Fuzzy activity time-based model predictive control of open-station assembly lines
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TLDR
The results confirm that the application of the proposed algorithm is widely applicable in cases where a production line of a supply chain is not well balanced and the activity times are uncertain.About:
This article is published in Journal of Manufacturing Systems.The article was published on 2020-01-01 and is currently open access. It has received 13 citations till now. The article focuses on the topics: Model predictive control & Fuzzy logic.read more
Citations
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Modelling for Digital Twins—Potential Role of Surrogate Models
TL;DR: The aim was to overview the difficulties and challenges regarding the modelling aspects of digital twin applications and to explore the fields where surrogate models can be utilised advantageously.
Journal ArticleDOI
Discrete event-driven model predictive control for real-time work-in-process optimization in serial production systems
TL;DR: The results show that the discrete e-MPC can rapidly and cost-effectively reconfigure production logic and decrease the r-WIP without deteriorating system throughput and is a step forward in smart manufacturing to achieve improved system responsiveness and efficiency.
Journal ArticleDOI
An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions
TL;DR: This paper reviews 301 relevant papers from 1998 to 2020 and proposes future directions regarding fuzzy techniques in SCM in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection.
Journal ArticleDOI
A survey of the parallel assembly lines balancing problem
TL;DR: This paper surveys the parallel assembly lines balancing problem (PALBP) studies and a classification scheme is provided to ease understanding and the main gaps in the literature are described.
Journal ArticleDOI
Stochastic model predictive control for remanufacturing system management
TL;DR: A stochastic discrete-time dynamical model is proposed to represent a remanufacturing system, where the relationship between the market satisfaction, inventory status, and operational actions is explicitly modeled and reveals notable managerial insights and effects of product commonality, demand patterns, and Operational actions scheduling on the efficiency of the re manufacturing system.
References
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Book
Fuzzy Mathematical Models in Engineering and Management Science
A. Kaufmann,Madan M. Gupta +1 more
TL;DR: This paper presents a general view about the Fuzzification of Models in Engineering and Management Science, and some examples of Fuzzy Set Theory and System Modelling, as well as some of its applications in Transportation Optimization and Decision Making.
Journal ArticleDOI
State-of-the-art exact and heuristic solution procedures for simple assembly line balancing
Armin Scholl,Christian U. Becker +1 more
TL;DR: This paper gives an up-to-date and comprehensive survey of SALBP research with a special emphasis on recent outstanding and guiding contributions to the field.
BookDOI
Handbook of Granular Computing
TL;DR: The Handbook of Granular Computing offers a comprehensive reference source for the granular computing community, edited by and with contributions from leading experts in the field, and represents a significant and valuable contribution to the literature.
Journal ArticleDOI
Processing fuzzy temporal knowledge
D. DuBois,H. Prade +1 more
TL;DR: L.A. Zadeh's (1975) possibility theory is used as a general framework for modeling temporal knowledge pervaded with imprecision or uncertainty, and Deductive patterns of reasoning involving fuzzy and/or uncertain temporal knowledge are established.
Journal ArticleDOI
Brief Model predictive control for max-plus-linear discrete event systems
TL;DR: The conventional model predictive control framework is extended and adapted to max-plus-linear systems, i.e., discrete-event systems that can be described by models that are ''linear'' in the (max,+) algebra.
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