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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 defined.

The assembly of large and complex products such as cars, trucks, and white goods typically involves a huge amount of production resources such as workers, pieces of equipment, and layout areas. In this context, multi-manned workstations commonly characterize these assembly lines. The simultaneous operators’ activity in the same assembly station suggests considering compatibility/incompatibility between the different mounting positions, equipment sharing, and worker cooperation. The management of all these aspects significantly increases the balancing problem complexity due to the determination of the start/end times of each task. This paper proposes a new mixed-integer programming model to simultaneously optimize the line efficiency, the line length, and the workload smoothness. A customized procedure based on a simulated annealing algorithm is developed to effectively solve this problem. The aforementioned procedure is applied to the balancing of the real assembly line of European sports car manufacturers distinguished by 665 tasks and numerous synchronization constraints. The experimental results present remarkable performances obtained by the proposed procedure both in terms of solution quality and computation time. The proposed approach is the practical reference for efficient multi-manned assembly line design, task assignment, equipment allocation, and mounting position management in the considered industrial fields.

This article considers multi-manned assembly line balancing problems with walking workers. The objective of the problem is the minimization of number of workers and workstations simultaneously. Several exact-solution algorithms based on Benders’ decomposition are proposed to solve the problem optimally. In one of the algorithms a constructive heuristic that generates effective task-worker assignments and some problem-specific symmetry breaking constraints are used. Moreover, the solutions obtained by meta-heuristic in the literature are used as starting points to increase the performance of proposed decomposition methods. A benchmark set of 99 instances are used to analyze the performance of the proposed exact methods, contribution of the developed heuristic and the ability of Benders’ decomposition on improving the starting solutions. Our results indicate a significiant improvement in the optimal solvability of the problem for larger-sized instances. Suggested methods also improve the results of the meta-heuristic method for significant number of instances. Consequntly, proposed methods solved most of instances optimally and they are able to find the optimal solutions of 17 instances that cannot be solved optimally with previous methods.

With the rapid transformation of the automotive industry towards electric vehicles, how to achieve efficient mixed-line production of electric vehicles and fuel vehicles has become a key challenge for modern assembly systems. This study investigated the balancing problem of a mixed-model multi-manned assembly line, considering workstation sharing (MMuALBP-WS), and developed a deterministic multi-objective model that integrates the heterogeneity of tasks and the coordination of shared workstations. An improved genetic algorithm was proposed, whose decoding mechanism enables different types of electric vehicle and fuel vehicle tasks to achieve dynamic collaboration within the shared workstations. A real case study from the chassis assembly line of Company W demonstrated the effectiveness of the proposed method, achieving a 25% reduction in the number of workstations, a 27% decrease in the total number of workers, and a 23.56% increase in average workstation utilization. The results confirmed that the workstation sharing mechanism significantly improved production balance, labor utilization, and flexibility, providing a practical and scalable optimization framework for the mixed-model assembly system in the era of the transition from electric vehicles to fuel vehicles. In addition to its practical significance, this study enhances the understanding of mixed-model multi-manned line balancing by incorporating workstation-sharing logic into both the mathematical modeling and optimization process, offering a theoretical basis for future extensions to more complex production environments.

PurposeMulti-manned assembly lines are designed to produce large-sized products, such as automobiles. In this paper, a multi-manned assembly line balancing problem (MALBP) is addressed in which a group of workers simultaneously performs different tasks on a workstation. The key idea in this work is to improve the workstation efficiency and worker efficiency of an automobile plant by minimizing the number of workstations, the number of workers, and the cycle time of the MALBP.Design/methodology/approachA mixed-integer programming formulation for the problem is proposed. The proposed model is solved with benchmark test problems mentioned in research papers. The automobile case study problem is solved in three steps. In the first step, the authors find the task time of all major tasks. The problem is solved in the second step with the objective of minimizing the cycle time for the sub-tasks and major tasks, respectively. In the third step, the output results obtained from the second step are used to minimize the number of workstations using Lingo 16 solver.FindingsThe experimental results of the automobile case study show that there is a large improvement in workstation efficiency and worker efficiency of the plant in terms of reduction in the number of workstations and workers; the number of workstations reduced by 24% with a cycle time of 240 s. The reduced number of workstations led to a reduction in the number of workers (32% reduction) working on that assembly line.Practical implicationsFor assembly line practitioners, the results of the study can be beneficial where the manufacturer is required to increased workstation efficiency and worker efficiency and reduce resource requirement and save space for assembling the products.Originality/valueThis paper is the first to apply a multi-manned assembly line balancing approach in real life problem by considering the case study of an automobile plant.

The disassembly line plays a vital role to recover the products for remanufacturing enterprises. For this reason, designing and balancing of the disassembly line are important to utilize the economic and tactical benefits. This study explores a multi-objective disassembly line balancing problem (MODLBP) from a different point of view by considering the workers’ heterogeneity and the multi-manned stations where the group-based worker assignment strategy is implemented. Although the MODLBP has been attracting attention in the last decade, to the best of our knowledge, this is the first study investigating the addressed problem in the current form. To further analyze the problem, first, it is described by focusing on the tactical level strategies and operational level scenarios. Subsequently, a novel multi-objective optimization model is formulated with three objectives, that of minimizing overall cost, cycle time, and workload imbalance. On one hand, the improved augmented ϵ-constrained (AUGMECON2) method is used to obtain the Pareto-optimal solutions for small-sized problems. On the other hand, a set of algorithms based on the non-dominated sorting genetic algorithm-II is implemented to gain managerial insights regarding the strategies and scenarios for large-sized problems. A computational study is conducted based on the generated problems to reveal the prominent differences between strategies in terms of performance metrics. According to the computational results, high-quality solutions are achieved when the group-based assignment strategy is realized. Besides, it is revealed from scenario analysis that the training of workers leads to considerable improvements in the system performance.

This paper addresses an assembly line balancing problem in which each station is allowed to have more than one worker, with minimization of two objectives: (i) the number of workers used in the line and (ii) the number of stations opened in the line. Each station has an own workpiece, and workers at the same station simultaneously perform different assembly tasks on its workpiece. For solving the balancing problem of these lines, a new mixed integer programming formulation is presented, and then a simulated annealing based heuristic method is proposed. The new formulation requires less number of variables and constraints. The basic property of the proposed heuristic is that it is directly applied to Gantt representations of problem’s solutions. Moreover, the paper includes some important neighborhood generation properties for Gantt representations of solutions. Both methods are compared with existing methods available in the literature. Experimental study indicates that the proposed methods can yield promising results.

In real-world assembly lines, that the size of the product is large (e.g., automotive industry), usually there are multi-manned workstations where a group of workers simultaneously perform different operations on the same individual product. This paper presents a mixed integer programming model to solve the balancing problem of the multi-manned assembly lines optimally. This model minimizes the total number of workers on the line as the first objective and the number of opened multi-manned workstations as the second one. Since this problem is well known as NP (nondeterministic polynomial-time)-hard, a heuristic approach based on the ant colony optimization approach is developed to solve the medium- and large-size scales of this problem. In the proposed algorithm, each ant tries to allocate given tasks to multi-manned workstations in order to build a balancing solution for the assembly line balancing problems by considering the precedence relations, multi-manned assembly line configuration, task times, and cycle time constraints. Through computational experiments, the performance of the proposed ACO is compared with some existing heuristic on various problem instances. The experimental results validate the effectiveness and efficiency of the proposed algorithm.

Some feasible mathematical model solution results in a recent paper (Fattahi et al., Int J Adv Manuf Technol , 53:363–378, 2011) are incorrect. In this note, we show by counterexamples that the results are incorrect and mathematical formulation is corrected by additional constraints.

Purpose – Within team-oriented approaches, tasks are assigned to teams before being assigned to workstations as a reality of industry. So it becomes clear, which workers assemble which tasks. Design/methodology/approach – Team numbers of the assembly line can increase with the number of tasks, but at the same time, due to physical situations of the stations, there will be limitations of maximum working team numbers in a station. For this purpose, heuristic assembly line balancing (ALB) procedure is used and mathematical model is developed for the problem. Findings – Well-known assembly line test problems widely used in the literature are solved to indicate the effectiveness and applicability of the proposed approach in practice. Originality/value – This paper draws attention to ALB problem in which workers have been assigned to teams in advance due to the need for specialized skills or equipment on the line for the first time.