Assembly Line

An assembly line is a manufacturing process in which parts are added to a product in a sequential manner using optimally planned logistics to create a finished product in the fastest possible way. It is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The present edited book is a collection of 12 chapters written by experts and well-known professionals of the field. The volume is organized in three parts according to the last research works in assembly line subject. The first part of the book is devoted to the assembly line balancing problem. It includes chapters dealing with different problems of ALBP. In the second part of the book some optimization problems in assembly line structure are considered. In many situations there are several contradictory goals that have to be satisfied simultaneously. The third part of the book deals with testing problems in assembly line. This section gives an overview on new trends, techniques and methodologies for testing the quality of a product at the end of the assembling line

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

Time and space multi-manned assembly line balancing problem using genetic algorithm

Purpose: Time and Space assembly line balancing problem (TSALBP) is the problem of balancing the line taking the area required by the task and to store the tools into consideration. This area is important to be considered to minimize unplanned traveling distance by the workers and consequently unplanned time waste. Although TSALBP is a realistic problem that express the real-life situation, and it became more practical to consider multi-manned assembly line to get better space utilization, few literatures addressed the problem of time and space in simple assembly line and only one in multi-manned assembly line. In this paper the problem of balancing bi-objective time and space multi-manned assembly line is proposed Design/methodology/approach: Hybrid genetic algorithm under time and space constraints besides assembly line conventional constraints is used to model this problem. The initial population is generated based on conventional assembly line heuristic added to random generations. The objective of this model is to minimize number of workers and number of stations. Findings: The results showed the effectiveness of the proposed model in solving multi-manned time and space assembly line problem. The proposed method gets better results in solving real-life Nissan problem compared to the literature. It is also found that there is a relationship between the variability of task time, maximum task time and cycle time on the solution of the problem. In some problem features it is more appropriate to solve the problem as simple assembly line than multi-manned assembly line. Originality/value: It is the first article to solve the problem of balancing multi-manned assembly line under time and area constraint using genetic algorithm. A relationship between the problem features and the solution is found according to it, the solution method (one sided or multi-manned) is definedPeer Reviewe

Industry 4.0—from Smart Factory to Cognitive Cyberphysical Production System and Cloud Manufacturing

This book focuses on recent developments in new industrial platforms, with Industry 4.0 on its way to becoming Industry 5.0. The book covers smart decision support systems for green and sustainable machining, microscale machining, cyber-physical production networks, and the optimization of assembly lines. The modern multiobjective algorithms and multicriteria decision-making methods are applied to various real-world industrial problems. The emerging problem of cybersecurity in advanced technologies is addressed as well

Aggregate assembly process planning for concurrent engineering

In today's consumer and economic climate, manufacturers are finding it increasingly difficult to produce finished products with increased functionality whilst fulfilling the aesthetic requirements of the consumer. To remain competitive, manufacturers must always look for ways to meet the faster, better, and cheaper mantra of today's economy. The ability for any industry to mirror the ideal world, where the design, manufacturing, and assembly process of a product would be perfected before it is put mto production, will undoubtedly save a great deal of time and money. This thesis introduces the concept of aggregate assembly process planning for the conceptual stages of design, with the aim of providing the methodology behind such an environment. The methodology is based on an aggregate product model and a connectivity model. Together, they encompass all the requirements needed to fully describe a product in terms of its assembly processes, providing a suitable means for generating assembly sequences. Two general-purpose heuristics methods namely, simulated annealing and genetic algorithms are used for the optimisation of assembly sequences generated, and the loading of the optimal assembly sequences on to workstations, generating an optimal assembly process plan for any given product. The main novelty of this work is in the mapping of the optimisation methods to the issue of assembly sequence generation and line balancing. This includes the formulation of the objective functions for optimismg assembly sequences and resource loading. Also novel to this work is the derivation of standard part assembly methodologies, used to establish and estimate functional tunes for standard assembly operations. The method is demonstrated using CAPABLEAssembly; a suite of interlinked modules that generates a pool of optimised assembly process plans using the concepts above. A total of nine industrial products have been modelled, four of which are the conceptual product models. The process plans generated to date have been tested on industrial assembly lines and in some cases yield an increase in the production rate

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

A STUDY ON GENERAL ASSEMBLY LINE BALANCING MODELING METHODS AND TECHNIQUES

The borders of the assembly line balancing problem, as classically drawn, are as clear as any other operations research topic in production planning, with well-defined sets of assumptions, parameters, and objective functions. In application, however, these borders are frequently transgressed. Many of these deviations are internal to the assembly line balancing problem itself, arising from any of a wide array of physical or technological features in modern assembly lines. Other issues are founded in the tight coupling of assembly line balancing with external production planning and management problems, as assembly lines are at the intersection of multiple related problems in job sequencing, part flow logistics, worker safety, and quality. The field of General Assembly Line Balancing is devoted to studying the class of adapted and extended solution techniques necessary in order to model these applied line balancing problems. In this dissertation a complex line balancing problem is presented based on the real production environment of our industrial partner, featuring several extensions for task-to-task relationships, station characteristics limiting assignment, and parallel worker zoning interactions. A constructive heuristic is developed along with two improvement heuristics, as well as an integer programming model for the same problem. An experiment is conducted testing each of these new solution methods upon a battery of testbed problems, measuring solution quality, runtime, and achievement of feasibility. Additionally, a new method for measuring a secondary horizontal line balancing objective is established, based on the options-mix paradigm rather than the customary model-mix paradigm

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

ASALBP: the Alternative Subgraphs Assembly Line Balancing Problem. Formalization and Resolution Procedures

Hoy en día, los problemas de equilibrado de líneas de montaje se encuentran comúnmente en la mayoría de sistemas industriales y de manufactura. Básicamente, estos problemas consisten en asignar un conjunto de tareas a una secuencia ordenada de estaciones de trabajo, de manera que se respeten las restricciones de precedencia y se optimice una medida de eficiencia dada (como, por ejemplo, el número de estaciones de trabajo o el tiempo ciclo). Dada la complejidad de los problemas de equilibrado de líneas, en los trabajos de investigación tradicionalmente se consideraban numerosas simplificaciones en las que, por ejemplo, una sola línea serial procesaba un único modelo de un solo producto. Además, los problemas estaban principalmente restringidos por las relaciones de precedencia y el tiempo ciclo. Sin embargo, la disponibilidad de recursos computacionales de hoy en día, así como la necesidad de las empresas a adaptarse a los rápidos cambios en los procesos de producción, han motivado tanto a investigadores como a gerentes a tratar problemas más realistas. Algunos ejemplos incluyen problemas que procesan modelos mixtos, estaciones de trabajo y líneas en paralelo, consideran múltiples objetivos y restricciones adicionales, como la capacidad de proceso de las estaciones de trabajo y la ubicación de los recursos en la línea de montaje.Esta tesis doctoral trata un nuevo problema de equilibrado de líneas, que ha sido titulado ASALBP: the Alternative Subgraphs Assembly Line Balancing Problem, en el que se consideran variantes alternativas para diferentes partes de un proceso de montaje o de manufactura. Cada alternativa puede ser representada por un subgrafo de precedencias, que determina las tareas requeridas para procesar un producto particular, las restricciones de precedencia y los tiempos de proceso. Para resolver eficientemente el ASALBP, se deben resolver dos problemas simultáneamente: (1) el problema de decisión para seleccionar un subgrafo de montaje para cada parte que admite alternativas y (2) el problema de equilibrado para asignar las tareas a las estaciones de trabajo. El análisis del estado del arte revela que este problema no ha sido estudiado previamente en la literatura, lo que ha conducido a la caracterización y a la definición de un nuevo problema. Por otra parte, dado que no es posible representar las variantes de montaje en un diagrama de precedencias estándar, se propone el S-grafo como una herramienta de diagramación, para representar en un único grafo todas las alternativas de montaje.Habitualmente, los problemas de equilibrado de líneas que consideran alternativas de montaje se resuelven en dos etapas. En la etapa inicial, el diseñador de sistema selecciona una de las variantes posibles utilizando cierto criterio de decisión como por ejemplo tiempo total de proceso. Una vez que se han seleccionado las alternativas de montaje, y se dispone de un diagrama de precedencias (es decir, el problema de planificación ha sido resuelto), la línea de montaje es equilibrada en una segunda etapa. Sin embargo, utilizando dicho procedimiento de dos etapas no se puede garantizar que una solución óptima del problema global se pueda obtener, porque las decisiones tomadas por el diseñador de sistema restringen el problema y causan perdida de información; es decir, cuando se selecciona una alternativa priori los efectos de las posibilidades restantes quedan sin explorar. Por ejemplo, si el diseñador de sistema utiliza tiempo total de proceso como criterio de decisión, la alternativa con el tiempo total de proceso más grande será descartada a pesar de que pueda ser la que proporcione la mejor solución del problema (es decir, requiere el mínimo número de estaciones de trabajo o el mínimo tiempo ciclo). Por lo tanto, pareciera razonable considerar que para solucionar eficientemente un ALBP que implica alternativas de proceso, todas las alternativas de montaje deben ser tomadas en cuenta en el proceso de equilibrado. Para este propósito, en esta tesis el problema de selección de una variante de montaje y el problema de equilibrado de la línea se consideran conjuntamente en lugar de independientemente.Para resolver el Problema de Equilibrado de Líneas con Alternativas de Montaje (ASALBP) se usan varios enfoques. El problema se formaliza y se resuelve de manera óptima a través de dos modelos de programación matemática. Un enfoque aproximativo es usado para resolver problemas de tamaño industrial. Además, se proponen procedimientos de optimización local con el objetivo de mejorar la calidad de las soluciones obtenidas por los métodos heurísticos desarrollados en este trabajo.Nowadays assembly line balancing problems are commonly found in most industrial and manufacturing systems. Basically, these problems seek to assign a set of assembly tasks to an ordered sequence of workstations in such a way that precedence constraints are maintained and a given efficiency measure (e.g. the number of workstations or the cycle time) is optimized.Because of the computational complexity of balancing problems, research works traditionally considered numerous simplifying assumptions in which, for example, a single model of a unique product were processed in a single line; moreover, problems were mainly restricted by precedence and cycle time constrains. Nevertheless, the current availability of computing resources and the enterprises need to adapt to rapid changes in production and manufacturing processes have encouraged researchers and decision-makers to address more realistic problems. Some examples include problems that involve mixed models, parallel workstations and parallel lines, multiple objectives and also further restrictions such as workstation processing capacity and resource allocation constraints. This doctoral thesis addresses a novel assembly line balancing problem, entitled here ASALBP: the Alternative Subgraphs Assembly Line Balancing Problem, which considers alternative variants for different parts of an assembly or manufacturing process. Each variant can be represented by a precedence subgraph that establishes the tasks required to process a particular product, their precedence requirements and their processing times. Therefore, to efficiently solve the Alternative Subgraphs Assembly Line Balancing Problem two subproblems need to be solved simultaneously: (1) the decision problem that selects one assembly variant for each part that admit alternatives and (2) the balancing problem that assigns the tasks to the workstations. The analysis of the state-of-the-art carried out revealed that the Alternative Subgraphs Assembly Line Balancing Problem has not been addressed before in literature studies, which leaded to the characterization and definition of this new problem. Moreover, due to the impossibility of representing assembly variants in a standard precedence graph, the S-Graph is proposed here as a diagramming tool to represent all available assembly alternatives in a unique diagram. Habitually, problems involving assembly alternatives are solved by using a two-stage based approach. In the initial stage, the system designer selects one of the possible variants according to criteria such as total processing time. Once the assembly alternatives have been selected, and a precedence graph is available (i.e. the assembly planning problem has been already solved), the line is then balanced in the second stage. However, by following this two-stage procedure it cannot be guaranteed that an optimal solution of the global problem can be obtained, because the decisions taken by the system designer restrict the problem and cause information loss; i.e., a priori selection of an alternative leaves the effects of the other possibilities unexplored. For instance, if the system designer uses total processing time as decision criterion, the alternative with largest total processing time will be discarded notwithstanding it may provide the best solution of the problem (i.e., it requires the minimum number of workstations or minimum cycle time). Therefore, it seems reasonable to consider that to solve efficiently an ALBP that involves processing alternatives all possibilities must be considered within the balancing process. For this purpose, in this thesis both the variant selection problem and the balancing problem are jointly considered instead of independently.Different approaches are used here to address the Alternative Subgraphs Assembly Line Balancing Problem (ASALBP). The problem is formalize and optimally solved by means of two mathematical programming models. An approximate approach is used to address industrial-scale problems. Furthermore, local optimization procedures are proposed aiming at improving the quality of the solutions provided by all heuristic methods developed here

Best matching processes in distributed systems

The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individuals—from clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory. The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies