A modified colonial competitive algorithm for the mixed-model U-line balancing and sequencing problem

Different from a large number of existing studies in the literature, this paper addresses two important issues in managing production lines, the problems of line balancing and model sequencing, concurrently. A novel hybrid agent-based ant colony optimization–genetic algorithm approach is developed for the solution of mixed model parallel two-sided assembly line balancing and sequencing problem. The existing agent-based ant colony optimization algorithm is enhanced with the integration of a new genetic algorithm-based model sequencing mechanism. The algorithm provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain. A numerical example is given to illustrate the solution building procedure of the algorithm and the evolution of the chromosomes. The performance of the developed algorithm is also assessed through test problems and analysis of their solutions through a statistical test, namely paired sample t test. In accordance with the test results, it is statistically proven that the integrated genetic algorithm-based model sequencing engine helps agent-based ant colony optimization algorithm robustly find significantly better quality solutions.

A mixed model assembly line is a production line where a variety of product models are produced. Line balancing and model sequencing problems are important for an efficient use of such lines. Although the two problems are tightly interrelated with each other, prior researches have considered them separately or sequentially. This paper presents a new method using a coevolutionary algorithm that can solve the two problems at the same time. In the algorithm, it is important to promote population diversity and search efficiency. We adopt a localized interaction within and between populations, and develop methods of selecting symbiotic partners and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. Also presented are the experimental results that demonstrate the proposed algorithm is superior to existing approaches.

SUMMARYIn this paper, an integrated mathematical model for the balancing and sequencing problems of a mixed-model assembly line (MMAL) is developed. The proposed model minimizes the total overload and idleness times. For the sake of reality, the impact of operator’s learning and fatigue issues on the optimization of the assembly line balancing and sequencing problems is considered. Furthermore, it is assumed that the Japanese mechanism is used in this assembly line to deal with the overload issue. With respect to the complexity level of the proposed model, a genetic algorithm is developed to solve the model. In order to set the parameters of the developed genetic algorithm, the well-known Taguchi method is used and the efficiency of this solution method is compared with the GAMS software using several test problems with different sizes. Finally, the sensitivity of the balancing and sequencing problems to the parameters such as station length, learning rate, and fatigue rate are analyzed and the impact of changing these parameters on the model is studied.

Disassembly operations are inevitable elements of product recovery with the disassembly line as the best choice to carry out the same. In the light of different structures of returned products (models) and variations in task completion times, the process of disassembly could not be efficiently mapped on a simple straight line. Another important issue that needs consideration is the task-time variability pertaining to human factor. In order to resolve these complexities a Mixed-Model U-shaped Disassembly Line with Stochastic Task Times has been proposed in this article. A novel approach, Collaborative Ant Colony Optimization (CACO), has been utilized that simultaneously tackles the interrelated problem of line balancing and model sequencing. The distinguishing feature of the proposed approach is that it maintains bilateral colonies of ants which independently identifies the two sequences, but utilizes the information obtained by their collaboration to guide the future path. The approach is tested on benchmark instances that were generated using Design of Experiment techniques and Analysis of Variance is performed to determine the impact of various factors on the objective. The robustness of proposed algorithm is authenticated against Ant Colony Optimization over which it always demonstrated better results thereby proving its superiority on the concerned problem.

ABSTRACTIn the context of Industry 4.0, a production line must be flexible and adaptable to stochastic or real‐world environments. As a result, the assembly line balancing (ALB) problem involves managing uncertainty or stochasticity. Several methods have been proposed, including stochastic mathematical programming models and simulations. However, programming models can only incorporate a few sources of uncertainty that result in impractical or unfeasible solutions to implement due to overlooked complexities, while simulation is only used to test solutions from deterministic approaches or adjust parameters without maintaining their optimum value. The proposed methodology uses a deterministic mathematical model to minimize the cycle time, followed by the simulation to measure the impact of selected sources of uncertainty on the cycle time. Finally, the optimum value of the stochastic parameters is computed using simulation‐based optimization to maintain the average cycle time close to the deterministic one. A real‐life assembly line balancing problem for a motorcycle manufacturing company is solved to test the proposed methodology. The sources of uncertainty are the tasks' stochastic processing times, inter‐arrival time, the number of workers in each station, and the speed of the material handling system. Results show that the average cycle time is above 2.7% from the deterministic value computed by the programming model when the inter‐arrival time is set to 270 60 s; the processing times are allowed to increase or decrease by 3 s; the material handling system's speed is 1.5 m/s; and the number of workers in cells is between 4 and 6, with a speed of 2 m/s. The reader can download the source code and the simulation model to apply the methodology to other instances.

In this paper, type II robotic mixed-model two-sided assembly line balancing (RMTALB-II) problem is considered. In recent years, robots have been widely used in assembly systems as called robotic assembly lines where a set of tasks have to be assigned to stations and each station needs to select one of the different robots to process the assigned tasks. Two-sided assembly lines are especially used to produce large-sized high-volume products, such as automobiles, trucks, and buses. In this type of production line, both left side and right side of the line are used in parallel. However, little attention has been paid to solve RMTALB problems. Moreover, according to our best knowledge, there is no published work in the literature on RMTALB. This paper presents a new mixed-integer programming model for RMTALB-II to minimize the cycle time for a given number of mated stations. Since RMTALB problems are in NP-hard class of combinatorial optimization problems, a simulated annealing (SA) algorithm as metaheuristic method is proposed to solve the problem. Some problems are solved by SA and performance of the SA is evaluated by comparing their results with the optimal solution. The computational results show that SA is more efficient.

In real-life mixed-model assembly lines, multiple problems collectively affect the final production’s performance. In this study, mixed-model assembly lines integrated with balancing and sequencing problems are considered simultaneously solved. A comprehensive mathematical model is formulated to evaluate the current multi-objective problem. An intelligent hybrid genetic algorithm (IHGA) is proposed to solve the integrated mixed-model assembly line balancing and sequencing problem. The performance of the proposed algorithm is triggered by integrating heuristic rules through a generation gap mechanism which helps in reducing search space without succumbing to local optima. Additionally, parametric tuning of the algorithm is performed using Q-learning, enabling adaptive optimization through reinforcement learning. This helps to enhance computational efficiency and achieve robust performance of the proposed algorithm. The performance of the IHGA algorithm is rigorously compared with existing approaches, including a non-dominated sorting genetic algorithm, multi-objective artificial bee colony, multi-objective particle swarm optimization, multi-objective evolutionary algorithm based on Decomposition, and multi-objective grey wolf optimizer. Results demonstrate the superior performance of the proposed algorithm across various metrics, showcasing its efficacy in optimizing mixed-model assembly lines, where symmetry in task allocation and sequencing can significantly enhance operational efficiency in contemporary industrial settings. Additionally, a real-life case study is solved to validate the empirical applicability of the proposed IHGA. The extensive experimental analysis notably shows that the proposed IHGA outperforms the existing methods.

The manufacturing industry has evolved in the past decades, due to the competition of the global economy, where the market demands high quality and customized products, and meeting lowest possible costs are paramount. One of the many processes in manufacturing is the assembly line operation, namely the assembly line balancing (ALB) problem. ALB problem is dependent on optimum resource utilization in order to improve production output, reduces costs, and shortens production lead times. In these recent years, various approaches have been proposed to solve the complexity of assembly line balancing operations, which composed of exact, heuristic, and meta-heuristic approaches. However, little work had been done to solve the type E assembly line balancing problem. This paper proposed an approach using artificial immune system (AIS) algorithm, namely as the artificial immune cell (AIC) approach, for solving type E assembly line balancing problem. An initialization mechanism through the bone marrow model and probabilistic clonal selection mechanism had encouraged efficient exploration and exploitation of the solution space. The computational results over 242 instances of 24 datasets had demonstrated the efficiency of the proposed AIC approach by achieving high-quality solutions (up to 85.94% optimum solutions was obtained). Also, the results were statistically justified by comparing with multi-rule multi-objective simulated annealing (MRMOSA), priority-based genetic approach (PriGA), two-phased genetic approach (2P-GA), and assignment genetic approach (MA-GA).  Â

A risk-averse two-stage stochastic programming model for a joint multi-item capacitated line balancing and lot-sizing problem

Gökçen et al. (2006) have proposed several procedures and a mathematical model on single-model (product) assembly line balancing (ALB) problem with parallel lines. In parallel ALB problem, the goal is to balance more than one assembly line together. In this paper, a network model for parallel ALB problem has been proposed and illustrated on a numerical example. This model is a new approach for parallel ALB and it provides a different point of view for interested researchers.

Worker assignment is a new type of problem in assembly line balancing problems, which typically occurs in sheltered work centers for the disabled. However, only a few contributions consider worker assignment in a two-sided assembly line. This research presents three variants of artificial bee colony algorithm to solve worker assignment and line balancing in two-sided assembly lines. The utilization of meta-heuristics is motivated by the NP-hard nature of the problem and the chosen methods utilize different operators for onlooker phase and scout phase. The proposed algorithms are tested on 156 cases generated from benchmark problems. A comparative study is conducted on the results obtained from the three proposed variants and other well-known metaheuristic algorithms, such as simulated annealing, particle swarm optimization and genetic algorithm. The computational study demonstrates that the proposed variants produce more promising results and are able to solve this new problem effectively in an acceptable computational time.

Purpose The purpose of this paper is to find the most efficient assembly line balancing solution across many heuristic line balancing methods, in assistance with a developed computer program. Design/methodology/approach In this paper, assembly line balancing problem was analyzed using t-shirt and knitted pants data. A computer program using MATLAB software for the solution of assembly line balancing problems has been developed. In this study, following heuristic assembly line balancing methods were applied: Hoffman method; position weight method; COMSOAL method; and Kilbridge and Wester method. A MATLAB program has been developed by taking into account of theoretical solution of all these methods. Later the program is developed further by analyzing solutions made manually and is made to verify the developed program. Findings Pre-studies which were conducted in order to decide which programming language would be the best choice for line balancing methods’ application came out with the result that MATLAB, from between C, C++, C# and Java, would be the best software choice. The main reason for this choice is that MATLAB is a powerful matrix operation software with a powerful user interface designing tool and has the tools to make development program to be used universally in every computer. Originality/value When the researches were investigated, it is clearly seen that, this study is the first research on using computer program for solving assembly line balancing problem.

Balancing U-shaped assembly lines with resource dependent task times: A Simulated Annealing approach

A goal programming mixed-model line balancing for processing time and physical workload

When defining the layout for a production line, it is necessary to assign tasks to workstations, so that the work is performed in a feasible sequence and approximately equal amounts of time are needed at each workstation, a process called line balancing. Therefore, the need for balancing production lines involves the distribution of sequential activities for jobs in order to allow high labour and equipment utilisation and minimise the idle time. Line balancing problems are complex to treat, being used distinct methodologies to perform it. This paper describes an application for line balancing using two genetic algorithms (the first obtains solutions to the problem and the second optimises those solutions), associated with a graphical interface for the problem data input and visualisation of results. Results demonstrate advantages over heuristic methods as it is possible to obtain more than one solution and it is more practical to use the developed application.