Abstract
Handling uncertainty in an appropriate manner during the real operation of a cyber-physical system (CPS) is critical. Uncertain production scheduling as a part of CPS uncertainty issues should attract more attention. In this paper, a Mixed Integer Nonlinear Programming (MINLP) uncertain model for batch process is formulated based on a unit-specific event-based continuous-time modeling method. Utility uncertainty and uncertain relationship between production rate and utility supply are described by fuzzy theory. The uncertain scheduling model is converted into deterministic model by mathematical method. Through one numerical example, the accuracy and practicability of the proposed model is proved. Fuzzy scheduling model can supply valuable decision options for enterprise managers to make decision more accurate and practical. The impact and selection of some key parameters of fuzzy scheduling model are elaborated.
Highlights
A cyber-physical system (CPS) was identified as a key research area by the US National ScienceFoundation (NSF) and was listed as the number one research priority by the US President’s Council of Advisors on Science and Technology [1]
Uncertainty is intrinsic in most technical systems, including CPS
For the uncertain relationship between the production rate and the utility supply, firstly establish the linear function in deterministic scheduling model, and use fuzzy method to represent the coefficient of linear function
Summary
A cyber-physical system (CPS) was identified as a key research area by the US National Science. Et al [15] proposed a robust optimization approach for the short-term scheduling of batch plants under demand uncertainty where the uncertain parameters can be described by a normal distribution function. Et al [17] established a scheduling mathematical model for multi-product batch processes under finite intermediate storage policy with uncertain processing time based on fuzzy programming theory. Most studies mainly deal with uncertainties of demand and processing time in production scheduling. Utilities such as steam, cooling water and electricity, are supplied to areas or product lines to support the normal operation of equipment. For the uncertain relationship between the production rate and the utility supply, firstly establish the linear function in deterministic scheduling model, and use fuzzy method to represent the coefficient of linear function.
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