Stochastic Scheduling Subject to Preemptive-Repeat Breakdowns with Incomplete Information

• Published in: Operations research Vol. 57; no. 5; pp. 1236 - 1249
• Main Authors: Cai, Xiaoqiang, Wu, Xianyi, Zhou, Xian
• Format: Journal Article
• Language: English
• Published: Hanover, MD INFORMS 01.09.2009; Institute for Operations Research and the Management Sciences
• Subjects: Analysis; Applied sciences; Dynamic programming; dynamic programming/optimal control; Exact sciences and technology; Information modeling; Information operations; learning; Markov processes; Mass production; Mathematical programming; Mathematical sequences; Operational research and scientific management; Operational research. Management science; Operations research; Optimal policy; Probability; Probability distributions; Production scheduling; Random variables; Scheduling; Scheduling, sequencing; semi-Markov; stochastic; Stochastic analysis; stochastic model applications; Stochastic models; Stopping distances; Studies; United States; Performance evaluation; Markov process; Preemption; Stochastic model; Optimal policy; Processing time; learning; History; Modeling; Dynamic conditions; Uncertain system; Dynamic programming; Incomplete information; Random variable; Posterior distribution; Single machine; Execution time; Probabilistic approach; Prior distribution; Scheduling; production/scheduling: stochastic; Stochastic programming; Exact solution; Production management; Optimal control; Reward; probability: stochastic model applications; By product; dynamic programming/optimal control: semi-Markov

This paper considers the problem of scheduling a set of jobs on a single machine subject to stochastic breakdowns with incomplete information on the probability distributions involved in the decision process. We focus on the preemptive-repeat discipline, under which a machine breakdown leads to the...