A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost

In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to find best production strategy of in-house manufacturing and outsourcing in small and medium scale cellular manufacturing companies. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to be used in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; Simulated Annealing algorithms; Fuzzy Art Control; Ant Colony Optimization and a hybrid Multi-layer Perceptron and Simulated Annealing algorithms. Our findings indicate that the uncertain condition of system costs affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. The results showed that the proposed method can significantly reduce cell load variation while finding the best trading off values between in-house manufacturing and outsourcing

Cell Production System Design: A Literature Review

Purpose In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology: To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously. Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified. Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

This paper presents a review of clustering and mathematical programming methods and their impacts on cell forming (CF) and scheduling problems. In-depth analysis is carried out by reviewing 105 dominant research papers from 1972 to 2017 available in the literature. Advantages, limitations and drawbacks of 11 clustering methods in addition to 8 meta-heuristics are also discussed. The domains of studied methods include cell forming, material transferring, voids, exceptional elements, bottleneck machines and uncertain product demands. Since most of the studied models are NP-hard, in each section of this research, a deep research on heuristics and metaheuristics beside the exact methods are provided. Outcomes of this work could determine some existing gaps in the knowledge base and provide directives for objectives of this research as well as future research which would help in clarifying many related questions in cellular manufacturing systems (CMS)

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

Отечественный опыт в области научной организации производства и возможности его использования с позиций бережливого производства

Purpose: the article presents the results of a comparative study of the achievements of the leading Russian scientific schools, which stood at the origins of the scientific production organization. The main purpose of the study is to find out which theories of national scientific schools in the field of production organization appeared at the beginning of the 20th century have remained relevant until now and can be developed in our days. First of all, these theories were considered from the standpoint of the lean manufacturing concept usage, not only for material products, but for information products as well.Methods: the analysis and synthesis, as well as the method of analogies were used as the main methods of this scientific research.Results: the conducted research proved that almost in all researches of the production organization in 20–30 years of XX century, as a rule, the vector of influence of human resources on the manufacturing process was implicitly present. However, at that time, the dominant role was played by the production of material economic products with a small part of the intellectual component in their structure (compared to nowadays), while today the intellectual component became incomparably larger. The result of the study was also a modified version of the Japanese concept of «4M» on the role of materials, machine, man and method of labor management with the addition of the 5th «M» (mentality – a mental state), which allows to evaluate also the intellectual and creative (intangible) component of the production process. For the first time it is offered to consider and investigate possibilities of use in practice of human resources management the concept of "intellectual and creative attitudes" as the factor defining the degree of predisposition of the worker to use his intellectual potential through the required level of the creative abilities at production of information economy products.Conclusions and Relevance: it was shown that the subject of the in-depth research is not material products, but the information economy products, in the production of which the logic of providing the necessary information becomes crucial, taking into consideration the formation of confiding relations between the management of enterprises and their employees.В статье представлены результаты сравнительного исследования достижений ведущих российских научных школ, стоявших у истоков научной организации производства.Цель: Основная цель работы состоит в том, чтобы выяснить, какие положения отечественных научных школ в области организации производства, появившихся в начале XX века, сохранили свою актуальность до сих пор и могут быть развиты в настоящее время. Прежде всего, эти учения рассматривались с позиции использования концепций бережливого производства, причем не только материальных, но и информационных экономических продуктов.Методология проведения работы: В качестве основных методов научного исследования использовались анализ и синтез, а также метод аналогий.Результаты работы: Проведенное исследование доказало, что практически во всех направлениях исследований процессов организации производства в 20–30 гг. XX века, как правило, неявно присутствовал вектор влияния человеческих ресурсов. Однако в то время главенствующую роль играло производство материальных экономических продуктов с незначительной частью интеллектуальной составляющей в их составе (по сравнению с сегодняшним днем), в то время как сегодня интеллектуальная компонента стала несравнимо больше. Результатом исследования также стал модифицированный вариант японской концепции «4М» о роли материалов, машины, человека и метода управления трудом, с добавлением 5-й «М» (mentality – умственное состояние), позволяющий оценивать, наравне с материальной, также и интеллектуально-креативную (нематериальную) составляющую производственного процесса. Впервые предложено рассмотреть и исследовать возможности использования в практике управления человеческими ресурсами понятие «интеллектуально-креативные установки», как фактор, определяющий степень предрасположенности работника к использованию своего интеллектуального потенциала через проявление требуемого уровня своих созидательных способностей при производстве информационных экономических продуктов.Выводы: Было показано, что предметом углубленных исследований становятся не материальные, а информационные экономические продукты, при производстве которых решающей является логика донесения необходимой информации, причем с обязательным учетом формирования доверительных отношений между менеджментом предприятий и их работниками

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining

Smart process monitoring of machining operations

The following thesis explores the possibilities to applying artificial intelligence techniques in the field of sensory monitoring in the manufacturing sector. There are several case studies considered in the research activity. The first case studies see the implementation of supervised and unsupervised neural networks to monitoring the condition of a grinding wheel. The monitoring systems have acoustic emission sensors and a piezoelectric sensor capable to measuring electromechanical impedance. The other case study is the use of the bees' algorithm to determine the wear of a tool during the cutting operations of a steel cylinder. A script permits this operation. The script converts the images into a numerical matrix and allows the bees to correctly detect tool wear

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field