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The time buffer approximated Buffer Allocation Problem: A row-column generation approach

机译:时间缓冲区近似缓冲区分配问题:行列生成方法

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One of the main problems in production systems is the buffer sizing. Choosing the right buffer size, at each production stage, that allows to achieve some performance measure (usually throughput or waiting time) is known as Buffer Allocation Problem (BAP), and it has been widely studied in the literature. Due to its complexity, BAP is usually approached using decomposition methods, under very strict system assumptions, or using simulation-optimization techniques. In this paper, the approximated mathematical programming formulation of the BAP simulation-optimization based on the time buffer concept is used. Using this approximation, buffers are modeled as temporal lags (time buffers) and this allows to use Linear Programming (LP) instead of Mixed Integer Linear Programming (MILP) models. Although LP models are easier to solve than MILPs, the huge dimension and the complex solution space topology of the time buffer approximated BAP call for ad hoc solution algorithms. To this purpose, a row-column generation algorithm is proposed, which exploits the theoretical properties of the time buffer approximation to reduce the solution time. The proposed algorithm has been compared with a standard LP solver (ILOG CPLEX) and with a state-of-the-art MILP solver and it proved to be better than the LP solver in most of the cases, and more robust than the MILP solver with respect to computation time. Moreover, the LP model (for flow lines) is able to solve the BAP also for assembly/disassembly lines. (C) 2019 Elsevier Ltd. All rights reserved.
机译:生产系统中的主要问题之一是缓冲区大小。在每个生产阶段选择合适的缓冲区大小以实现某种性能指标(通常是吞吐量或等待时间),这被称为缓冲区分配问题(BAP),并且在文献中对此进行了广泛的研究。由于其复杂性,通常在非常严格的系统假设下使用分解方法或使用仿真优化技术来实现BAP。在本文中,使用了基于时间缓冲概念的BAP仿真优化的近似数学编程公式。使用这种近似,将缓冲区建模为时间滞后(时间缓冲区),这允许使用线性规划(LP)而不是混合整数线性规划(MILP)模型。尽管LP模型比MILP更容易解决,但是时间缓冲区的巨大维度和复杂的解决方案空间拓扑近似于BAP,因此需要临时解决方案算法。为此,提出了一种行列生成算法,该算法利用时间缓冲区逼近的理论特性来减少求解时间。将该算法与标准LP求解器(ILOG CPLEX)和最新的MILP求解器进行了比较,在大多数情况下,它被证明比LP求解器更好,并且比MILP求解器更健壮关于计算时间。此外,LP模型(用于流水线)还能够解决组装/拆卸流水线的BAP。 (C)2019 Elsevier Ltd.保留所有权利。

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