Abstract
Current literature presents optimal control computational algorithms with regard to state, control, and conjunctive variable spaces. This paper first analyses the advantages and limitations of different optimal control computational methods and algorithms which can be used for short-term scheduling. Second, it develops an optimal control computational algorithm that allows for the solution of short-term scheduling in an optimal manner. Moreover, qualitative and quantitative analysis of the manufacturing system scheduling problem is presented. Results highlight computer experiments with a scheduling software prototype as well as potential future research avenues.
Highlights
Short-term scheduling in manufacturing systems (MS) considers jobs that contain operation chains with equal or different machine sequences and different processing times
The justification of the choice of the AS method is related to its dynamic nature ([43,44])
Optimal controls as functions of the system and control state allow for the generation of optimal decisions in consideration of a system’s evolution in time in the presence of perturbations which the integrated decision-support system and guides the planning and scheduling decisions in dynamics on the principles of optimization and adaptation
Summary
Short-term scheduling in manufacturing systems (MS) considers jobs that contain operation chains with equal (i.e., flow shop) or different (i.e., job shop) machine sequences and different processing times. [5,6,7,8,9,10,11,12] and [13] have demonstrated that specific large-scale scheduling problems with complex hybrid logical and terminal constraints, process execution non-stationary (i.e., interruptions in machine availability), complex interrelations between process dynamics, capacity evolution and setups (i.e., intensity-dependent processing times for machine work) require further investigation in terms of a broad range of methodical approaches.
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