Optimization Of Assembly Line Research Articles

A literature review on assembly line optimization using data structure algorithm

In the automotive industry and other manufacturing sectors, a final assembly line is responsible for assembling different components of a product in the production system. The productivity of a manufacturing process is determined by its duration, the quantity of workstations employed, and the proficiency of its operators. Therefore, in order to effectively oversee or rival several industrial sectors, or to enhance efficiency in a certain assembly line to uphold productivity. This study details the enhancement process of a four-circuit pressure valve assembly line through the utilization of simulation approaches. This study proposes the utilization of data structure as a means to enhance the efficiency of assembly line operations. The main objectives of this program are to reduce workstation operations and enhance product output, as well as to offer recommendations for optimizing the manufacturing process to attain superior outcomes. The study utilized qualitative methodologies, whereby the procedural sequence was observed and data was gathered in accordance with the data framework specifications. The data structure was built using the collected data, followed by analysis of the results. Subsequently, new simulations were developed, incorporating improvements, to finalize the outputs.

Human-robot collaboration in assembly line balancing problems: Review and research gaps

Researchers have been working on assembly line optimization problems, especially assembly line balancing, for decades. Integration of Industry 4.0 technologies into assembly lines creates new challenges in optimal resource selection, task allocation, line balance, and scheduling decisions, to name a few. Over the last decade, researchers have developed novel frameworks for conventional assembly line optimization problems that comply with the recent industrial revolution and leading technologies. State-of-the-art Balancing problems in assembly lines with collaborative robots are the core of this study as a key decision in Assembly 4.0. The collaboration of humans and robots in a fenceless environment requires a more comprehensive approach than the traditional task-station assignment in a balancing model. Resource selection/allocation and scheduling are the most common decisions that researchers combine with balancing models. Thus, the studies on all these optimization challenges in the last ten years through the Web of Science and Engineering Village are present in the current review. We analyze the articles’ statistics (e.g., year and journals) and contents to find the research trends and gaps for future research direction in collaborative robotic assembly line optimization which incorporates occupational health and safety.

Retracted: Research on Automobile Assembly Line Optimization Based on Industrial Engineering Technology and Machine Learning Algorithm

Retracted: Research on Automobile Assembly Line Optimization Based on Industrial Engineering Technology and Machine Learning Algorithm

Assembly Line Optimization Using MTM Time Standard and Simulation Modeling—A Case Study

This study presents an approach to solving the assembly line balancing problem (ALBP) using the Methods-Time Measurement (MTM) time standard and simulation software. ALBP is a common problem in manufacturing where a set of tasks with fixed times must be assigned to a series of sequential workstations in order to minimize the total idle time and reduce the assembly cost per product. This study uses MTM, a widely used production process scheduling method, to create a new time analysis of an assembly process that was previously balanced using the Work-Factor method and time study. This literature review shows that there are a lack of combinations of updated time analyses with newer simulation approaches in the current literature, and this was the motivation for the present work. An assembly line simulation was performed using Simio software to evaluate different design options and operating scenarios. The results show that the use of MTM and simulation can help minimize idle time and improve assembly line performance, thereby reducing costs and increasing efficiency. This study shows that the approach of using MTM and simulation is effective in solving ALBP and is a useful tool for manufacturing companies to improve the performance of their assembly lines and reduce costs.

Research on optimization of assembly line based on product scheduling and just-in-time feeding of parts

PurposeReducing production auxiliary time is the key to improve the efficiency of the existing mixed-flow assembly line. This paper proposes a method combining improved genetic algorithm (GA) and Flexsim software. It also investigates mixed-flow assembly line scheduling and just-in-time (JIT) parts feeding scheme to reduce waste in production while taking the existing hill-drop mixed-flow assembly line as an example to verify the effectiveness of the method.Design/methodology/approachIn this research, a method is presented to optimize the efficiency of the present assembly line. The multi-objective mathematical model is established based on the objective function of the minimum production cycle and part consumption balance, and the solution model is developed using multi-objective GA to obtain the mixed flow scheduling scheme of the hill-drop planter. Furthermore, modeling and simulation with Flexsim software are investigated along with the contents of line inventory, parts transportation means, daily feeding times and time points.FindingsTheoretical analysis and simulation experiments are carried out in this paper while taking an example of a hill-drop planter mixed-flow assembly line. The results indicate that the method can effectively reduce the idle and overload of the assembly line, use the transportation resources rationally and decrease the accumulation of the line inventory.Originality/valueThe method of combining improved GA and Flexsim software was used here for the first time intuitively and efficiently to study the balance of existing production lines and JIT feeding of parts. Investigating the production scheduling scheme provides a reference for the enterprise production line accompanied by the quantity allocation of transportation tools, the inventory consumption of the spare parts along the line and the utilization rate of each station to reduce the auxiliary time and apply practically.

Research on Intelligent Assembly Modes of Aerospace Products Based on Digital Twin

Based on the multi-varieties and scalable batch production mixed line assembly of aerospace products, this paper proposes the technical approach and system architecture of an intelligent assembly integration platform which is composed of hardware layer, software layer and application layer from bottom up, so as to construct an intelligent assembly integration application platform based on the digital twin. Hardware layer is composed of the equipment and communication, software layer mainly includes the database and functional modules, and application layer is constructed based on a digital twin model, realizing visual monitoring of the assembly process. This paper establishes an intelligent monitoring mode based on digital twin and an intelligent control mode based on two-layer optimization. At the workshop layer, a digital twin mode of entities is constructed so as to form a plan optimization information, logistics optimization information and quality optimization information, and post control optimized execution information is sent back to the workshop operational layer. At the assemble line layer, real-time information is acquired, equipment monitoring is realized, and optimized information is sent back to the workshop operational layer, achieving control and optimization of assembly lines. This paper reshapes processes under this intelligent assembly mode; and verify its application in aerospace product assembly.

Retracted] Research on Automobile Assembly Line Optimization Based on Industrial Engineering Technology and Machine Learning Algorithm

Aiming at the lack of search depth of traditional genetic algorithm in automobile assembly line balance optimization, an improved genetic algorithm based on bagging integrated clustering is proposed for balance optimization. Through the integrated learning of several K‐means algorithm based learners through bagging, a population clustering analysis method based on bagging integrated clustering algorithm is established, and then, a dual objective automobile assembly line balance optimization model is established. The population clustering analysis method is used to improve the intersection link of genetic algorithm to improve the search depth. The effectiveness and search performance of the improved genetic algorithm in solving the double objective assembly line balance problem are verified in an example.

PROBLEM SOLVING IN CROSS OVER LINE BALANCING USING HHO

The assembly line is a series of workflow manufacturing structures that are still common in large scale manufacturing of sustainable items. In the unbalanced tax system, more idle time is required to complete the task; A similar hanging problem arises when the tasks are not properly planned. This article proposes a technique to the line balance problem with crossover workstations. Different techniques were developed to resolve assembly line balance troubles. Existing methods for solving the assembly line balance problem can’t be implemented to included type issues. Using the Harris Hawks optimization, improve the performance of a network-based line balancing problem. The assembly line optimization is performed using the Harris Hawks optimization cycle multipoint crossover method to acquire actual posterity and the high-quality chromosome is chosen to determine the performance of the sequential construction system.

Mixed-model assembly line balancing problem with tasks assignment

Modern production systems are focused on the objectives dictated by customer needs oriented market. The best results are obtained by production systems combining the advantages of mass production with the possibility of implementing product variants corresponding to the needs of an individual customer. An Example of the production system that meets those conditions is mixed-model assembly line. The main problem of this solution is to plan the order of operations and assign the operators’ tasks in such a way to obtain the best balance of a given line. In this study, several metaheuristic methods that can be used to solve line balancing and tasks assignment problems are presented. The purpose of this study is to show the advantage of simulated annealing over other methods in solving the described problem. This paper is introduction to future research in scope of mixed-model assembly line optimization focused on employees’ effectiveness.

Optimization of Assembly Line of An Eicher Tractor Unit

Line balancing is about arranging a production line so that there is an even flow of production from one work station to the next. Line balancing also a successful tool to reduce bottleneck by balancing the task time of each work station so that there is no delays and nobody is overburden with their task. This thesis presents a case study on a line balancing problem in an eicher company and the study focused on assembly line. This study aims to improve the productivity and line efficiency also to recommend improvement activities based on the line balancing and analysis done in the simulation model. The simulation was done by using Tecnomatix Plant Simulation. All the data needed for the line balancing analysis was collected and a line balancing model equipped with manual calculation was done. This data gathered is then simulated in Tecnomatix Plant Simulation. Among the improvement activities conducted in order to balance the line was combining a few process into one, transformation from manual process to mechanization, and removing waste from the line. Throughout the study, three layouts were proposed. Among these three layouts only one will be proposed to the company. The layout proposed has a better line efficiency and rate of productivity.

An improved immune clonal selection algorithm for bi-objective robotic assemble line balancing problems considering time and space constraints

PurposeThe extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing method of both workstation time and station area to improve the efficiency and productivity of the robotic assembly lines. A tradeoff was made between two conflicting objective functions, minimizing the number of workstations and minimizing the area of each workstation.Design/methodology/approachThis research proposes an optimal method for balancing robotic assembly lines with space consideration and reducing robot changeover and area for tools and fixtures to further minimize assembly line area and cycle time. Due to the NP-hard nature of the considered problem, an improved multi-objective immune clonal selection algorithm is proposed to solve this constrained multi-objective optimization problem, and a special coding scheme is designed for the problem. To enhance the performance of the algorithm, several strategies including elite strategy and global search are introduced.FindingsA set of instances of different problem scales are optimized and the results are compared with two other high-performing multi-objective algorithms to evaluate the efficiency and superiority of the proposed algorithm. It is found that the proposed method can efficiently solve the real-world size case of time and space robotic assembly line balancing problems.Originality/valueFor the first time in the robotic assembly line balancing problems, an assignment-based tool area and a sequence-based changeover time are took into consideration. Furthermore, a mathematical model with bi-objective functions of minimizing the number of workstations and area of each station was developed. To solve the proposed problem, an improved multi-objective immune clonal selection algorithm was proposed and a special coding scheme is designed.

Comparative analysis and optimization of Mixed Model assembly line using Genetic Algorithm

Line balancing aims at arrangement of different facilities like workstation, machines, tool, and operator in an adequate manner in order to obtain most efficient balancing of assembly line, its capabilities, flow of product and services. Various methods are proposed in the literature to optimize these parameters hence this research effort starts with the review of the various methods developed and to compare main features of few of them for their relative assets and limitation to allow choose the most suitable method for optimization of line balancing. The researchers used the average times of the tasks of the models as the timings of the tasks of the combined assembly line model while designing the assembly line which is an approximate method of dealing with the data. Hence, in this paper, the design of the assembly line of the models is done simultaneously for all the models using their original task times. Such an attempt gives a realistic design of the mixed-model assembly line using genetic algorithm for the balancing of this mixed model assembly line problem. A new methodology of balancing the assembly line has been adopted and the output is compared with conventional methods of line balancing. The optimization of assembly line using cyclic multipoint crossover method of genetic algorithm has been performed to obtained real offspring and the best chromosome is selected to find out the efficiency of assembly line. The superiority of this approach is demonstrated using a numerical example. The multi-objective genetic algorithm with cyclic crossover and with individual task times of the models is a unique contribution. Although the RPW method also gives better results other than any conventional methods of line balancing however when the population size (assembly lines) of the system increase and number of workstation are more than genetic algorithm method is suitable and gives better result and this research paper presents the comparative analysis of mixed model assembly line balancing methods and tries to find out the best possible solution in order to minimize no. of workstation, ideal time, bottleneck and increase the efficiency of plant

Multi-objective optimization of assembly lines with workers fatigue consideration

The work-related musculoskeletal disorders (MSDs) occur when the worker’s capabilities do not match the physical demands of work. In assembly lines, with the execution of repetitive tasks, workers are exposed to fatigue and ergonomics risks. Thus, there is a need to find compromises between assembly lines performance and physical demands and ergonomics.In this work, we introduce a general fatigue criterion for assessing workers fatigue. We propose a multi-objective mixed-integer linear programming model for the assembly line balancing problem with consideration of workers fatigue. We use ε-constraint approach to address both objectives and present the Pareto front. Experiments on instances from the literature are performed and discussed to highlight the trade-off between the numbers of workstations and fatigue.

Traveling worker assembly line (re)balancing problem: Model, reduction techniques, and real case studies

The assembly line balancing problem arises from equally dividing the workload among all workstations. Several solution methods explore different variants of the problem, but no model includes all characteristics real assembly lines might contain. This paper presents a mixed integer linear programming model that solves the Traveling Worker Assembly Line Balancing Problem (TWALBP). In this problem, the tasks’ balancing along with the assignment of workers to one or more workstations is determined for a given layout. The assignment flexibility is solved with a traveling salesman problem formulation integrated in the balancing model. Adapted standard datasets and three real case scenarios are used as benchmark sets. These scenarios present particularities such as human and robotic workers, assignment restrictions, zoning constraints, automatic and common tasks. The model successfully determines the tasks’ assignments and the routing of every worker for a layout aware optimization of assembly lines. Better quality balancing solutions were achieved allowing workers to perform tasks at multiple stations, showing a trade-off between assignment flexibility and movement time.

The optimization design of uniform’s hanging production line

Purpose – Garment manufacturing is a traditional industry with global competition. The most critical part is streamline, as it generally involves a great number of operations. The purpose of this paper is to, based on the model of group technology (GT), sum up the methods of assembly line optimization to optimize the streamline, so that the machines of the workstation can perform the assigned tasks with a balanced loading. Design/methodology/approach – In this paper, the methods of obtaining and optimizing data includes literature, research and optimization methods of basing on GT model. The main direction of literature has two aspects: Lean production theory and streamline assignment. Research mainly concludes three aspects: research place, research object and the research content. The method of time determination is averaging the testing values of repeated measurements. Optimization methods of basing on GT model mainly include combination of the same type of technologic processes in sequence, combination of independent branch process, the combination of mainstream and branch processes and offside combination of the same type of processes. Findings – After optimization, in the utilization rate of equipment: hanging system ratio was increased from 22.95 to 62.12 percent, which greatly improved the utilization rate of enterprise equipment. In equipment layout: hanging equipment layout was turned linear type into “U” type, which realized the synchronization of production. GT model will be well applied in the garment production streamline. Compared with other enterprises, A company has large garment hanging system, which reflects the advanced, comprehensive, representative. Therefore, the paper applied optimization methods to trousers, production efficiency has been improved greatly, the utilization rate of equipment is promoted and reworking phenomenon is reduced greatly. Research limitations/implications – Through the optimization of the production process and equipment layout, the A company’s compiling efficiency has been greatly improved, but how can the system become computerized, accurately and intelligence, which has been an important direction of research now. Practical implications – By optimizing, it is fully proved that applying GT to the streamline optimization is feasible. GT is an important branch of Lean production, summing up the optimization methods basing on the GT model to optimize the streamline not only enriches the relevant theory research, but also provides a theoretical basis for the practical production. GT model not only can be used in the shirt production process, as for other production processes, for example, trousers, skirts can also be widely applied, which realizes the theory application in the practical production. Originality/value – The originality of this paper is that through the analysis of the model of GT, the paper sums up the methods of assembly line optimization to optimize the streamline. Although the model of GT is used widely in electronics, automobile and industry, it is relatively weak in the optimization of garment production line, there are not many literatures on practical application of group technology in the clothing. Besides, the paper is applied to practical production, which not only can improve production efficiency, but also make the theoretical research have basis and combine the theory and practical production.

A Multiobjective Optimization Algorithm to Solve the Part Feeding Problem in Mixed-Model Assembly Lines

Different aspects of assembly line optimization have been extensively studied. Part feeding at assembly lines, however, is quite an undeveloped area of research. This study focuses on the optimization of part feeding at mixed-model assembly lines with respect to the Just-In-Time principle motivated by a real situation encountered at one of the major automobile assembly plants in Spain. The study presents a mixed integer linear programming model and a novel simulated annealing algorithm-based heuristic to pave the way for the minimization of the number of tours as well as inventory level. In order to evaluate the performance of the algorithm proposed and validate the mathematical model, a set of generated test problems and two real-life instances are solved. The solutions found by both the mathematical model and proposed algorithm are compared in terms of minimizing the number of tours and inventory levels, as well as a performance measure called workload variation. The results show that although the exact mathematical model had computational difficulty solving the problems, the proposed algorithm provides good solutions in a short computational time.

Synthesis of first-order dynamic programming algorithms

To solve a problem with a dynamic programming algorithm, one must reformulate the problem such that its solution can be formed from solutions to overlapping subproblems. Because overlapping subproblems may not be apparent in the specification, it is desirable to obtain the algorithm directly from the specification. We describe a semi-automatic synthesizer of linear-time dynamic programming algorithms. The programmer supplies a declarative specification of the problem and the operators that might appear in the solution. The synthesizer obtains the algorithm by searching a space of candidate algorithms; internally, the search is implemented with constraint solving. The space of candidate algorithms is defined with a program template reusable across all linear-time dynamic programming algorithms, which we characterize as first-order recurrences. This paper focuses on how to write the template so that the constraint solving process scales to real-world linear-time dynamic programming algorithms. We show how to reduce the space with (i)~symmetry reduction and (ii)~domain knowledge of dynamic programming algorithms. We have synthesized algorithms for variants of maximal substring matching, an assembly-line optimization, and the extended Euclid algorithm. We have also synthesized a problem outside the class of first-order recurrences, by composing three instances of the algorithm template.

Developing and Deploying Electronics Assembly Line Optimization Tools: A Motorola Case Study

The assignment of workloads to production equipment is one category of planning decision for an electronics assembly factory. In practice, line balancing requires not only selecting machines with sufficient placement accuracy and feeder capacity, but also address- ing a host of other operational objectives and constraints. Motorola Labs led a multi-year effort to apply mathematical programming to balance a variety of production mix and vol- ume scenarios. By representing the optimization problem as a specially structured, mixed linear-integer program, we were able to incorporate a high degree of reality in the model, simultaneously optimizing fixed setups, handling custom parts, maximizing machine uptime, and mitigating secondary bottlenecks. This paper presents the story of how we developed and deployed a software solution that significantly improved assembly cycle times, setup changeovers, and overall factory productivity, saving the company tens of millions of dollars.

Bounds for the cardinality constrainedP?Cmax problem

We consider the generalization of the classical P∥Cmax problem arising when a given limit k is imposed on the number of jobs that can be assigned to any machine. This generalization has practical interest in the optimization of assembly lines for printed circuit boards (PCB). The problem is strongly NP-hard for general k, it is solvable in O(n log n) time for fixed k=2, while it remains strongly NP-hard for any fixed k⩾3. We consider immediate adaptations of simple upper and lower bounds for P∥Cmax, and analyse their worst-case behaviour. We show that the cardinality constraint does not strengthen the LP relaxation of the problem, and that the worst-case performance of the bounds for P∥Cmax generally worsen when they are adapted to the new problem. New specifically tailored lower bounds are introduced, and their average tightness is evaluated through extensive computational experiments on randomly generated test instances. Copyright © 2001 John Wiley & Sons, Ltd.