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

Dynamic Facility Layout for Cellular and Reconfigurable Manufacturing using Dynamic Programming and Multi-Objective Metaheuristics

The facility layout problem is one of the most classical yet influential problems in the planning of production systems. A well-designed layout minimizes the material handling costs (MHC), personnel flow distances, work in process, and improves the performance of these systems in terms of operating costs and time. Because of this importance, facility layout has a rich literature in industrial engineering and operations research. Facility layout problems (FLPs) are generally concerned with positioning a set of facilities to satisfy some criteria or objectives under certain constraints. Traditional FLPs try to put facilities with the high material flow as close as possible to minimize the MHC. In static facility layout problems (SFLP), the product demands and mixes are considered deterministic parameters with constant values. The material flow between facilities is fixed over the planning horizon. However, in today’s market, manufacturing systems are constantly facing changes in product demands and mixes. These changes make it necessary to change the layout from one period to the other to be adapted to the changes. Consequently, there is a need for dynamic approaches of FLP that aim to generate layouts with high adaptation concerning changes in product demand and mix. This thesis focuses on studying the layout problems, with an emphasis on the changing environment of manufacturing systems. Despite the fact that designing layouts within the dynamic environment context is more realistic, the SFLP is observed to have been remained worthy to be analyzed. Hence, a math-heuristic approach is developed to solve an SFLP. To this aim, first, the facilities are grouped into many possible vertical clusters, second, the best combination of the generated clusters to be in the final layout are selected by solving a linear programming model, and finally, the selected clusters are sequenced within the shop floor. Although the presented math-heuristic approach is effective in solving SFLP, applying approaches to cope with the changing manufacturing environment is required. One of the most well-known approaches to deal with the changing manufacturing environment is the dynamic facility layout problem (DFLP). DFLP suits reconfigurable manufacturing systems since their machinery and material handling devices are reconfigurable to encounter the new necessities for the variations of product mix and demand. In DFLP, the planning horizon is divided into some periods. The goal is to find a layout for each period to minimize the total MHC for all periods and the total rearrangement costs between the periods. Dynamic programming (DP) has been known as one of the effective methods to optimize DFLP. In the DP method, all the possible layouts for every single period are generated and given to DP as its state-space. However, by increasing the number of facilities, it is impossible to give all the possible layouts to DP and only a restricted number of layouts should be fed to DP. This leads to ignoring some layouts and losing the optimality; to deal with this difficulty, an improved DP approach is proposed. It uses a hybrid metaheuristic algorithm to select the initial layouts for DP that lead to the best solution of DP for DFLP. The proposed approach includes two phases. In the first phase, a large set of layouts are generated through a heuristic method. In the second phase, a genetic algorithm (GA) is applied to search for the best subset of layouts to be given to DP. DP, improved by starting with the most promising initial layouts, is applied to find the multi-period layout. Finally, a tabu search algorithm is utilized for further improvement of the solution obtained by improved DP. Computational experiments show that improved DP provides more efficient solutions than DP approaches in the literature. The improved DP can efficiently solve DFLP and find the best layout for each period considering both material handling and layout rearrangement costs. However, rearrangement costs may include some unpredictable costs concerning interruption in production or moving of facilities. Therefore, in some cases, managerial decisions tend to avoid any rearrangements. To this aim, a semi-robust approach is developed to optimize an FLP in a cellular manufacturing system (CMS). In this approach, the pick-up/drop-off (P/D) points of the cells are changed to adapt the layout with changes in product demand and mix. This approach suits more a cellular flexible manufacturing system or a conventional system. A multi-objective nonlinear mixed-integer programming model is proposed to simultaneously search for the optimum number of cells, optimum allocation of facilities to cells, optimum intra- and inter-cellular layout design, and the optimum locations of the P/D points of the cells in each period. A modified non-dominated sorting genetic algorithm (MNSGA-II) enhanced by an improved non-dominated sorting strategy and a modified dynamic crowding distance procedure is used to find Pareto-optimal solutions. The computational experiments are carried out to show the effectiveness of the proposed MNSGA-II against other popular metaheuristic algorithms

Оптимізація та підвищення ефективності планування цеху на основі генетичного алгоритму

Структура роботи. Магістерська робота містить 4 розділи з висновками до кожного з них, загальні висновки, список використаних джерел, який викладено на 88 сторінці тексту, включає 32 рисунки, 29 таблиць та 28 використаних джерел. Актуальність дослідження. Зі стрімким розвитком світової обробної промисловості конкуренція між машинобудівними компаніями ставала все більш жорсткою, а проблема необгрунтованого розташування цехів механічної обробки ставала все більш серйозною. Виробництво підприємств потребує підвищення ефективності виробництва. Щоб підвищити корпоративну ефективність, зменшити виробничі й експлуатаційні витрати та підвищити ефективність цеху, оптимізація компонування цеху стала актуальною проблемою, яку необхідно вирішити в машинобудівній промисловості. Мета й завдання дослідження . Виробнича система цехів є важливою складовою виробничої системи підприємства, а системи в різних типах цехів становлять виробничу систему підприємства. У цеху, як основної одиниці виробничої системи, компонування обладнання має важливий вплив на ефективність, вартість і використання площі виробництва. Грамотно влаштована система виробництва цеху може не тільки підвищити ефективність логістики та заощадити витрати на обробку матеріалів, але й максимально використовувати виробничі потужності в цеху, при цьому раціонально використовувати простір, покращити робоче середовище працівників та підвищити ефективність роботи. Завдання дослідження 1 ─ Провести детальний аналіз компонування цеху з обробки крильчатки відцентрового компресора та специфічної логістики виробництва. ─ За допомогою методу SLP отримати імітаційну схему попереднього макета цеху. ─ Оптимізувати компонування обладнання майстерні за допомогою генетичного алгоритму. ─ Використовуйте програмне забезпечення моделювання заводу для створення моделі компонування обладнання цеху. ─ Перевірити ефективність програми оптимізації моделювання. Методи дослідження Візуальне моделювання в середовищі Siemens Tecnomatix Plant Simulation 14.0, Попередній макет цеху отримано методом SLP, а оптимальний макет цеху – за допомогою генетичного алгоритму. Використовуйте програмне забезпечення Plant Simulation для перевірки моделювання. Наукова новизна отриманих результатів. Результати дослідження, проведені в рамках магістерської роботи, мають такі наукові новинки: ─ Використання CATIA для віртуального моделювання обробки робочого колеса відцентрового компресора ─ Використання методу SLP для моделювання макету майстерні ─ Використовуйте генетичні алгоритми для оптимізації макета семінару. ─ Використовуйте Plant Simulation для моделювання та оптимізації макета майстерні. Шляхом порівняльного аналізу отримано оптимальне розташування цеху обробки робочих колес відцентрового компресора. ─ У поєднанні зі статистичною функцією Tecnomatix Plant Simulation Публікації. 2 1. Сюхон Вей, Воронцов Б.С. Моделювання процесу автоматичної виробничої лінії на базі Tecnomatix / Wei Xuhong, Б.С. Воронцов // Молода наука - робота і нанотехнології сучасного машинобудування: зб. наук. праць Міжнар. молодіжної наук.техн. конф., 14-15 квітня 2021 р. – Краматорськ : ДДМА, 2021. – С.36-39. 2. Сюхон Вей, Воронцов Б.С. Дослідження нового типу відцентрового токарного кріплення / Wei Xuhong, Б.С. Воронцов // Комплексне забезпечення якості технологічних процесів та систем (КЗЯТПС – 2021): XІ Міжнар. наук.-практ. конф., 26-27 травня 2021 р.: тези доп. – Чернігів : НУ «Чернігівська політехніка», 2021. –Т. 1. – С. 29-30.Structure of work. The Master's Thesis contains 4 sections with conclusions to each of them, general conclusions, a list of sources used, which outlined in 88 pages of text, includes 32 figures, 29 tables and 28 used sources. Actuality of the research. With the rapid development of the global manufacturing industry, competition among machinery manufacturing companies has become increasingly fierce, and the problem of unreasonable layout of mechanical processing workshops has become more and more serious. The production of enterprises needs to improve production efficiency. In order to improve corporate efficiency, reduce production and operation costs, and increase Workshop efficiency, so the optimization of workshop layout has become an urgent problem to be solved in the machinery manufacturing industry. The purpose and objectives of the study . Workshop production system is an important component of the enterprise's manufacturing system, and the systems in different types of workshops constitute the enterprise's manufacturing system. In the workshop, as the most basic unit of the production system, the layout of the equipment has an important influence on the efficiency, cost and space utilization of production. A well-arranged workshop manufacturing system can not only improve logistics efficiency and save material handling costs, but also maximize the use of production facilities in the workshop, while rationally using space, improving the working environment of workers, and improving work efficiency. Research objectives: Carry out a detailed analysis of the layout of the centrifugal compressor impeller machining workshop and the specific production logistics. 4 ─ Using the SLP method to obtain a simulation diagram of the preliminary layout of the workshop. ─ Optimize the layout of workshop equipment through genetic algorithm. ─ Use Plant Simulation software to build the equipment layout model of the workshop. ─ Verify the effectiveness of the simulation optimization program. Research methods. Visual modeling in the environment of Siemens Tecnomatix Plant Simulation 14.0, The preliminary layout of the workshop is obtained through the SLP method, and the optimal layout of the workshop is obtained through the genetic algorithm. Use Plant Simulation software for simulation verification. Scientific novelty of the obtained results. The research results carried out as part of the master's degree thesis have the following scientific novelties: ─ Using CATIA for virtual simulation of centrifugal compressor impeller processing. ─ Using SLP method for workshop layout simulation. ─ Use genetic algorithms to optimize the layout of the workshop. ─ Use Plant Simulation to simulate and optimize the layout of the workshop. Through comparative analysis, the optimal layout of the centrifugal compressor impeller processing workshop is obtained. ─ Combined with the statistical function of Tecnomatix Plant Simulation. Publications. 1. Xuhong Wei, Vorontsov B.S. Process simulation of automatic workshop based on Tecnomatix / Wei Xuhong, Б.С. Воронцов // Молода наука - роботизація і нано- технології сучасного машинобудування: зб. наук. праць Міжнар. молодіжної наук.- техн. конф., 14-15 квітня 2021 р. – Краматорськ : ДДМА, 2021. – С.36-39. 5 2. Xuhong Wei, Vorontsov B.S. Research on a new type of centrifugal lathe fixture / Wei Xuhong, Б.С. Воронцов // Комплексне забезпечення якості технологічних процесів та систем (КЗЯТПС – 2021): XІ Міжнар. наук.-практ. конф., 26-27 травня 2021 р.: тези доп. – Чернігів : НУ «Чернігівська політехніка», 2021. – Т. 1. – С. 29-30

International Logistics

In this study guide the essence, the basic conceptions and the role of international logistics in economic development, the international and organizational aspects of procurement logistics, international warehousing, conceptual foundations of distribution logistics and inernational transport logistics are examined. This study guide is intended for students of specialty “International Economic Relations”

A metaheuristic and simheuristic approach for the p-Hub median problem from a telecommunication perspective

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Avanços recentes no setor das telecomunicações oferecem grandes oportunidades para cidadãos e organizações em um mundo globalmente conectado, ao mesmo tempo em que surge um vasto número de desafios complexos que os engenheiros devem enfrentar. Alguns desses desafios podem ser modelados como problemas de otimização. Alguns exemplos incluem o problema de alocação de recursos em redes de comunicações, desenho de topologias de rede que satisfaça determinadas propriedades associadas a requisitos de qualidade de serviço, sobreposição de redes multicast e outros recursos importantes para comunicação de origem a destino. O primeiro objetivo desta tese é fornecer uma revisão sobre como as metaheurísticas têm sido usadas até agora para lidar com os problemas de otimização associados aos sistemas de telecomunicações, detectando as principais tendências e desafios. Particularmente, a análise enfoca os problemas de desenho, roteamento e alocação de recursos. Além disso, devido á natureza desses desafios, o presente trabalho discute como a hibridização de metaheurísticas com metodologias como simulação pode ser empregada para ampliar as capacidades das metaheurísticas na resolução de problemas de otimização estocásticos na indústria de telecomunicações. Logo, é analisado um problema de otimização com aplicações práticas para redes de telecomunica ções: o problema das p medianas não capacitado em que um número fixo de hubs tem capacidade ilimitada, cada nó não-hub é alocado para um único hub e o número de hubs é conhecido de antemão, sendo analisado em cenários determinísticos e estocásticos. Dada a sua variedade e importância prática, o problema das p medianas vem sendo aplicado e estudado em vários contextos. Seguidamente, propõem-se dois algoritmos imune-inspirados e uma metaheurística de dois estágios, que se baseia na combinação de técnicas tendenciosas e aleatórias com uma estrutura de busca local iterada, além de sua integração com a técnica de simulação de Monte Carlo para resolver o problema das p medianas. Para demonstrar a eficiência dos algoritmos, uma série de testes computacionais é realizada, utilizando instâncias de grande porte da literatura. Estes resultados contribuem para uma compreensão mais profunda da eficácia das metaheurísticas empregadas para resolver o problema das p medianas em redes pequenas e grandes. Por último, uma aplicaçã o ilustrativa do problema das p medianas é apresentada, bem como alguns insights sobre novas possibilidades para ele, estendendo a metodologia proposta para ambientes da vida real.Recent advances in the telecommunication industry o er great opportunities to citizens and organizations in a globally-connected world, but they also arise a vast number of complex challenges that decision makers must face. Some of these challenges can be modeled as optimization problems. Examples include the framework of network utility maximization for resource allocation in communication networks, nding a network topology that satis es certain properties associated with quality of service requirements, overlay multicast networks, and other important features for source to destination communication. First, this thesis provides a review on how metaheuristics have been used so far to deal with optimization problems associated with telecommunication systems, detecting the main trends and challenges. Particularly the analysis focuses on the network design, routing, and allocation problems. In addition, due to the nature of these challenges, this work discusses how the hybridization of metaheuristics with methodologies such as simulation can be employed to extend the capabilities of metaheuristics when solving stochastic optimization problems. Then, a popular optimization problem with practical applications to the design of telecommunication networks: the Uncapacitated Single Allocation p-Hub Median Problem (USApHMP) where a xed number of hubs have unlimited capacity, each non-hub node is allocated to a single hub and the number of hubs is known in advance is analyzed in deterministic and stochastic scenarios. p-hub median problems are concerned with optimality of telecommunication and transshipment networks, and seek to minimize the cost of transportation or establishing. Next, two immune inspired metaheuristics are proposed to solve the USApHMP, besides that, a two-stage metaheuristic which relies on the combination of biased-randomized techniques with an iterated local search framework and its integration with simulation Monte Carlo technique for solving the same problem is proposed. In order to show their e ciency, a series of computational tests are carried out using small and large size instances from the literature. These results contribute to a deeper understanding of the e ectiveness of the employed metaheuristics for solving the USApHMP in small and large networks. Finally, an illustrative application of the USApHMP is presented as well as some insights about some new possibilities for it, extending the proposed methodology to real-life environments.Els últims avenços en la industria de les telecomunicacions ofereixen grans oportunitats per ciutadans i organitzacions en un món globalment connectat, però a la vegada, presenten reptes als que s'enfronten tècnics i enginyers que prenen decisions. Alguns d'aquests reptes es poden modelitzar com problemes d'optimització. Exemples inclouen l'assignació de recursos a les xarxes de comunicació, trobant una topologia de xarxa que satisfà certes propietats associades a requisits de qualitat de servei, xarxes multicast superposades i altres funcions importants per a la comunicació origen a destinació. El primer objectiu d'aquest treball és proporcionar un revisió de la literatura sobre com s'han utilitzat aquestes tècniques, tradicionalment, per tractar els problemes d'optimització associats a sistemes de telecomunicació, detectant les principals tendències i desa aments. Particularment, l'estudi es centra en els problemes de disseny de xarxes, enrutament i problemes d'assignació de recursos. Degut a la naturalesa d'aquests problemes, aquest treball també analitza com es poden combinar les tècniques metaheurístiques amb metodologies de simulació per ampliar les capacitats de resoldre problemes d'optimització estocàstics. A més, es tracta un popular problema d'optimització amb aplicacions pràctiques per xarxes de telecomunicació, el problema de la p mediana no capacitat, analitzant-lo des d'escenaris deterministes i estocàstics. Aquest problema consisteix en determinar el nombre d'instal lacions (medianes) en una xarxa, minimitzant la suma de tots els costs o distàncies des d'un punt de demanda a la instal lació més propera. En general, el problema de la p mediana està lligat amb l'optimització de xarxes de telecomunicacions i de transport, i busquen minimitzar el cost de transport o establiment de la xarxa. Es proposa dos algoritmes immunològics i un algoritme metaheurístic de dues etapes basat en la combinació de tècniques aleatòries amb simulacions Monte Carlo. L'e ciència de les algoritmes es posa a prova mitjançant alguns dels test computacionals més utilitzats a la literatura, obtenint uns resultats molt satisfactoris, ja que es capaç de resoldre casos petits i grans en qüestió de segons i amb un baix cost computacional. Finalment, es presenta una aplicació il lustrativa del problema de la p mediana, així com algunes noves idees sobre aquest, que estenen la metodologia proposta a problemes de la vida real

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

A multi-objective imperialist competitive algorithm for integrating intra-cell layout and processing route reliability in a cellular manufacturing system

International audienc

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words