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Combined Scheduling and Control Books Combined Scheduling and Control Edited by John D. Hedengren and Logan Beal Printed Edition of the Special Issue Published in Processes www.mdpi.com/journal/processes MDPI Combined Scheduling and Control Books Special Issue Editors John D. Hedengren Logan Beal MDPI Basel Beijing Wuhan Barcelona Belgrade MDPI Special Issue Editors John D. Hedengren Logan Beal Brigham Young University Brigham Young University USA USA Editorial Office MDPI AG St. Alban-Anlage 66 Basel, Switzerland This edition is a reprint of the Special Issue published online in the open access journal Processes (ISSN 2227-9717) in 2017 (available at: http://www.mdpi.com/journal/processes/special_issues/Combined_Scheduling). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: Books Lastname, F.M.; Lastname, F.M. Article title. Journal Name. Year Article number, page range. First Edition 2018 ISBN 978-3-03842-805-3 (Pbk) ISBN 978-3-03842-806-0 (PDF) Cover photo courtesy of John D. Hedengren and Logan Beal. Articles in this volume are Open Access and distributed under the Creative Commons Attribution license (CC BY), which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book taken as a whole is © 2018 MDPI, Basel, Switzerland, distributed under the terms and conditions of the Creative Commons license CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/). MDPI Table of Contents About the Special Issue Editors ..................................... v Preface to ”Combined Scheduling and Control” ............................ vii John Hedengren and Logan Beal Special Issue: Combined Scheduling and Control doi: 10.3390/pr6030024 . 1 Dimitri Lefebvre Dynamical Scheduling and Robust Control in Uncertain Environments with Petri Nets for DESs doi: 10.3390/pr5040054 . 4 Girish Joglekar Using Simulation for Scheduling and Rescheduling of Batch Processes doi: 10.3390/pr5040066 . 20 Dhruv Gupta, Christos T. Maravelias A General State-Space Formulation for Online Scheduling doi: 10.3390/pr5040069 . 38 Fernando Nunes de Barros, Aparajith Bhaskar and Ravendra Singh A Validated Model for Design and Evaluation of Control Architectures for a Continuous Tablet Compaction Process Books doi: 10.3390/pr5040076 . 69 Logan D. R. Beal, Damon Petersen, Guilherme Pila, Brady Davis, Derek Prestwich, Sean Warnick and John D. Hedengren Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes doi: 10.3390/pr5040084 . 101 Damon Peterson, Logan D. R. Beal, Derek Prestwich, Sean Warnick and John D. Hedengren Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes doi: 10.3390/pr5040083 . 121 Ali M. Sahlodin and Paul I. Barton Efficient Control Discretization Based on Turnpike Theory for Dynamic Optimization doi: 10.3390/pr5040085 . 143 iii MDPI Books MDPI About the Special Issue Editors John D. Hedengren is Associate Professor at Brigham Young University in the Chemical Engineering Department, leading the PRISM (Process Research and Intelligent System Modeling) group. He is a chemical engineer by training with a B.S. and M.S. degree from Brigham Young University, and a Ph.D. from the University of Texas at Austin. His area of expertise is in process dynamics, control, and optimization with applications in fiber optic monitoring, automation of oil and gas processes, unmanned aerial systems, systems biology, and grid-scale energy systems. He has extensive experience in automation and modeling complex systems. Automation software (APMonitor) he developed has been applied in many industries world-wide including unmanned aircraft systems, chemicals manufacturing, and in energy production. Logan Beal is a Graduate Research Assistant at Brigham Young University in the Chemical Engineering Department. He leads the PRISM research group on combined scheduling and control and is the key contributor to the National Science Foundation award, "EAGER: Cyber-Manufacturing with Multi- echelon Control and Scheduling". His prior work experience includes optimization research with ExxonMobil and automation at Knoll's Atomic Power Lab. His current research is in developing a computing platform for large-scale dynamic optimization with the GEKKO package for Python. He is also developing a novel approach to nonlinear programming by combining elements of interior point methods and active set Sequential Quadratic Programming (SQP). Books v MDPI Books MDPI Preface to “Combined Scheduling and Control” Scheduling and control are typically viewed as separate applications because of historical factors such as limited computing resources. Now that algorithms and computing resources have advanced, there are new efforts to have short-term decisions (control) interact or merge with longer-term decisions (scheduling). A new generation of numerical optimization methods are evolving to capture additional benefits and unify the approach to manufacturing process automation. This special issue is a collection of some of the latest advancements in scheduling and control for both batch and continuous processes. It contains developments with multi-scale problem formulation, software for the new class of problems, and a survey of the strengths and weaknesses of successive levels of integration. John D. Hedengren and Logan Beal Special Issue Editors Books vii MDPI MDPI Books processes Editorial Special Issue: Combined Scheduling and Control John Hedengren * and Logan Beal Department of Chemical Engineering, Process Research and Intelligent Systems Modeling (PRISM), Brigham Young University, Provo, UT 84602, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-801-477-7341 Received: 1 March 2018; Accepted: 2 March 2018; Published: 7 March 2018 This Special Issue (SI) of Processes, “Combined Scheduling and Control,” includes approaches to formulating combined objective functions, multi-scale approaches to integration, mixed discrete and continuous formulations, estimation of uncertain control and scheduling states, mixed integer and nonlinear programming advances, benchmark development, comparison of centralized and decentralized methods, and software that facilitates the creation of new applications and long-term sustainment of benefits. Contributions acknowledge strengths, weaknesses, and potential further advancements, along with a demonstration of improvement over current industrial best-practice. Advanced optimization algorithms and increased computational resources are opening new possibilities to integrate control and scheduling. Some of the most popular advanced control methods today were conceptualized decades ago. Over a time span of 30 years, computers have increased in speed by about 17,000 times and algorithms such as integer programming have a speedup of approximately 150,000 times on some benchmark problems. With the combined hardware and software Books improvements, benchmark problems can now be solved 2.5 billion times faster; i.e., applications that formerly required 120 years to solve are now completed in 5 s [1]. New computing architectures and algorithms advance the frontier of solving larger scale and more complex integrated problems. Recent work demonstrates economic and operational incentives for merging scheduling and control. The accepted publications cover a range of topics and methods for combining control and scheduling. There were many submissions to the special issue, and about 50% were accepted for publication. The seven that were accepted have novel approaches, summary surveys, and illustrative examples that validate the methods and motivate further investigation. The articles are summarized below. Lefebvre, D. Dynamical Scheduling and Robust Control in Uncertain Environments with Petri Nets for DESs [2]. This paper is about the incremental computation of control sequences for discrete event systems in uncertain environments through implementation of timed Petri nets. The robustness of the resulting trajectory is also evaluated according to risk probability. A sufficient condition is provided to compute robust trajectories. The proposed results are applicable to a large class of discrete event systems, in particular in the domains of flexible manufacturing. Joglekar, G. Using Simulation for Scheduling and Rescheduling of Batch Processes [3]. This paper uses a BATCHES simulation model to accurately represent the complex recipes and operating rules typically encountered in batch process manufacturing. By using the advanced capabilities of the simulator (such as modeling assignment decisions, coordination logic, and plant operation rules), very reliable and verifiable schedules can be generated for the underlying process. Scheduling methodologies for a one-segment recipe and a rescheduling methodology for day-to-day decisions are presented. Gupta, D.; Maravelias, C. A General State-Space Formulation for Online Scheduling [4]. This paper presents a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows for the modeling of (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes when using a discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative yield estimates and processing times; Processes 2018, 6, 24 1 www.mdpi.com/journal/processes
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