Optimality of zero-inventory policies for unreliable manufacturing systems
Operations Research
Probability, statistics, and queueing theory with computer science applications
Probability, statistics, and queueing theory with computer science applications
The pointwise stationary approximation for M1/M1/s
Management Science
Some effects of nonstationarity on multiserver Markovian queueing systems
Operations Research
Mt/G/∞ queues with sinusoidal arrival rates
Management Science
The physics of the Mt/G/ ∞ symbol Queue
Operations Research
Analysis of a production/inventory system subject to random disruptions
Management Science
Analysis of a production-inventory system with machine breakdowns and shutdowns
Computers and Operations Research
Supply interruptions in a lost-sales inventory system with random lead time
Computers and Operations Research
A PERIODIC-REVIEW INVENTORY SYSTEM WITH SUPPLY INTERRUPTIONS
Probability in the Engineering and Informational Sciences
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Optimal newsvendor policies for dual-sourcing supply chains: A disruption risk management framework
Computers and Operations Research
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We consider a firm that faces random demand and receives shipments from a single supplier who faces random supply. The supplier's availability may be affected by events such as storms, strikes, machine breakdowns, and congestion due to orders from its other customers. In our model, we consider a dynamic environment: the probability of disruption, as well as the demand intensity, can be time dependent. We model this problem as a two-dimensional non-homogeneous continuous-time Markov chain (CTMC), which we solve numerically to obtain the total cost under various ordering policies. We propose several such policies, some of which are time dependent while others are not. The key question we address is: How much improvement in cost is gained by using time-varying ordering policies rather than stationary ones? We compare the proposed policies under various cost, demand, and disruption parameters in an extensive numerical study. In addition, motivated by the fact that disruptions are low-probability events whose non-stationary probabilities may be difficult to estimate, we investigate the robustness of the time-dependent policies to errors in the supply parameters. We also briefly investigate sensitivity to the repair-duration distribution. We find that non-stationary policies can provide an effective balance of optimality (low cost) and robustness (low sensitivity to errors).