Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review

By Christodoulos A. Floudas, Xiaoxia Lin. Computers and Chemical Engineering, 2004

649 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/a2ac2ea90af849a4e462ae4954607365f021f22a

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In Control and Systems Engineering

In Industrial and Manufacturing Engineering

Citations in the corpus

Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives (2016, background): “In general, one distinguishes between discrete- and continuous-time models, and there is a large body of work in the literature discussing different formulations and their strengths and limitations (Floudas & Lin, 2004; Méndez et al., 2006; Sundaramoorthy & Maravelias, 2011).”
Review of job shop scheduling research and its new perspectives under Industry 4.0 (2017, background): “For an earlier and more extensive explanation of the diverse aspects of scheduling models, there are some direct reviews such as Graves (1981)[9], Floudas and Lin (2004)[13] and Floudas and Lin (2005)[14].”
State-of-the-art review of optimization methods for short-term scheduling of batch processes (2006, background): “There have been significant research efforts over the last decade in this area in the development of optimization approaches, and several excellent reviews can be found in Floudas and Lin (2004), Kallrath (2002), Pekny and Reklaitis (1998), Pinto and Grossmann (1998), Reklaitis (1992), Shah (1998). ”
Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications (2005, methodology): “The performance of a schedule is measured with one or more criteria, for example, the overall profit, the operating costs, and the makespan.”
Process scheduling under uncertainty: Review and challenges (2008, methodology): “All rights reserved. oi:10.1016/j.compchemeng.2007.03.001 optimization; Sensitivity analysis; Parametric programming There are a great variety of aspects that need to be considered hen developing scheduling models.”
A new robust optimization approach for scheduling under uncertainty: : I. Bounded uncertainty (2004, background): “Therefore, it is of crucial importance to develop systematic methods to address the problem of scheduling under uncertainty, in order to create efficient and reliable schedules (Floudas & Lin, 2003).”