Advances in Engineering Software
Computers and Industrial Engineering
A survey of dynamic scheduling in manufacturing systems
Journal of Scheduling
Training a neural network to select dispatching rules in real time
Computers and Industrial Engineering
DUST: a generalized notion of similarity between uncertain time series
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Intelligent Diagnosis and Prognosis of Industrial Networked Systems
Intelligent Diagnosis and Prognosis of Industrial Networked Systems
Evolutionary algorithm for stochastic job shop scheduling with random processing time
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach
Computers and Industrial Engineering
Computers and Industrial Engineering
Parallel-machine scheduling to minimize tardiness penalty and power cost
Computers and Industrial Engineering
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Motivated by the need to deal with uncertainties in energy optimization of flexible manufacturing systems, this paper considers a dynamic scheduling problem which minimizes the sum of energy cost and tardiness penalty under power consumption uncertainties. An integrated control and scheduling framework is proposed including two modules, namely, an augmented discrete event control (ADEC) and a max-throughput-min-energy reactive scheduling model (MTME). The ADEC is in charge of inhibiting jobs which may lead to deadlocks, and sequencing active jobs and resources. The MTME ensures the fulfillment of the innate constraints and decides the local optimal schedule of active jobs and resources. Our proposed framework is applied to an industrial stamping system with power consumption uncertainties formulated using three different probability distributions. The obtained schedules are compared with three dispatching rules and two rescheduling approaches. Our experiment results verify that MTME outperforms three dispatching rules in terms of deviation from Pareto optimality and reduces interrupted time significantly as compared to rescheduling approaches. In addition, ADEC and MTME are programmed using the same matrix language, providing easy implementation for industrial practitioners.