The Use of Disaggregate Choice Models in Semi-Markov Process Models of Trip Chaining Behavior

1979 ◽  
Vol 13 (4) ◽  
pp. 273-291 ◽  
Author(s):  
Steven R. Lerman
Keyword(s):  
Markov Process ◽  
Choice Models ◽  
Process Models ◽  
Trip Chaining ◽  
Semi Markov Process

Redundancy Allocation Optimization for Multistate Systems With Failure Interactions Using Semi-Markov Process

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Jing Wang ◽  
Mian Li
Keyword(s):  
Markov Process ◽  
System Analysis ◽  
Research Work ◽  
Process Models ◽  
Redundancy Allocation ◽  
Allocation Problems ◽  
Multistate Systems ◽  
Semi Markov Process ◽  
Failure Interaction ◽  
Main Subsystem

Failure interactions and multiple states are two common phenomena in engineering systems. However, most of the redundancy allocation problems assume binary states and ignore failure interactions, which will cause inaccurate and misleading results. Although some research work focuses on the multistate systems, failure interactions have been ignored. This paper, for the first time, solves the redundancy allocation problems considering the systems having both multiple states and failure interactions. The system studied in this paper is a kind of multistate system containing a main subsystem and an auxiliary subsystem with the failure interaction existing from the auxiliary subsystem to the main subsystem. Semi-Markov process is proposed as the model for the system analysis, and a reliability measure, availability, is obtained based on the proposed semi-Markov process models. The system availability is used as the constraint in the redundancy allocation problem. A case study from a navy application is presented to demonstrate the applicability of the proposed method.

Partially observable semi-Markov reward processes

1993 ◽  
Vol 30 (3) ◽  
pp. 548-560 ◽  
Author(s):  
Yasushi Masuda
Keyword(s):  
Markov Process ◽  
Counting Process ◽  
Probabilistic Interpretation ◽  
Real Domain ◽  
Semi Markov Process ◽  
Reward Process ◽  
Markov Reward ◽  
Partially Observable ◽  
Joint Behavior

The main objective of this paper is to investigate the conditional behavior of the multivariate reward process given the number of certain signals where the underlying system is described by a semi-Markov process and the signal is defined by a counting process. To this end, we study the joint behavior of the multivariate reward process and the multivariate counting process in detail. We derive transform results as well as the corresponding real domain expressions, thus providing clear probabilistic interpretation.

Continuous Semi-Markov Process by HARLAMOV, B.

Biometrics ◽  
2008 ◽  
Vol 64 (4) ◽  
pp. 1301-1301
Author(s):  
Mei-Jie Zhang
Keyword(s):  
Markov Process ◽  
Semi Markov Process

scholarly journals Maximal success durations for a semi-Markov process

1987 ◽  
Vol 24 (2) ◽  
pp. 203-224 ◽  
Author(s):  
David E. Fousler ◽  
Samuel Karlin
Keyword(s):  
Markov Process ◽  
Semi Markov Process
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