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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Model for Integrating Production Scheduling and Maintenance Planning of Flow Shop Production System
TL;DR: The computational results show that the recommended approach is qualified over the simulation based genetic algorithm optimization technique for obtaining superior solutions that has been proved in the literature to be one of the best approach.
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Integrated Optimization Approach of Hybrid Flow-Shop Scheduling Based on Process Set
TL;DR: In this paper, an improved artificial bee colony algorithm has been developed to solve the model and a segmented decoding method based on the insertion principle and the release time of the predecessor process is proposed to effectively use the idle time of a machine.
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Job Scheduling at Cascading Machines
Edward Huang,Kan Wu +1 more
TL;DR: This work considers the serial batching scheduling problem in which a group of machines can process multiple jobs continuously to reduce the processing times of the second and subsequent jobs and identifies several optimality properties of the optimal batching sequence.
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A data-driven approach to multi-product production network planning
TL;DR: This paper develops an alternative to the Allocated Clearing Function formulation, wherein system throughput is estimated at discrete work-in-process points and a mixed integer programming formulation is presented to use these throughput estimates for discrete release choices.
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Clustering and Dispatching Rule Selection Framework for Batch Scheduling
Gilseung Ahn,Sun Hur +1 more
TL;DR: A clustering algorithm and dispatching rule selection model is developed to minimize total tardiness and a method to generate a training dataset from historical data to train the neural network is developed.