Papers by Keyword: Mixed-Model Assembly Line

Abstract: For a large number of uncertainties such as suppliers, customer demand and different external uncertainties, as well as workers, machines, environment and other internal uncertainties, they affects the mixed-model assembly line balancing. And these uncertainties can be turned into the changes of demand, operation time and workstation availability, which are relevant to mixed-model assembly line balancing. By solving the three factors influence on the balancing, these uncertainties influence is processed. This paper provides a way to process uncertainties influence on the mixed-model assembly line balancing.

Abstract: For timely diagnosing the faults of mixed-model assembly line, and highly eliminating abnormal the production line, the time causality relation in the faults of mixed-model assembly line was Fuzzy discretized by the introduction of fuzzy set theory, and the fault diagnosis method of mixed-model assembly line based on dynamic causality bayesian network was established in this paper. Finally, a fault diagnosis simulation system of mixed-model assembly line based on dynamic causality bayesian network was demonstrated and validated by QUEST software. It has shown the proposed method can improve the benefit of production scheduling, and provide a support for adapting to complex and dynamic production scheduling in mixed-model flow production.

Abstract: This article focuses on the study of optimization of mixed-model assembly line modeling. Based on colored timed Petri net, it forms knowledge colored timed Petri net by introducing Agent technology and knowledge place. Through taking advantage of Agent technology, it modularizes each technique process of mixed-model assembly line and then makes the bottom-up hierarchical modeling, avoiding the complexity problem of traditional Petri net modeling. Such a model always possesses Agent's autonomy and intelligence while the knowledge place could resolve conflicts and deadlock problems in models.

Abstract: This thesis puts forward a reconfiguration approach of mixed-model assembly line on the basis of adding equipments against short-term and violent change of demand. This approach makes uses of equivalent transformation of task volume to judge whether the capacity of current assembly line can satisfy the demand according to delivery time, and gives equipment configuration of completing the order by adjusting assembly line structure. Research results show that the shortage and excess of assembly line capacity as well as equipment configuration is different when order changes. The approach proposed in this thesis can be directly adopted as the scheme of mixed-model assembly line and referred to other capacity adjustment schemes, such as balancing the mixed-model assembly lines and cooperative manufacture.

Abstract: A mathematic model is established for a car mixed-model assembly line. To solve the line balancing problem of car mixed-model assembly, an improved differential evolution algorithm with adaptive operator is proposed. To verify the proposed algorithm, an application example is offered. The solution result shows that the proposed improved differential evolution algorithm can offer better fitness than the standard one. It also shows that this algorithm is feasible and effective.

Abstract: Mixed-model assembly lines are become more and more important by producing different models of the same product on an assembly line. Aiming at the existing mixed-model assembly line balancing problem, first, two important objective functions for minimizing cycle time and workload variance were provided, and mathematical models were established. Furthermore, in order to obtain the optimal or near optimal solutions, an improved genetic algorithm was proposed with combined precedence graph. Finally, the experiment results illustrate the feasibility and validity of the proposed improved genetic algorithm.

Abstract: Mixed-model assembly line is known to be a special case of production lines where various and different models of the same products are inter-mixed to be assembled on the same line. Using mixed model assembly line involves not only the traditional problems of assembly line design (i.e., determining the line cycle time, the number and sequence of stations of the line, and balancing the line) but also determining the sequence in which products will be scheduled for assembly. In each station, some components are assembled in the final products. Under just in time (JIT) approach; an objective is to have a fairly constant usage rate for each component going into final assembly to facilitate the use of Kanban or other JIT shop-floor systems. In this paper, a mixed approach, the sequencing mixed-model assembly line problem with a JIT-supply of required materials, ‌is studied and a mathematical model is proposed for solving it. The results are compared with a traditional approach. Some experiments are used to evaluate the performance of the proposed model.

Abstract: Mixed model production is the practice of assembling different and distinct models in a line without changeovers with responding to sudden demand changes for a variety of models. In this paper, we specify sequence of models to minimize the conveyer stoppages. We assume our lines are fixed and we can not change the balance of the lines. When the condition of lines like setup cost, demand of each models change, it is important to specify the sequence for minimizing the conveyer stoppages without balancing the line again, because the main lines are fixed. We consider three objective functions, simultaneously: minimizing the variation in actual and required production capacity of the line, minimizing the objectives which increase the chance of conveyer stoppage including: a) minimizing the total setup time, b) minimizing the total production variation cost, and minimizing the total utility work cost. We use a new fuzzy programming for this problem; it can present us an estimator for nearness of conveyer stoppages. We study about sub lines and conveyer velocity and its affect on stoppages.

Abstract: This paper focus on single vehicle scheduling problem minimizing the line-side inventory of mixed-model assembly lines, considering the assembly sequence as an input.An exact algorithm is proposed based on three newly introduced concepts: conflicting, tight subset and complete conflicting subset, which divide the transportation operations into three classes.The solution generated by the algorithm is proved to be optimal. The algorithm is compared with the most popular FCFS (first-come, first-served) rule, and results shows the advantages of the algorithm, especially for large instances of the problem.

Abstract: Simple genetic algorithm has shortcomings of poor local search ability and premature convergence. To overcome these disadvantages, simulated annealing algorithm which has good local search ability was combined with genetic algorithm to form simulated annealing genetic algorithm. The tests by two commonly used test functions of Shaffer’s F6 and Rosenbrock show that simulated annealing genetic algorithm outperforms the simple genetic algorithm both in convergence rate and convergence quality. Finally, the simulated annealing genetic algorithm was firstly applied in a practical problem of balancing and sequencing design of mixed-model assembly line, once again, the solution results show that simulated annealing genetic algorithm outperforms the simple genetic algorithm. Meanwhile, it provides a new algorithm for solving the design problem of mixed-model assembly line.