Modelling emergency lateral transshipments in inventory systems
Management Science
The mathematics of product form queuing networks
ACM Computing Surveys (CSUR)
Bounded relative error in estimating transient measures of highly dependable non-Markovian systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Modeling Emergency Supply Flexibility in a Two-Echelon Inventory System
Management Science
Stocking Decisions for Low-Usage Items in a Multilocation Inventory System
Management Science
STRSCNE: A Scaled Trust-Region Solver for Constrained Nonlinear Equations
Computational Optimization and Applications
A model for lumpy demand parts in a multi-location inventory system with transshipments
Computers and Operations Research
Insights into inventory sharing in service parts logistics systems with time-based service levels
Computers and Industrial Engineering
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This study compares approximation techniques for the estimation of the operational availability of a corrective maintenance system. The assessment is based on the practical maintenance of safety equipments in operation in 38 Italian Airports. A single echelon one-for-one ordering policy with complete pooling is analyzed, with a deterministic rule for lateral transshipments. With this policy, the state probabilities of the associated Markov model cannot be expressed in product form. Since the exact computation of the state probabilities is not practical as the number of states in the Markov chain increases, this study describes three approximation techniques and assesses their performance in terms of computational effort, memory requirement and error with respect to the exact value. The first two techniques are based on a method by Alfredsson and Verrijdt and on the Equivalent Random Traffic method, respectively. The idea of both methods is to approximate the state probabilities with a product form, so that the Markov chain can be decomposed. The third technique is based on the multi-dimensional scaling down approach, which studies an equivalent reduced Markov chain rather than decomposing the original one.