Uncertain Production Scheduling Based on Fuzzy Theory Considering Utility and Production Rate

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.

[1]  Kleanthis Thramboulidis,et al.  A cyber-physical system-based approach for industrial automation systems , 2014, Comput. Ind..

[2]  S. H. Choi,et al.  A hybrid estimation of distribution algorithm for simulation-based scheduling in a stochastic permutation flowshop , 2015, Comput. Ind. Eng..

[3]  Charlotta Johnsson,et al.  Plant-wide utility disturbance management in the process industry , 2013, Comput. Chem. Eng..

[4]  A. Barbosa‐Póvoa,et al.  An Improved RTN Continuous-Time Formulation for the Short-term Scheduling of Multipurpose Batch Plants , 2001 .

[5]  Luis Puigjaner,et al.  Addressing the design of chemical supply chains under demand uncertainty , 2006 .

[6]  C. Hwang,et al.  Possibilistic linear programming for managing interest rate risk , 1993 .

[7]  Ignacio E. Grossmann,et al.  New General Continuous-Time State−Task Network Formulation for Short-Term Scheduling of Multipurpose Batch Plants , 2003 .

[8]  Sarangapani Jagannathan,et al.  Optimal Defense and Control for Cyber-Physical Systems , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[9]  Pontus Giselsson,et al.  Minimization of economical losses due to utility disturbances in the process industry , 2013 .

[10]  A. G. Lagodimos,et al.  Hierarchical production planning for multi-product lines in the beverage industry , 2007 .

[11]  Jin Zhu,et al.  Robust optimization approach for short-term scheduling of batch plants under demand uncertainty? , 2011, Kybernetes.

[12]  Ramsagar Vooradi,et al.  Improved three-index unit-specific event-based model for short-term scheduling of batch plants , 2012, Comput. Chem. Eng..

[13]  Reay-Chen Wang,et al.  Applying possibilistic linear programming to aggregate production planning , 2005 .

[14]  Guohe Huang,et al.  Multistage scenario-based interval-stochastic programming for planning water resources allocation , 2009 .

[15]  Daniela Giordano,et al.  Mobile cyber physical systems for health care: Functions, ambient ontology and e-diagnostics , 2016, 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[16]  S. Ali Torabi,et al.  Fuzzy hierarchical production planning (with a case study) , 2010, Fuzzy Sets Syst..

[17]  Farhad Mehdipour Smart Field Monitoring: An Application of Cyber-Physical Systems in Agriculture (Work in Progress) , 2014, 2014 IIAI 3rd International Conference on Advanced Applied Informatics.

[18]  Christodoulos A. Floudas,et al.  Improved Unit-Specific Event-Based Continuous-Time Model for Short-Term Scheduling of Continuous Processes: Rigorous Treatment of Storage Requirements , 2007 .

[19]  M. Ierapetritou,et al.  Robust short-term scheduling of multiproduct batch plants under demand uncertainty , 2001 .

[20]  C. Floudas,et al.  Effective Continuous-Time Formulation for Short-Term Scheduling. 1. Multipurpose Batch Processes , 1998 .

[21]  Panganamala Ramana Kumar,et al.  Dynamic Watermarking: Active Defense of Networked Cyber–Physical Systems , 2016, Proceedings of the IEEE.

[22]  Deyi Mou,et al.  An Uncertain Programming for the Integrated Planning of Production and Transportation , 2014 .

[23]  Iraj Mahdavi,et al.  A bi-objective possibilistic programming model for open shop scheduling problems with sequence-dependent setup times, fuzzy processing times, and fuzzy due dates , 2012, Appl. Soft Comput..

[24]  Bran Selic,et al.  Understanding Uncertainty in Cyber-Physical Systems: A Conceptual Model , 2016, ECMFA.

[25]  C. Floudas,et al.  Novel Unified Modeling Approach for Short-Term Scheduling , 2009 .

[26]  Reha Uzsoy,et al.  Executing production schedules in the face of uncertainties: A review and some future directions , 2005, Eur. J. Oper. Res..

[27]  E. Hassini,et al.  Multi-site production planning integrating procurement and distribution plans in multi-echelon supply chains: an interactive fuzzy goal programming approach , 2009 .

[28]  Josefa Mula,et al.  Capacity and material requirement planning modelling by comparing deterministic and fuzzy models , 2008 .

[29]  T.-F. Liang,et al.  Integrating production-transportation planning decision with fuzzy multiple goals in supply chains , 2008 .

[30]  Gunther Reinhart,et al.  Characterization of Cyber-Physical Sensor Systems , 2016 .

[31]  Krister Forsman,et al.  Hierarchical Scheduling and Utility Disturbance Management in the Process Industry , 2013, MIM.

[32]  Yue Wang,et al.  Unit-Specific Event-Based and Slot-Based Hybrid Model Framework with Hierarchical Structure for Short-Term Scheduling , 2015 .

[33]  Jiao Bin,et al.  Scheduling of Uncertain Multi-product Batch Processes Under Finite Intermediate Storage Policy , 2008 .

[34]  Tien-Fu Liang,et al.  Distribution planning decisions using interactive fuzzy multi-objective linear programming , 2006, Fuzzy Sets Syst..

[35]  Christodoulos A. Floudas,et al.  Unit-specific event-based continuous-time approach for short-term scheduling of batch plants using RTN framework , 2008, Comput. Chem. Eng..

[36]  Dietmar P. F. Möller,et al.  Cyber-physical systems in smart transportation , 2016, 2016 IEEE International Conference on Electro Information Technology (EIT).

[37]  M.D. Ilic,et al.  Modeling future cyber-physical energy systems , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[38]  S.A. Torabi,et al.  An interactive possibilistic programming approach for multiple objective supply chain master planning , 2008, Fuzzy Sets Syst..

[39]  Ihsan Sabuncuoglu,et al.  Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research , 2009, Int. J. Comput. Integr. Manuf..

[40]  Ciprian-Radu Rad,et al.  Smart Monitoring of Potato Crop: A Cyber-Physical System Architecture Model in the Field of Precision Agriculture , 2015 .

[41]  C. Hwang,et al.  A new approach to some possibilistic linear programming problems , 1992 .

[42]  Mohammad Abdullah Al Faruque,et al.  A model-based design of Cyber-Physical Energy Systems , 2014, 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC).

[43]  Feng Xia,et al.  A Secured Health Care Application Architecture for Cyber-Physical Systems , 2011, ArXiv.

[44]  Ignacio E. Grossmann,et al.  New general continuous-time state-task network formulation for short-term scheduling of multipurpose batch plants , 2003 .

[45]  Syed Mahfuzul Aziz,et al.  Review of Cyber-Physical System in Healthcare , 2014, Int. J. Distributed Sens. Networks.