Markov Processes in Modeling Life Cycle of Economic Clusters

The object is economic clusters' life cycle (ECLC). The aim is to model its regulations algorithm. The hypothesis: ECLC transitions are stochastic processes independent of past. The method is Markov processes with discrete time. The algorithm is implemented in programming language Eсlipse. The results are: 1) mathematical model of ECLC with life-cycle stages, stochastic transitions, transition values. For mature clusters their borders and actors are defined; 2) ECLC modeling algorithm with given stochastic transition matrix considering influence of different environments; 3) software "Modeling _Cluster_v1" for Androids to model online cluster development based on ECLC regulations. The novelties: for the first time ECLC model considers stochastic character of life-cycle stages simulating the number of actors at each stage, cluster’s size, diameter and delimitation. Practical application: Program is applied to education, for interest groups involved in cluster development and projects to model of clusters’ evolution trajectories under different environmental

[1]  Mark P. Van Oyen,et al.  Dynamic control of a closed two-stage queueing network for outfitting process in shipbuilding , 2016, Comput. Oper. Res..

[2]  Kiyoshi Kobayashi,et al.  A competing Markov model for cracking prediction on civil structures , 2014 .

[3]  白洋.  Desirable policies of a strategic petroleum reserve in coping with disruption risk: A Markov decision process approach , 2015 .

[4]  Urtzi Ayesta,et al.  Optimal dynamic resource allocation to prevent defaults , 2016, Oper. Res. Lett..

[5]  José David Martín-Guerrero,et al.  Optimization of anemia treatment in hemodialysis patients via reinforcement learning , 2014, Artif. Intell. Medicine.

[6]  Chen Xu,et al.  Dynamic capacity management with uncertain demand and dynamic price , 2016 .

[7]  Graçaliz Pereira Dimuro,et al.  Regulating social exchanges in open MAS: The problem of reciprocal conversions between POMDPs and HMMs , 2015, Inf. Sci..

[8]  Anders Ringgaard Kristensen,et al.  From biological models to economic optimization. , 2015, Preventive veterinary medicine.

[9]  Carles Mateu,et al.  A predictor model for the composting process on an industrial scale based on Markov processes , 2016, Environ. Model. Softw..

[10]  Kenneth Sörensen,et al.  Generating structured music for bagana using quality metrics based on Markov models , 2015, Expert Syst. Appl..

[11]  Martha A. Grover,et al.  Controlling assembly of colloidal particles into structured objects: Basic strategy and a case study , 2015 .

[12]  Raphaël Suire,et al.  Clusters for life or life cycles of clusters: in search of the critical factors of clusters' resilience , 2014 .

[13]  Xuesong Li,et al.  A Markov Chain-based quantitative study of angular distribution of photons through turbid slabs via isotropic light scattering , 2016, Comput. Phys. Commun..

[14]  Tristan Tomala,et al.  Approximate Implementation in Markovian Environments , 2015 .

[15]  Kerstin Press.  Divide to conquer? Limits to the adaptability of disintegrated, flexible specialization clusters , 2007 .

[16]  Andrew Popp,et al.  Life Cycles, Contingency, and Agency: Growth, Development, and Change in English Industrial Districts and Clusters , 2007 .

[17]  Bi-Huei Tsai,et al.  Cluster evolution of IC industry from Taiwan to China , 2009 .

[18]  Tatyana Chernonog,et al.  A two-state partially observable Markov decision process with three actions , 2016, Eur. J. Oper. Res..

[19]  Yu Zeng,et al.  Modelling of cluster supply network with cascading failure spread and its vulnerability analysis , 2014 .

[20]  W. Johnston,et al.  Perspectives of SME innovation clusters development in Russia , 2013 .

[21]  K. Mcdougall,et al.  Spatial modelling of natural disaster risk reduction policies with Markov decision processes , 2014 .

[22]  Bo Huang,et al.  Lending decisions with limits on capital available: The polygamous marriage problem , 2016, Eur. J. Oper. Res..

[23]  L. Wahl,et al.  Extinction probabilities and stationary distributions of mobile genetic elements in prokaryotes: The birth-death-diversification model. , 2015, Theoretical population biology.