Abstract Two of the most researched problems on transfer line, transfer line balancing problem (TLBP) and buffer allocation problem (BAP), are usually solved separately, although they are closely interrelated. When machine tools have different reliability, the traditional balancing approaches lead to a deviation of the production rate from the actual throughput, which is used as the objective of the following optimization on BAP. This may not only reduce the solution space of BAP, but also bring about a biased overall result. In this paper, the simultaneous solution of these two problems is presented, which includes transfer line balancing problem, BAP, and selection of line configuration, machine tools and fixtures. Production rate computed through simulation software and total cost considering machine tools and buffer capacities are used as two objective functions. The problem is solved applying a multi-objective optimization approach. Two well-known evolutionary algorithms are considered: Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). A real case study related to automotive sector is used to demonstrate the validity of the proposed approach.

中文翻译：

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用多目标方法同时解决传输线平衡和缓冲区分配问题

摘要 传输线研究最多的两个问题，传输线平衡问题（TLBP）和缓冲区分配问题（BAP），虽然它们密切相关，但通常是分开解决的。当机床具有不同的可靠性时，传统的平衡方法会导致生产率与实际产量的偏差，这作为下面对 BAP 优化的目标。这不仅可能会减少 BAP 的解空间，还会带来有偏差的整体结果。在本文中，提出了这两个问题的同时解决方案，包括传输线平衡问题、BAP 以及线路配置、机床和夹具的选择。通过仿真软件计算的生产率和考虑机床和缓冲能力的总成本被用作两个目标函数。应用多目标优化方法解决了该问题。考虑了两种著名的进化算法：非支配排序遗传算法-II (NSGA-II) 和多目标粒子群优化 (MOPSO)。一个与汽车行业相关的真实案例研究被用来证明所提出方法的有效性。