Please use this identifier to cite or link to this item:
Title: Energy-aware flowshop scheduling: a case for AI-driven sustainable manufacturing
Authors: Danishvar, M
Danishvar, S
Katsou, E
Mansouri, SA
Mousavi, A
Keywords: scheduling;deep neural networks;discrete event simulation (DES);key performance indicator (KPI);operational planning and scheduling (OPS);optimization;hard metal
Issue Date: 14-Oct-2021
Publisher: IEEE
Citation: Danishvar, M., Danishvar, S., Katsou, E.., Mansouri, S.A. and Mousavi, A. (2021) 'Energy-Aware Flowshop Scheduling: A Case for AI-Driven Sustainable Manufacturing,' IEEE Access, 9, pp. 141678-141692, doi: 10.1109/ACCESS.2021.3120126.
Abstract: © Copyright 2021 The Author(s). A fully verifiable and deployable framework for optimizing schedules in a batch-based production system is proposed. The scheduler is designed to control and optimize the flow of batches of material into a network of identical and non-identical parallel and series machines that produce a high variation of complex hard metal products. The proposed multi-objective batch-based flowshop scheduling optimization (MOBS-NET) deploys a fully connected deep neural network (FCDNN) with respect to three performance criteria of energy, cost and makespan. The problem is NP-hard and considers minimizing the energy consumed per unit of product, operations cost, and the makespan. The output of the method has been validated and verified as optimal operational planning and scheduling meeting the business operational objectives. Real-time and look ahead discrete event simulation of the production process provides the feedback and assurance of the robustness and practicality of the optimum schedules prior to implementation.
Appears in Collections:Dept of Computer Science Research Papers
Dept of Mechanical and Aerospace Engineering Research Papers
Dept of Civil and Environmental Engineering Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf5.39 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons