TY - JOUR
T1 - A problem evolution algorithm with linear programming for the dynamic facility layout problem—A general layout formulation
AU - Xiao, Yiyong
AU - Xie, Yue
AU - Kulturel-Konak, Sadan
AU - Konak, Abdullah
N1 - Funding Information:
This work is partially supported by the National Natural Science Foundation of China (grant nos. 71271009 and 71501007) and the Aviation Science Foundation of China (grant no. 2014ZG51075).
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/12
Y1 - 2017/12
N2 - 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.
AB - 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.
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U2 - 10.1016/j.cor.2017.06.025
DO - 10.1016/j.cor.2017.06.025
M3 - Article
AN - SCOPUS:85023616498
SN - 0305-0548
VL - 88
SP - 187
EP - 207
JO - Computers and Operations Research
JF - Computers and Operations Research
ER -