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Abstract
A stochastic service system of finite size M is comprised of identical service facilities, including or not a waiting queue, which simultaneously treats N customers, N ∈ {0, 1, …, M}. Depending on the concepts of system information i and system entropy S = E(i), we promote a risk assessment procedure. By definition, the system entropy is the uncertainty associated with the system, and the system expected loss is the risk associated with the system. Thus, accepting the system information as loss function, we can identify risk and uncertainty, associated with the system, using the entropy as risk function. Further, we differ risk of the system (i.e., risk observed by an outside observer), risk observed by an arriving customer, and risk observed by a departing customer, giving a separate expression for each one. Then, these risks are compared with each other, when the system has the same average number E(N) of customers seen by any viewpoint. The three risk types (together with the three customer means) allow us to distinguish two systems obeying the same probability distribution. This approach enables system operators to choose suitable values for system utilization and size, in view of the three risks ratio. The developed procedure is applied to the information linear system, Erlang loss system, single-server queueing system with discouraged arrivals, Binomial system and Engset loss system.
Keywords
Absolute and relative risk
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uncertainty
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system information and entropy
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system utilization
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stochastic service system
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Igor Lazov.
Risk Assessment of a Stochastic Service System.
Journal of Systems Science and Systems Engineering, 2020, 29(5): 537-554 DOI:10.1007/s11518-020-5460-6
| [1] |
Akimaru H, Kawashima K. Teletraffic - Theory and Applications, 1999, London: Springer
|
| [2] |
Aven T, Renn O. On risk defined as an event where the outcome is uncertain. Journal of Risk Research, 2009, 12(1): 1-11.
|
| [3] |
Badila ES, Boxma OJ, Resing JAC, Winands EMM. Queues and risk models with simultaneous arrivals. Advances in Applied Probability, 2014, 46: 812-831.
|
| [4] |
Bertsekas D, Gallagher R. Data Networks, 1987, New Jersey: Prentice Hall
|
| [5] |
Buckley JJ. Entropy principles in decision making under risk. Risk Analysis, 1979, 5: 303-313.
|
| [6] |
Csiszar I. Information-type measures of difference of probability distributions and indirect observations. Studia Scientiarum Mathematicarum Hungarica, 1967, 2: 299-318.
|
| [7] |
Ferdinand AE (1970). A statistical mechanics approach to systems analysis. IBM Journal of Research & Development 539–547.
|
| [8] |
Franke J, Hördle W, Stahl G. Measuring Risk in Complex Stochastic Systems, 2000, New York: Springer.
|
| [9] |
Gibbs JW. On the equilibrium of heterogeneous substances: Abstract by the author. American Journal of Science, 1878, 16(3): 441-458.
|
| [10] |
Goerlandt F, Reniers G. Prediction in a risk analysis context: Implications for selecting a risk perspective in practical applications. Safety Science, 2018, 101: 344-351.
|
| [11] |
Gönsch J, Hassler M, Schur R. Optimizing conditional value-at-risk in dynamic pricing. OR Spectrum, 2018, 40(3): 711-750.
|
| [12] |
Gross D, Harris C. Fundamentals of Queueing Theory, 1998, New York: John Wiley & Sons, Inc..
|
| [13] |
Guiasu. Maximum entropy condition in queueing theory. The Journal of the Operational Research Society, 1986, 37: 293-301.
|
| [14] |
Hayes JF, Ganesh Babu TVJ. Modeling and Analysis of Telecommunications Networks, 2004, New York: Wiley 416
|
| [15] |
Iversen VB. Teletraffic Engineering and Network Planning, 2011, Lyngby: Technical University of Denmark 380
|
| [16] |
Kaniadakis G. Non-linear kinetics underlying generalized statistics. Physica A: Statistical Mechanics and Its Applications, 2001, 296: 405-425.
|
| [17] |
Karmeshu (2003). Entropy Measures, Maximum Entropy Principle and Emerging Applications. Springer-Verlag.
|
| [18] |
Kaufman JS. Blocking in a shared resource environment. IEEE Transactions on Communications, 1981, 29(10): 1474-1481.
|
| [19] |
Kleinrock L. Queueing Systems. Vol.1: Theory, 1975, New York: John Wiley & Sons, Inc..
|
| [20] |
Koenig M, Meissner J. Risk management policies for dynamic capacity control. Computers & Operations Research, 2015, 59: 104-118.
|
| [21] |
Koenig M, Meissner J. Risk minimising strategies for revenue management problems with target values. Journal of the Operational Research Society, 2016, 67(3): 402-411.
|
| [22] |
Lazov I. A methodology for information and capacity analysis of broadband wireless access systems. Telecommunication Systems, 2016, 63(2): 127-139.
|
| [23] |
Lazov I. Profit management of car rental companies. European Journal of Operational Research, 2017, 258(1): 307-314.
|
| [24] |
Lazov I. Information analysis of queueing systems. International Journal of General Systems, 2017, 46(6): 616-639.
|
| [25] |
Lazov I. An uncertainty quantification methodology for broadband wireless access systems. Pervasive and Mobile Computing, 2017, 42: 151-165.
|
| [26] |
Lazov I. Entropy analysis of broadband wireless access systems. IEEE Systems Journal, 2017, 11(4): 2366-2373.
|
| [27] |
Lazov I. A methodology for revenue analysis of parking lots. Networks and Spatial Economics, 2019, 19(1): 177-198.
|
| [28] |
Lazov I. Risk-based analysis of manufacturing systems. International Journal of Production Research, 2019, 57(22): 7089-7103.
|
| [29] |
Lazov P, Lazov I. A general methodology for population analysis. Physica A: Statistical Mechanics and Its Applications, 2014, 415: 557-594.
|
| [30] |
Molloy M. Fundamentals of Performance Modeling, 1989, New York: Macmillan
|
| [31] |
Newman MEJ (2010). Networks: An Introduction. Oxford University Press.
|
| [32] |
Nowak J, Sarkani S, Mazzuchi T. Risk assessment for a national renewable energy target part II: Employing the model. IEEE Systems Journal, 2016, 10(2): 459-470.
|
| [33] |
Paz JM, Mark BL, Kobayashi H (1995). A maximum entropy approach to the analysis of loss systems. Proceeding of IEEE International Conference on Networks. Singapore, July 1995.
|
| [34] |
Perry P. Risk Assessment: Questions and Answers, 2003, London, UK: ICE Publishing
|
| [35] |
Rausand M. Risk Assessment: Theory, Methods, and Applications, 2011, Hoboken, New Jersey: John Wiley & Sons, Inc..
|
| [36] |
Ross KW (1995). Multiservice Loss Models for Broadband Telecommunication Networks. Springer-Verlag.
|
| [37] |
Shannon CE. A Mathematical theory of communication. The Bell System Technical Journal, 1948, 27: 379-423. 623–656
|
| [38] |
Sharma S, Karmeshu. Bimodal packet distribution in loss systems using maximum Tsallis entropy principle. IEEE Transactions on Communications, 2008, 56(9): 1530-1535.
|
| [39] |
Shore JE. Information theoretic approximations for M/G/1 and G/G/1 queueing systems. Acta Informatica, 1982, 17: 43-61.
|
| [40] |
Slovic. Perception of Risk. Science, 1987, 236: 280-285.
|
| [41] |
Smith. Appraisal, Risk and Uncertainty, 2003, London, UK: ICE Publishing
|
| [42] |
Sole RV, Valverde S. Information theory of complex networks: On evolution and architectural constraints, 2004, Berlin: Springer 189-207.
|
| [43] |
Stallings W. Data and Computer Communications, 2007, Upper Saddle River, NJ: Prentice Hall. (8ed)
|
| [44] |
Stuart A, Ord JK (2010). Kendall’s Advanced Theory of Statistics, Volume 1: Distribution Theory (6ed). John Wiley & Sons (first published 1958).
|
| [45] |
Stuck B, Arthurs E. A Computer and Communications Network Performance Analysis Primer, 1985, New Jersey: Prentice Hall
|
| [46] |
Thomas MU (1979). A generalized maximum entropy principle. Operations Research (27):1188–1196.
|
| [47] |
Touchette H, Lloyd S. Information-theoretic approach to the study of control systems. Physica A: Statistical Mechanics and Its Applications, 2004, 331(1–2): 140-172.
|
| [48] |
Journal of Statistical Physics, 1988, 52(479):
|
| [49] |
Wu DD, Kefan X, Gang C, Ping G. A risk analysis model in concurrent engineering product development. Risk Analysis, 2010, 30(9): 1440-1453.
|
| [50] |
Xue F, Kumar PR. Scaling laws for Ad hoc wireless networks: An information theoretic approach. Foundations and Trends® in Networking, 2006, 1(2): 145-270.
|