Large-Scale Discrete-Time Scheduling Optimization: Industrial-Size Applications

Optimization of large-scale discrete-time scheduling problems is challenging due to the combinatorial complexity of binary or discrete decisions to be made. When including networks of unit-operations and inventory-tanks to fulfill both the logistics and quality balances as found in complex-scope pro...

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Bibliographic Details
Published in:IFAC-PapersOnLine 2022, Vol.55 (10), p.2581-2586
Main Authors: Franzoi, Robert E., Menezes, Brenno C.
Format: Article
Language:English
Subjects:
Industrial applications
Large-scale
MINLP
Modeling
Optimization
Scheduling
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Summary:Optimization of large-scale discrete-time scheduling problems is challenging due to the combinatorial complexity of binary or discrete decisions to be made. When including networks of unit-operations and inventory-tanks to fulfill both the logistics and quality balances as found in complex-scope process industries, the decomposition of mixed-integer nonlinear programming (MINLP) regarding its quantity-logic-quality phenomena (QLQP) paradigm into mixed-integer linear programming (MILP) and nonlinear programming (NLP) has been commonly and naturally used to find solutions of industrial-sized problems. Other approaches can be incorporated into an optimization-based decision-making framework to provide proper capabilities for handling complex large-scale applications. This includes strategies related to reduction of model, time, and scope that can be based on machine learning approaches and heuristic algorithms. Such a decision-making framework is useful not only to allow solving industrial-scale problems, but also to achieve enhanced applications. There are open challenges to automatically solve complex large-scale discrete-time problems in acceptable computing time. In this context, this paper employs a decision-making framework based on modeling and optimization capabilities to handle large-scale scheduling problems. The examples are built using the unit-operation-port-state superstructure (UOPSS) constructs and the semantics of the QLQP concepts in a discrete-time formulation. The proposed framework is shown to effectively use decomposition and heuristic strategies for solving industrial-sized scheduling formulations.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2022.10.098