Energy-Aware Flowshop Scheduling: A Case for AI-Driven Sustainable Manufacturing
TLDR
In this article, a fully verifiable and deployable framework for optimizing schedules in a batch-based production system is proposed, which 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.Abstract:
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.read more
Citations
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Neural agent-based production planning and control: An architectural review
TL;DR: In this article , the authors provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production, which can process large quantities of high-dimensional data in real time.
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Green Hybrid Flow Shop Scheduling Problem Considering Sequence Dependent Setup Times and Transportation Times
Shaoxing Wu,Li Liu +1 more
TL;DR: In this paper , a mixed integer programming model is established with the objectives of minimizing the maximum completion time (makespan) and total energy consumption simultaneously, and an improved memetic algorithm is proposed with the problem characteristics.
References
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A hybrid scheduling approach for a two-stage flexible flow shop with batch processing machines
Yi Tan,Lars Mönch,John W. Fowler +2 more
TL;DR: It turns out that the proposed hybrid approach outperforms an iterative decomposition scheme where a fairly simple heuristic based on time window decomposition and the apparent tardiness cost dispatching rule is used to solve the subproblems.
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Bi-objective optimisation for scheduling the identical parallel batch-processing machines with arbitrary job sizes, unequal job release times and capacity limits
TL;DR: A new bi-objective-mixed integer linear programming model for BPM in which arbitrary job size, unequal release time and capacity limits are considered as realistic assumptions occur in the manufacturing environments is presented.
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Deep Learning-Based Dynamic Scheduling for Semiconductor Manufacturing With High Uncertainty of Automated Material Handling System Capability
TL;DR: From the results, it is verified that the proposed dynamic scheduling system can enhance the performance of existing AMHS and reduce machine starvation and production losses.
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Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling
TL;DR: The results reveal that the evolved SPs can discover more useful heuristics and behave more competitive than the man-made SPs in more complex scheduling scenarios and have a strong generalization performance to be reused in new unobserved scheduling scenarios.
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Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant
TL;DR: A case study focusing on multi-objective flexible job shop scheduling problem (MO-FJSP) in an aero-engine blade manufacturing plant is presented and it is demonstrated that the bottleneck-agent-based hyper-heuristics (MAHH) produces the best result among the three MAHH methods.