195\right] $
J3 of D1 $t_{13} = \left[ 285, 290\right] $
J1 of D2 $t_{21} = \left[ 150, 155\right] $
J2 of D2 $t_{22} = \left[ 120, 125\right] $
J3 of D2 $t_{23} = \left[ 180, 185\right] $
J1 of D3 $t_{31} = \left[ 120, 125\right] $
J2 of D3 $t_{32} = \left[ 150, 155\right] $
J3 of D3 $t_{33} = \left[ 90, 95\right] $
Table 5.  The weights of c, d, n of J1 for D1, D2, D3
$ \textbf{Property of job} $ $\textbf{Compute Intensity} $ $ \textbf{Data parsing}$ $\textbf{Network}$
cost $c$ $d$ $n$
J1D1 3 2 1
J2D1 2 3 1
J3D1 1 2 3
J1D2 3 2 1
J2D2 1 3 2
J3D2 1 2 3
J1D3 3 1 2
J2D3 2 3 1
J3D3 1 1 1
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$ \textbf{Property of job} $ $\textbf{Compute Intensity} $ $ \textbf{Data parsing}$ $\textbf{Network}$
cost $c$ $d$ $n$
J1D1 3 2 1
J2D1 2 3 1
J3D1 1 2 3
J1D2 3 2 1
J2D2 1 3 2
J3D2 1 2 3
J1D3 3 1 2
J2D3 2 3 1
J3D3 1 1 1
Table 6.  Grey marginal vectors
$\sigma $ $m_{1}^{\sigma }\left( w^{\prime }\right) $ $m_{2}^{\sigma }\left( w^{\prime }\right) $ $m_{3}^{\sigma }\left( w^{\prime }\right) $
$\sigma _{1} = \left( 1, 2, 3\right) $ $m_{1}^{\sigma _{1}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{2}^{\sigma _{1}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{3}^{\sigma _{1}}\left( w^{\prime }\right) \in \left[ 68500, 72850\right] $
$\sigma _{2} = \left( 1, 3, 2\right) $ $m_{1}^{\sigma _{2}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{2}^{\sigma _{2}}\left( w^{\prime }\right) \in \left[ 68500, 72850\right] $ $m_{3}^{\sigma _{2}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $
$\sigma _{3} = \left( 2, 1, 3\right) $ $m_{1}^{\sigma _{3}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{2}^{\sigma _{3}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{3}^{\sigma _{3}}\left( w^{\prime }\right) \in \left[ 68500, 72850\right] $
$\sigma _{4} = \left( 2, 3, 1\right) $ $m_{1}^{\sigma _{4}}\left( w^{\prime }\right) \in \left[ 26500, 28050\right] $ $m_{2}^{\sigma _{4}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{3}^{\sigma _{4}}\left( w^{\prime }\right) \in \left[ 42000, 44800\right] $
$\sigma _{5} = \left( 3, 1, 2\right) $ $m_{1}^{\sigma _{5}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{2}^{\sigma _{5}}\left( w^{\prime }\right) \in \left[ 68500, 72850\right] $ $m_{3}^{\sigma _{5}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $
$\sigma _{6} = \left( 3, 2, 1\right) $ $m_{1}^{\sigma _{6}}\left( w^{\prime }\right) \in \left[ 26500, 28050\right] $ $m_{2}^{\sigma _{6}}\left( w^{\prime }\right) \in \left[ 42000, 44800\right] $ $m_{3}^{\sigma _{6}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $
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$\sigma $ $m_{1}^{\sigma }\left( w^{\prime }\right) $ $m_{2}^{\sigma }\left( w^{\prime }\right) $ $m_{3}^{\sigma }\left( w^{\prime }\right) $
$\sigma _{1} = \left( 1, 2, 3\right) $ $m_{1}^{\sigma _{1}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{2}^{\sigma _{1}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{3}^{\sigma _{1}}\left( w^{\prime }\right) \in \left[ 68500, 72850\right] $
$\sigma _{2} = \left( 1, 3, 2\right) $ $m_{1}^{\sigma _{2}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{2}^{\sigma _{2}}\left( w^{\prime }\right) \in \left[ 68500, 72850\right] $ $m_{3}^{\sigma _{2}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $
$\sigma _{3} = \left( 2, 1, 3\right) $ $m_{1}^{\sigma _{3}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{2}^{\sigma _{3}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{3}^{\sigma _{3}}\left( w^{\prime }\right) \in \left[ 68500, 72850\right] $
$\sigma _{4} = \left( 2, 3, 1\right) $ $m_{1}^{\sigma _{4}}\left( w^{\prime }\right) \in \left[ 26500, 28050\right] $ $m_{2}^{\sigma _{4}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{3}^{\sigma _{4}}\left( w^{\prime }\right) \in \left[ 42000, 44800\right] $
$\sigma _{5} = \left( 3, 1, 2\right) $ $m_{1}^{\sigma _{5}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $ $m_{2}^{\sigma _{5}}\left( w^{\prime }\right) \in \left[ 68500, 72850\right] $ $m_{3}^{\sigma _{5}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $
$\sigma _{6} = \left( 3, 2, 1\right) $ $m_{1}^{\sigma _{6}}\left( w^{\prime }\right) \in \left[ 26500, 28050\right] $ $m_{2}^{\sigma _{6}}\left( w^{\prime }\right) \in \left[ 42000, 44800\right] $ $m_{3}^{\sigma _{6}}\left( w^{\prime }\right) \in \left[ 0, 0\right] $
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