On properties of stochastic inventory systems

Article Abstract:

The control parameters of order quantity/reorder point inventory systems are typically determined using either of two general optimization models, the stochastic and the deterministic. The deterministic model, usually described as the EOQ model, assumes that the demand process is a deterministic constant stream and is more widely used despite this oversimplifying assumption. On the other hand, the stochastic model, usually described as the (Q,r) model, assumes that the demand process is stochastic in nature. The deterministic EOQ model and the stochastic (Q,r) model are compared and a sensitivity analysis is performed, the results of which indicate that the stochastic model yields more accurate results.

Inventory control

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Steady-state approximation for a vector valued Markov chain

Article Abstract:

Non-Markovian stochastic processes can be modeled as Markov Chains when additional components in the state description are included so that the Markovian property is satisfied by the revised state variable's transition. However, this technique leads to a large increase in the number of states, typically to the point where it becomes impractical to apply the model due to the state space dimension. Applying an approximation of the original state variable in condensed form to a specific form of vector-valued Markov chain can address the problem. An intensive algorithm which approximates the conditional distribution for S(t) given X(t) is presented.

Analysis, Management research, Markov processes

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