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A problem evolution algorithm with linear programming for the dynamic facility layout problem-A general layout formulation

机译：动态设施布局问题的线性规划问题演化算法-一般布局公式

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摘要

Facility layout problems (FLPs) are quite common and important in many industries. This paper presents a mixed integer linear programming (MILP) model for the dynamic facility layout problem, which is a generalization of several special cases of FLPs studied in recent years. A new evolutionary meta-heuristic framework, named as the problem evolution algorithm (PEA), is developed as a general solution approach for FLPs. Computational experiments show that the PEA combined with the linear programming (LP), called PEA-LP in short, performs well in various types of FLPs. In addition, a new polyhedral inner approximation method is proposed based on secant lines for the linearization of the non-linear constraint for department area requirements. This new method guarantees that the actual department area is always greater than or equal to the required area within a given maximum deviation error. Furthermore, two new symmetry-breaking constraints which help to improve the computational efficiency of the MILP model are also introduced. Computational experiments on several well-known problem instances from the literature are carried out to test the DFLP-FZ and the PEA-LP with promising results. (c) 2017 Elsevier Ltd. All rights reserved.
机译：设施布局问题（FLP）在许多行业中非常普遍且重要。本文提出了一种用于动态设施布局问题的混合整数线性规划（MILP）模型，该模型是对近年来研究的FLP的几种特殊情况的概括。开发了一种新的进化元启发式框架，称为问题进化算法（PEA），作为FLP的通用解决方案。计算实验表明，与线性编程（LP）结合使用的PEA（简称为PEA-LP）在各种类型的FLP中表现良好。此外，提出了一种新的基于割线的多面体内部逼近方法，以线性化部门区域需求的非线性约束。这种新方法可确保实际部门面积始终在给定的最大偏差误差范围内大于或等于所需区域。此外，还介绍了两个新的打破对称性的约束条件，它们有助于提高MILP模型的计算效率。对文献中几个著名的问题实例进行了计算实验，以测试DFLP-FZ和PEA-LP，并取得了可喜的结果。 （c）2017 Elsevier Ltd.保留所有权利。

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