6,980 research outputs found
Survey on Additive Manufacturing, Cloud 3D Printing and Services
Cloud Manufacturing (CM) is the concept of using manufacturing resources in a
service oriented way over the Internet. Recent developments in Additive
Manufacturing (AM) are making it possible to utilise resources ad-hoc as
replacement for traditional manufacturing resources in case of spontaneous
problems in the established manufacturing processes. In order to be of use in
these scenarios the AM resources must adhere to a strict principle of
transparency and service composition in adherence to the Cloud Computing (CC)
paradigm. With this review we provide an overview over CM, AM and relevant
domains as well as present the historical development of scientific research in
these fields, starting from 2002. Part of this work is also a meta-review on
the domain to further detail its development and structure
Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies
Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin
Multi crteria decision making and its applications : a literature review
This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM
Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing : an empirical study
With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. (C) 2017 Elsevier Ltd. All rights reserved
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Serial-batch scheduling – the special case of laser-cutting machines
The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning
Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm
This is the author’s version of a work that was accepted for publication in Robotics and Computer-Integrated Manufacturing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Robotics and Computer-Integrated Manufacturing, [Volume 29, Issue 5, October 2013, Pages 418–429] DOI10.1016/j.rcim.2013.04.001[EN] The traditional production scheduling problem considers performance
indicators such as processing time, cost and quality as optimization objectives in
manufacturing systems; however, it does not take energy consumption and
environmental impacts into account completely. Therefore, this paper proposes an
energy-efficient model for flexible flow-shop scheduling (FFS). First, a mathematical
model for a FFS problem, which is based on an energy-efficient mechanism, is
described to solve multi-objective optimization. Since FFS is well known as the NPhard
problem, an improved genetic-simulated annealing algorithm is adopted to make
a significant trade-off between the makespan and the total energy consumption for
implementing a feasible scheduling. Finally, a case study of production scheduling
problem for metalworking workshop in a plant is simulated. The experimental results
show the relationship between the makespan and the energy consumption is
conflicting apparently. Moreover, an energy saving decision is performed in a feasible
scheduling. Using the decision method, there can be a significant potential to
minimize energy consumption while complying with the conflicting relationshipThis research was carried out as a part of the CASES project which is supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Program under the Grant agreement no 294931. This research was also supported by National Science Foundation of China (No. 51175262), Jiangsu Province Science Foundation for Excellent Youths (No. BK201210111), Jiangsu Province Industry-Academy-Research Grant (No. BY201220116), the NUAA Fundamental Research Fund (No. NS2013053), the Project Funded by Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the research project TIN2010-20976-C02-01 (Ministry of Science and Innovation, Spain).Dai, M.; Tang, D.; Giret Boggino, AS.; Salido Gregorio, MA.; Li, W. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing. 29(5):418-429. https://doi.org/10.1016/j.rcim.2013.04.001S41842929
Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments
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