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Journal ArticleDOI

Energy-Aware Flowshop Scheduling: A Case for AI-Driven Sustainable Manufacturing

14 Oct 2021-IEEE Access (Institute of Electrical and Electronics Engineers (IEEE))-Vol. 9, pp 141678-141692
TL;DR: 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.

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Citations
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Journal ArticleDOI
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.

1 citations

Journal ArticleDOI
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.
Abstract: For the green hybrid flow shop scheduling problem considering sequence dependent setup times (SDST) and transportation times, 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. First of all, an encoding method combining the jobs sequence code at the first stage with the machine allocation code is designed to ensure that the algorithm can search the entire solution space to the greatest extent; then, a mixed population initialization method is designed to improve the quality of the initial population solution; thirdly, the crossover and mutation operators, and four neighborhood search strategies are designed to balance the global search and local search capabilities of the algorithm; finally, the effectiveness of the algorithm is verified by numerical experiments.
References
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Book
01 Jan 1998
TL;DR: In this article, the authors introduce the subject of simulation using examples from Arena and are designed for the introductory simulation course offered in industrial engineering and business departments Simulation with Arena is the only book to cover the Arena product and it is accompanied by an Arena CD.
Abstract: From the Publisher: Arena is the world's most effective simulation technology for modeling systems in manufacturing,transportation,logistics,warehousing,and business processing This highly anticipated text introduces the subject of simulation using examples from Arena and is designed for the introductory simulation course offered in industrial engineering and business departments Simulation with Arena is the only book to cover the Arena product and it is accompanied by an Arena CD

2,169 citations

Journal ArticleDOI
TL;DR: A comprehensive review of discrete event simulation publications published between 2002 and 2013 with a particular focus on applications in manufacturing is provided in this paper, where the literature is classified into three general classes of manufacturing system design, manufacturing system operation, and simulation language/package development.

448 citations

Journal ArticleDOI
TL;DR: This paper explores the future research direction in SDS and discusses the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.
Abstract: Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

308 citations

Journal ArticleDOI
TL;DR: In this paper, a mixed integer programming (MIP) model is used for energy-aware scheduling of manufacturing processes, where the reference schedule is modified to account for energy consumption.

276 citations

Journal ArticleDOI
TL;DR: A survey on the applications of optimal control to scheduling in production, supply chain and Industry 4.0 systems with a focus on the deterministic maximum principle to derive major contributions, application areas, limitations, as well as research and application recommendations for the future research.
Abstract: This paper presents a survey on the applications of optimal control to scheduling in production, supply chain and Industry 4.0 systems with a focus on the deterministic maximum principle. The first objective is to derive major contributions, application areas, limitations, as well as research and application recommendations for the future research. The second objective is to explain control engineering models in terms of industrial engineering and production management. To achieve these objectives, optimal control models, qualitative methods of performance analysis and computational methods for optimal control are considered. We provide a brief historic overview and clarify major mathematical fundamentals whereby the control engineering terms are brought into correspondence with industrial engineering and management. The survey allows the grouping of models with only terminal constraints with application to master production scheduling, models with hybrid terminal–logical constraints with applications to ...

212 citations