Two-sided Assembly Line Research Articles

A Decomposition Approach for Sequencing Mixed-Model Two-Sided Assembly Line with Stochastic Processing Time

This study tackles the challenge of optimizing mixed-model two-sided assembly lines, where task processing times are uncertain. The objective is to reduce the expected cycle time the average time to complete one product across the assembly line. Given the complexity of assessing objectives amidst stochastic conditions, we formulate the problem as a simulation optimization problem. We introduce a strategic decomposition method that breaks down the core problem into two discrete sub-tasks: allocating tasks to respective work-stations, and determining the sequence of tasks at each station. The decomposition framework systematically partitions the solution space, though it does not ensure a global optimum, it can efficiently guide the search towards a high-quality near-optimal solution with a practical time frame. Based on this framework, we develop a novel simulation-optimization algorithm, termed the Decomposition Approach with Harmony Search (DAHS), which incorporates a harmony search heuristic to effectively navigate the partitioned solution space. Additionally, we implement two innovative strategies to improve the search and simulation procedures. Numerical experiments reveal that our DAHS algorithm outperforms benchmark algorithms in terms of solution quality and computational efficiency.

Two-sided resource-constrained assembly line balancing problem: a new mathematical model and an improved genetic algorithm

Two-sided assembly lines are typically employed in the production of medium and large-sized products with the aim of reducing the length of the assembly line, enhancing assembly efficiency and consequently reducing the time required for product assembly. However, traditional Two-sided assembly lines lack effective resource scheduling management methods in production scheduling, which results in low productivity and high resource costs. In order to address this issue, we propose a new two-sided resource-constrained assembly line balancing problem (TRCLBP) model. The model takes the minimum number of workstations and the minimum assembly cost as its objective function and proposes an improved genetic algorithm (I-GA) to solve it. A three-layer chromosome initialization method is proposed for the assembly tasks and resource decisions, which effectively improves the diversity and quality of the initial population. Furthermore, the algorithm employs strategies such as matching crossover and redistributing variants to ensure rapid convergence of the populations and to prevent them from falling into local optimums. Finally, the efficacy of the model and algorithm proposed in this paper is validated through a comprehensive analysis of arithmetic case studies and enterprise engineering examples. This analysis reveals a reduction of approximately 18 % in the total cost of assembly. Furthermore, the model enables enterprises to make informed decisions regarding the optimal allocation of resources, thereby reducing production costs and improving the efficiency of assembly operations during periods of expansion.

Multi-objective simulated annealing algorithm for robotic mixed-model two-sided assembly line balancing with setup times and multiple constraints

Robotic mixed-model two-sided assembly lines (RMTALs) have become increasingly prevalent in manufacturing industries for productivity enhancement. However, only limited attention has been paid to the RMTAL balancing problems that contain both setup times and multiple constraints such as positional constraints, zoning constraints, and synchronism constraints. Given the coexistence of these aspects in real-world scenarios, this study aims to address the RMTAL balancing problem with setup times and the mentioned constraints. To formulate the problem, a mixed integer programming model is proposed to optimize cycle time and total energy consumption. To handle multiple constraints and setup times, a new method based on four-vector encoding is introduced. This method addresses the three constraints during the encoding phase using four intervals and five rules, while setup times are processed during the decoding phase. To solve the problem, a Pareto entropy-based two-mode multi-objective simulated annealing algorithm is developed. The algorithm employs variable neighborhood descent algorithm with switchable objectives to generate the initial Pareto archive. Subsequently, it selects the initial solution with minimal potential in the Pareto archive, alternating between two search modes of exploration and exploitation based on the entropy difference of Pareto solutions. Comparative experiments with four state-of-the-art algorithms on benchmark instances of varying scales demonstrate that the proposed algorithm outperforms the others in over 93% of instances for the hypervolume ratio and inverted generational distance metrics.

A Knowledge-Assisted Variable Neighborhood Search for Two-Sided Assembly Line Balancing Considering Preventive Maintenance Scenarios

A Knowledge-Assisted Variable Neighborhood Search for Two-Sided Assembly Line Balancing Considering Preventive Maintenance Scenarios

Improved ant colony algorithm for the mixed-model parallel two-sided assembly lines balancing problem

ABSTRACT In response to the increasing demand for individualized products in the ‘small-lot, multi-variety’ market, there is an urgent need to enhance the efficiency and intelligence level of assembly lines. To address this, a novel solution method for the mixed-model parallel two-sided assembly lines balancing problem (MMPTSALBP) is proposed. The first step is to define the type and layout of the parallel two-sided assembly lines. Next, a mathematical model is constructed with the objective of minimizing the number of workstations required for the MMPTSALBP, proposing an improved ant colony algorithm that utilizes double string representation for the initial solution encoding method and a double pheromone matrix update to solve the model. Its efficiency is tested on benchmark datasets according to some performance measures and classifications. A study validates its effectiveness on 25 classical arithmetic cases by comparison with the solutions of an heuristic algorithm and an artificial fish swarm algorithm that had been reported to perform well.

Solving human-robot collaborative mixed-model two-sided assembly line balancing using multi-objective discrete artificial bee colony algorithm

Human-robot collaboration (HRC) is an emerging technology that aims to optimize production efficiency and reduce ergonomic risks by enabling humans and collaborative robots to work together in a shared workspace. This paper investigates HRC mixed-model two-sided assembly line balancing problem, which is a challenging real-world problem in manufacturing systems. The problem is formulated using a mixed-integer programming model with the objectives of minimizing ergonomic risk, energy consumption, and cycle time. To solve the problem efficiently, a multi-objective discrete artificial bee colony algorithm with specialist bees (MDABCSB) is proposed. MDABCSB utilizes a new five-vector encoding and decoding scheme and incorporates two types of neighbor structures for each vector. Additionally, the algorithm introduces specialist bees to search for the optimal solution for each objective, combines a tabu list to help them jump out of local optima, and employs variable neighborhood descent to enhance their exploration capabilities. Furthermore, the algorithm adopts a new temperature-based food sources update mechanism to probabilistically update the food sources of employed bees to reinforce exploitation. By comparing the proposed algorithm with other algorithms, the results demonstrate that MDABCSB performs better in terms of solution performance.

A genetic algorithm for balancing and sequencing of mixed-model two-sided assembly line with unpaced synchronous transfer

Research on the assembly line balancing problem has been continuously incorporated the characteristics of actual production lines without considering the mixed-model two-sided assembly line with unpaced synchronous transfer, which is common in body shops of cars or trucks. This study attempts to model a mixed-model two-sided assembly line balancing and sequencing problem with unpaced synchronous transfer, and designs a genetic algorithm to solve this problem. First, to describe the process whereby model sequence and its execution, station and its number, and the process time caused by task allocation lead to the variable output of an unpaced synchronous line, a model without an observation point restriction is proposed. Based on this, a mathematical model of a balancing and sequencing problem of a mixed-model two-sided assembly line with an unpaced synchronous transfer is established, in which the balancing problem is aimed at Type-II. Then, an improved genetic algorithm adapted to the complexity of the problem is proposed. The algorithm designs a combination and evaluation mechanism to ensure that the solution populations of the balancing and sequencing problems are sufficiently combined and evaluated in each iteration to improve efficiency. The proposed algorithm is illustrated using a small numerical example. Finally, the algorithm is experimentally investigated using a set of test cases and several comparison algorithms. The experimental results show that the combination and evaluation mechanism is suitable for these problems, especially for large-sized cases, and that the proposed algorithm is effective and has better performance than the comparison algorithms.

Load Balancing of Two-Sided Assembly Line Based on Deep Reinforcement Learning

In the complex and ever-changing manufacturing environment, maintaining the long-term steady and efficient work of the assembly line is the ultimate goal pursued by relevant enterprises, the foundation of which is a balanced load. Therefore, this paper carries out research on the two-sided assembly line balance problem (TALBP) for load balancing. At first, a mathematical programming model is established with the objectives of optimizing the line efficiency, smoothness index, and completion time smoothness index of the two-sided assembly line (TAL). Secondly, a deep reinforcement learning algorithm combining distributed proximal policy optimization (DPPO) and the convolutional neural network (CNN) is proposed. Based on the distributed reinforcement learning agent structure assisted by the marker layer, the task assignment states of the two-sided assembly and decisions of selecting tasks are defined. Task assignment logic and reward function are designed according to the optimization objectives to guide task selection and assignment. Finally, the performance of the proposed algorithm is verified on the benchmark problem.

New models for the two-sided assembly line balancing problem

New models for the two-sided assembly line balancing problem

New models for the two-sided assembly line balancing problem

New models for the two-sided assembly line balancing problem

An Integrated Method for Solving the Two-Sided Assembly Line Balancing Problems

The problems of parallel two-sided assembly lines balancing (TALB) are nonpolynomial (NP-hard) problems that belong to the field of combinatorial optimization. In most cases, these problems can be resolved with algorithms. To this end, this research adopts colored Petri nets (CPN) and a minimal spanning tree (MST) to address the type I problems for two-sided assembly line balancing problem (TALBP). This research proposes a three-step method. In the first step, a reachability tree chart is built to show potential tasks order assignment. In the second step, the color firing mechanism is employed to determine if the colored conditions are satisfied. The final step uses a MST and a least-time prioritized rule to select the proper tasks for assignment efficiently. These steps will be performed repetitively until all the tasks are properly assigned. Since the whole assignment process is based on graphical and paradigm methods, it can be adopted to lessen the burdens of complex calculations. The end of the research will present the comparison results between the examples from the literature and the method proposed in this research. As indicated by the research findings, employing CPN and an MST for TALBPs (type-I) can obtain even favorable and consistent results over legacy methods, and further optimize workstation design and enhance balance efficiency.

An evolutionary genetic algorithm for a multi-objective two-sided assembly line balancing problem: a case study of automotive manufacturing operations

ABSTRACT Assembly lines are often indispensable in factories, and a good assembly-line balancing model is very important for manufacturers to maximize their profit using limited resources in a competitive environment. To maintain a productive assembly line, multiple objectives with different importance must be considered at the same time. In this paper, a two-sided assembly-line balancing problem (TALBP) with multiple objectives is examined. A fuzzy multi-objective linear programming-weighted model (FMOLP-W) for solving the TALBP is constructed first with the consideration of the importance weights of the line balancing performance factors, including minimizing the number of workstations, minimizing cycle time, maximizing line efficiency, minimizing smoothness index and minimizing workstation idle time. An evolutionary genetic algorithm (GA) is proposed next to tackle large-scale problems when the problems are too complex to be solved by the FMOLP-W.

Multi-objective multi-verse optimiser for integrated two-sided assembly sequence planning and line balancing

Multi-objective multi-verse optimiser for integrated two-sided assembly sequence planning and line balancing

Mathematical model and a variable neighborhood search algorithm for mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times

Mathematical model and a variable neighborhood search algorithm for mixed-model robotic two-sided assembly line balancing problems with sequence-dependent setup times

A Branch-and-Price Algorithm for Balancing Two-Sided Assembly Lines with Zoning Constraints

Two-sided assembly lines are widely used in the large-size product manufacturing industry, especially for automotive assembly production. Balancing the assembly line is significant for assembly process planning and assembly production. In this study, we develop a novel and exact method to optimize the two-sided assembly line balancing problem with zoning constraints (TALBz), in which the aim is to minimize the number of mated-stations considering the task restrictions. A mixed-integer programming model is employed to exactly describe the TALBz problem. To strengthen the computational efficiency, we apply Dantzig–Wolfe decomposition to reformulate the TALBz problem. We further propose a branch-and-price (B&P) algorithm that integrates the column generation approach into a branch-and-bound frame. Both the benchmark datasets with zoning constraints and without zoning constraints are tested to evaluate the performance of the B&P algorithm. The numerical results show that our proposed approach can obtain optimal solutions efficiently on most cases. In addition, experiments on the real-world datasets originating from passenger vehicle assembly lines are conducted. The proposed B&P algorithm shows its advantage in tackling practical problems with the task restrictions. This developed methodology therefore provides insight for solving large-scale TALBz problems in practice.

A Mathematical Model for Tool Sharing between Workers by Exact Solution Approach

Multi-manned assembly lines are generally used to produce large-sized volume products such as cars and trucks. This article addresses the multi-manned two sided assembly line balancing problem with the objectives sharing tool between workstations. This paper presents a mathematical model and a Lingo -16 solvers based exact algorithm for multi-manned two-sided assembly line system configuration with tool sharing between adjacent workstations for companies that need intelligent solutions to satisfy customized demands on time with existing resources. The results obtained indicate that tool shared between parallel stations of two or more parallel lines beneficial for assembly line to minimize workstations, idle time and reduce tool cost.

Mathematical model for robotic two-sided assembly line balancing problem with zoning constraints

Mathematical model for robotic two-sided assembly line balancing problem with zoning constraints

Combinatorial Benders decomposition for mixed-model two-sided assembly line balancing problem

In this work, we consider a mixed-model two-sided assembly line balancing problem for a given cycle time (MTALBP-I), in which the primary objective is to minimise the number of mated-stations (i.e. the length of the two-sided assembly line), while the number of stations, which describes the total number of operators, is also concerned. To solve this problem, we propose a combinatorial Benders decomposition-based exact algorithm, and develop a sequence-based enumerative search method to calculate effective combinatorial Benders cuts. To evaluate the performance of our proposed solution approach, we conduct extensive computational experiments on a set of benchmark instances, and the results demonstrate its efficiency of finding exact solutions even for large-sized instances.

Balancing time and physical effort in two-sided assembly lines

Abstract In assembly line balancing problems, time is balanced among workers. This does not necessarily balance their physical effort. The effort exerted by workers varies depending on the type of the job, and the size and weight of the component. In the two-sided assembly lines, commonly used for heavy industries, the variation in physical effort among workers is more emphasized. The unbalanced effort leads to fatigue, and may lower productivity and quality. This work aims at balancing the physical effort among workers in the two-sided assembly lines, while considering other conventional objectives. A genetic algorithm and a new decoding scheme are proposed. An effort smoothness index (ESI) is introduced to measure the variation in effort among workers. Results prove that balancing the physical effort is influential and can be achieved at no extra cost for most cases. Effort balancing is harder to achieve in difficult problems; of high order of strength.

Local search methods for type I mixed-model two-sided assembly line balancing problems

Two-sided assembly lines are widely utilized to assemble large-sized products such as cars and trucks. Recently, these types of assembly lines have been applied to assemble different types of products due to a large variety of customer demands and strong market competition. This paper presents two simple local search methods, the iterated greedy algorithm and iterated local search algorithm, to deal with type I mixed-model two-sided assembly line balancing problems. These two algorithms utilize new precedence-based local search functions with referenced permutation and two neighborhood structures to emphasize intensification while preserving high search speed. Additionally, these local search methods are enhanced by utilizing the best decoding scheme amongst nine candidates and a new station-oriented evaluation to guide the search direction. New lower bound calculations are also presented to check the optimality of the achieved solutions. Eleven recent and high-performing metaheuristic algorithms are re-implemented to test the performance of the proposed algorithms. A comprehensive study on a set of benchmark problems demonstrates the advantages of the improvements and the superiority of the two proposed methods. Experimental results show that the proposed algorithms obtain 23 new upper bounds compared with two recently published algorithms, among which 19 cases are proven to be optimal for the first time.