Balancing and Sequencing of Mixed Model Assembly Lines

Assembly lines are cost efficient production systems that mass produce identical products. Due to customer demand, manufacturers use mixed model assembly lines to produce customized products that are not identical. To stay efficient, management decisions for the line such as number of workers and assembly task assignment to stations need to be optimized to increase throughput and decrease cost. In each station, the work to be done depends on the exact product configuration, and is not consistent across all products. In this dissertation, a mixed model line balancing integer program (IP) that considers parallel workers, zoning, task assignment, and ergonomic constraints with the objective of minimizing the number of workers is proposed. Upon observing the limitation of the IP, a Constraint Programming (CP) model that is based on CPLEX CP Optimizer is developed to solve larger assembly line balancing problems. Data from an automotive OEM are used to assess the performance of both the MIP and CP models. Using the OEM data, we show that the CP model outperforms the IP model for bigger problems. A sensitivity analysis is done to assess the cost of enforcing some of the constraint on the computation complexity and the amount of violations to these constraints once they are disabled. Results show that some of the constraints are helpful in reducing the computation time. Specifically, the assignment constraints in which decision variables are fixed or bounded result in a smaller search space. Finally, since the line balance for mixed model is based on task duration averages, we propose a mixed model sequencing model that minimize the number of overload situation that might occur due to variability in tasks times by providing an optimal production sequence. We consider the skip-policy to manage overload situations and allow interactions between stations via workers swimming. An IP model formulation is proposed and a GRASP solution heuristic is developed to solve the problem. Data from the literature are used to assess the performance of the developed heuristic and to show the benefit of swimming in reducing work overload situations

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

Metaheuristic approach to solving U-shaped assembly line balancing problems using a rule-base coded genetic algorithm

Includes bibliographical references.2015 Summer.The need to achieve line balancing for a U-shaped production line to minimize production time and cost is a problem frequently encountered in industry. This research presents an efficient and quick algorithm to solve the U-shape line-balancing problem. Heuristic rules used to solve a straight line-balancing problem (LBP) were modified and adapted so they could be applied in a U-shape line-balancing problem model. By themselves, the heuristic rules, which were adapted from straight-line systems, can produce good solutions for the U-shape LBP, however, there is nothing that guarantees that this will be the case. One way to achieve improved solutions using heuristic rules can be accomplished by using a number of rules simultaneously to break ties during the task assignment process. In addition to the use of heuristic and simultaneous heuristic rules, basic genetic operations were used to further improve the performance of the assignment process and thus obtain better solutions. Two genetic algorithms are introduced in this research: a direct-coded and an indirect-coded model. The newly introduced algorithms were compared with well-known problems from literature and their performance as compared to other heuristic approaches showed that they perform well. The indirect-coded genetic algorithm uses the adapted heuristic rules from the LBP as genes to find the solutions to the problem. In the direct-coded algorithm, each gene represents an operation in the LBP and the position of the gene in the chromosome represents the order in which an operation, or task, will be assigned to a workstation. The indirect-coded genetic algorithm introduces sixteen heuristic rules adapted from the straight LBP for use in a U-shape LBP. Each heuristic rule was represented inside the chromosome as a gene. The rules were implemented in a way that precedence is preserved and at the same time, facilitate the use of genetic operations. Comparing the algorithm’s results with known results from literature, it obtained better solutions in 26% of the cases; it obtained an equivalent solution in 62% of the cases (not better, not worse); and a worse solution the remaining 12%. The direct-coded genetic algorithm introduces a new way to construct an ordered arrangement of the task assignation without violating any precedence. This method consists of creating a diagram that is isomorphic to the original precedence diagram to facilitate the construction of the chromosome. Also, crossover and mutation operations are conducted in a way that precedence relations are not violated. The direct-coded genetic algorithm was tested with the same set of problems as the indirect-coded algorithm. It obtained better solutions than the known solutions from literature in 22% of the cases; 72% of the problems had an equivalent solution; and 6% of the time it generated a solution less successful than the solution from literature. Something that had not been used in other genetic algorithm studies is a response surface methodology to optimize the levels for the parameters that are involved in the response model. The response surface methodology is used to find the best values for the parameters (% of children, % of mutations, number of genes, number of chromosomes) to produce good solutions for problems of different sizes (large, medium, small). This allows for the best solution to be obtained in a minimum amount of time, thus saving computational effort. Even though both algorithms produce good solutions, the direct-coded genetic algorithm option requires less computational effort. Knowing the capabilities of genetic algorithms, they were then tested in two real industry problems to improve assembly-line functions. This resulted in increased efficiency in both production lines

Integration of QRM and ergonomics in the design of a framework in identification complaints among automotive assembly line workers

The assembly line is the most critical area of automotive manufacturing. The smoothness of the production process depends on the situation and conditions of the environment and its workers. The assembly process is done manually by using humans to install all the related components in the production line. Complaints felt by workers during the manufacturing process can hinder the smooth running of production in meeting capacity, thus affecting the company's performance. Therefore, the purpose of this study is to design a framework for identifying workers' complaints by using a combination of Quick Response Manufacturing (QRM) and ergonomics. This framework is expected to identify grievances felt by workers from all aspects of the assembly environment that could potentially impact employment grievances. Framework design is created using the main concept of QRM which consists of time is money, tailoring your dynamics, focusing on the target market segment and thinking gold. Each of these concepts contains ergonomic elements such as workload variables and complaints of musculoskeletal disorders related to production schedules, production time, overtime, facility layout and equipment used. It is hoped that this framework can achieve the desired goal of minimizing work risk in optimizing the production process of the assembly line

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

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