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

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submitted on 2024-08-27, 11:02 and posted on 2024-08-27, 11:04 authored by Robert E. Franzoi, Brenno C. Menezes

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.

Other Information

Published in: IFAC-PapersOnLine
License: https://creativecommons.org/licenses/by-nc-nd/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.ifacol.2022.10.098

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU