Studi Tentang Penyelesaian Program Linier Stokastik Dengan Markov Chain
DAFTAR PUSTAKA
Adke S dan Deshmukh D (1988) Limit Distribution of a High Order Markov Chain,
Journal of Royal Statistical Society, Series B, 50:105-108.
Altman,E, (2008) Constrained cost-coupled stochastic games with independent state
processes, Operation Research Letter 36:160-164.
Billingsley, P. (1995). Prob. and measure, (Third ed.). New York, NY: WileyInterscience.
Bhat, U. Nurayan,” Element of Applied Stochastic Processes and markov Chain”,
John Wiley and Sons, Inc., Canada, 1984.
Boris D, Damien E dan Louis W (2001). Multistage Stochastic Programming:
A Scenario Tree Based Approach to planning under Uncertainty, European
Journal of Opertional Research, 15-22, B-4000 Liege, Belgium.
Bronson, Richard,” Theory and Problems of Operations Research”, Mc. Graw Hill,
Inc., 1982.
Buchholz P. (1995) Equivalence Relations for Stochastic Automata Networks. Computations of Markov chains: Proceedings of the 2nd international workshop
On numerical solutions of Markov chains. Kluwer, 197-216.
Bunke H dan Caelli T (2001) Hidden Markov models: applications in computer
vision, Editors, Horst Bunke, Terry Caelli, Singapore, World Scientific.
Chan R dan Ching W (2005) Boundary Value Methods for Solving Transient Solutions of Markovian Queueing Networks, Journal of Applied Mathematics
and Computations. to appear, 288-294
Ching W, Fung E dan Ng M (2002) A Multivariate Markov Chain Model for Categorical Data Sequences and Its Applications in Demand Predictions, IMA
Journal of Management Mathematics, 13:187-199.
Ching W, Fung E dan Ng M (2003) A Higher-order Markov Model for the Newsboy’s
Problem, Journal of Operational Research Society, 54:291-298.
Ching W dan Ng M. (2004) Building Simple Hidden Markov Models, International
Journal of Mathematical Education in Science and Engineering, 35:295-299.
Ching W, Ng M dan Wong K (2003) Higher-order Markov Decision Process and
Its Applications in Customer Lifetime Values, The 32nd International Conference on Computers and Industrial Engineering, Limerick, Ireland 2:821826.
42
Universitas Sumatera Utara
43
Ching W, Ng M, ”Markov Chains:
Hongkong, P.R. China, 2006
Models Algorithms and Applications”,
Dempster, M. A. H., Pflug, G.,Mitra, G. (Eds.). (2008). Quantitative Fund Management. Boca Raton, FL: Chapman,Hall/CRC.
Frantzeskakis L. F. dan Powell W. B. (1989) An improved polynomial bound for
the expected network recourse function. Technical report, Statistics and Operations Research Series, SOR-89-23, Princeton University, Princeton, New
Jersey.
Hoffman A. J. (1960) Some recent applications of the theory of a multivariate
random variable, Proc. Symp. Appl. Math. 10:113-128.
Krajewski, Lee J.,” Linier Programming and Extensions”, Mc. Graw-Hill, Inc.,
1981.
P. Kall, dan Wallace W Stein ,” Stohastic Programming”, NewYork, 1994.
P. Kall dan J. Mayer. (2000) On the role of bounds in stochastic linear programming.
Optimization, European Journal of Operational Research, 47:287-301.
Kouwenberg, RRP, (2001). Scenario generation and stochastic programming models
for asset liability management, European Journal of Opertional Research
134(2), 51-64.
Pomar, J. (2003) A Markov Decision Sow Model Representing the Productive Lifespan of Herd Sows, Agricultural Systems.76, 253-272.
Lechuga, P Surez, (2006). Stochastic linier programming to optimize some stochastic systems. European Journal of Operational Research, 313-318.
Lu L, Ching W dan Ng M (2004) Exact Algorithms for Singular Tridiagonal Systems with Applications to Markov Chains, Journal of Applied Mathematics
and Computation, 159:275-289.
Mulvey J. M. dan Vladimirou H. (1989) Evaluation of a parallel hedging algorithm
for stochastic network programming. In Sharda R., Golden B. L., Wasil E.,
Balci O., and Stewart W. (eds) Impact of Recent Computer Advances on
Operations Research, 106-119. North- Holland, New York.
Mulvey J. M. dan Vladimirou H. (1991) Applying the progressive hedging algorithm
to stochastic generalized networks. Ann. Oper. Res. 31:399- 424.
Olafsson, Sigurdur., ” Introduction to Stochastic Analysis”, NewYork, 2000.
Ross, Sheldon M.,”Stochastic Processes”, John Wiley and Sons, Canada, 1983.
Universitas Sumatera Utara
44
Meyn S dan Tweedie L Richard., Markov Chain and Stochastic Stability”, Cambridge. 2009.
Taha, Hamdy A.,” Operations Research An Introduction”, Macmillan Publ.,Co.,
New York, 1992.
Wallace S. W. (1986) Solving stochastic programs with network recourse. Networks
16:295-317.
Wallace S. W. dan Wets Rockafellar. (1995) Preprocessing in stochastic programming: The case of capacitated networks. Comp. 7:44-62.
Universitas Sumatera Utara
Adke S dan Deshmukh D (1988) Limit Distribution of a High Order Markov Chain,
Journal of Royal Statistical Society, Series B, 50:105-108.
Altman,E, (2008) Constrained cost-coupled stochastic games with independent state
processes, Operation Research Letter 36:160-164.
Billingsley, P. (1995). Prob. and measure, (Third ed.). New York, NY: WileyInterscience.
Bhat, U. Nurayan,” Element of Applied Stochastic Processes and markov Chain”,
John Wiley and Sons, Inc., Canada, 1984.
Boris D, Damien E dan Louis W (2001). Multistage Stochastic Programming:
A Scenario Tree Based Approach to planning under Uncertainty, European
Journal of Opertional Research, 15-22, B-4000 Liege, Belgium.
Bronson, Richard,” Theory and Problems of Operations Research”, Mc. Graw Hill,
Inc., 1982.
Buchholz P. (1995) Equivalence Relations for Stochastic Automata Networks. Computations of Markov chains: Proceedings of the 2nd international workshop
On numerical solutions of Markov chains. Kluwer, 197-216.
Bunke H dan Caelli T (2001) Hidden Markov models: applications in computer
vision, Editors, Horst Bunke, Terry Caelli, Singapore, World Scientific.
Chan R dan Ching W (2005) Boundary Value Methods for Solving Transient Solutions of Markovian Queueing Networks, Journal of Applied Mathematics
and Computations. to appear, 288-294
Ching W, Fung E dan Ng M (2002) A Multivariate Markov Chain Model for Categorical Data Sequences and Its Applications in Demand Predictions, IMA
Journal of Management Mathematics, 13:187-199.
Ching W, Fung E dan Ng M (2003) A Higher-order Markov Model for the Newsboy’s
Problem, Journal of Operational Research Society, 54:291-298.
Ching W dan Ng M. (2004) Building Simple Hidden Markov Models, International
Journal of Mathematical Education in Science and Engineering, 35:295-299.
Ching W, Ng M dan Wong K (2003) Higher-order Markov Decision Process and
Its Applications in Customer Lifetime Values, The 32nd International Conference on Computers and Industrial Engineering, Limerick, Ireland 2:821826.
42
Universitas Sumatera Utara
43
Ching W, Ng M, ”Markov Chains:
Hongkong, P.R. China, 2006
Models Algorithms and Applications”,
Dempster, M. A. H., Pflug, G.,Mitra, G. (Eds.). (2008). Quantitative Fund Management. Boca Raton, FL: Chapman,Hall/CRC.
Frantzeskakis L. F. dan Powell W. B. (1989) An improved polynomial bound for
the expected network recourse function. Technical report, Statistics and Operations Research Series, SOR-89-23, Princeton University, Princeton, New
Jersey.
Hoffman A. J. (1960) Some recent applications of the theory of a multivariate
random variable, Proc. Symp. Appl. Math. 10:113-128.
Krajewski, Lee J.,” Linier Programming and Extensions”, Mc. Graw-Hill, Inc.,
1981.
P. Kall, dan Wallace W Stein ,” Stohastic Programming”, NewYork, 1994.
P. Kall dan J. Mayer. (2000) On the role of bounds in stochastic linear programming.
Optimization, European Journal of Operational Research, 47:287-301.
Kouwenberg, RRP, (2001). Scenario generation and stochastic programming models
for asset liability management, European Journal of Opertional Research
134(2), 51-64.
Pomar, J. (2003) A Markov Decision Sow Model Representing the Productive Lifespan of Herd Sows, Agricultural Systems.76, 253-272.
Lechuga, P Surez, (2006). Stochastic linier programming to optimize some stochastic systems. European Journal of Operational Research, 313-318.
Lu L, Ching W dan Ng M (2004) Exact Algorithms for Singular Tridiagonal Systems with Applications to Markov Chains, Journal of Applied Mathematics
and Computation, 159:275-289.
Mulvey J. M. dan Vladimirou H. (1989) Evaluation of a parallel hedging algorithm
for stochastic network programming. In Sharda R., Golden B. L., Wasil E.,
Balci O., and Stewart W. (eds) Impact of Recent Computer Advances on
Operations Research, 106-119. North- Holland, New York.
Mulvey J. M. dan Vladimirou H. (1991) Applying the progressive hedging algorithm
to stochastic generalized networks. Ann. Oper. Res. 31:399- 424.
Olafsson, Sigurdur., ” Introduction to Stochastic Analysis”, NewYork, 2000.
Ross, Sheldon M.,”Stochastic Processes”, John Wiley and Sons, Canada, 1983.
Universitas Sumatera Utara
44
Meyn S dan Tweedie L Richard., Markov Chain and Stochastic Stability”, Cambridge. 2009.
Taha, Hamdy A.,” Operations Research An Introduction”, Macmillan Publ.,Co.,
New York, 1992.
Wallace S. W. (1986) Solving stochastic programs with network recourse. Networks
16:295-317.
Wallace S. W. dan Wets Rockafellar. (1995) Preprocessing in stochastic programming: The case of capacitated networks. Comp. 7:44-62.
Universitas Sumatera Utara