A Surrogate Model-Based Hybrid Approach for Stochastic Robust Double Row Layout Problem

Xing Wan, Xing-Quan Zuo and Xin-Chao Zhao
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Xing Wan: School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Xing-Quan Zuo: School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Xin-Chao Zhao: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Mathematics, 2021, vol. 9, issue 15, 1-18

Abstract: The double row layout problem is to arrange a number of machines on both sides of a straight aisle so as to minimize the total material handling cost. Aiming at the random distribution of product demands, we study a stochastic robust double row layout problem (SR-DRLP). A mixed integer programming (MIP) model is established for SR-DRLP. A surrogate model is used to linearize the nonlinear term in the MIP to achieve a mixed integer linear programming model, which can be readily solved by an exact method to yield high-quality solutions (layouts) for small-scale SR-DRLPs. Furthermore, we propose a hybrid approach combining a local search and an exact approach (LS-EA) to solve large-scale SR-DRLPs. Firstly, a local search is designed to optimize the machine sequences on two rows and the clearance from the most left machine on row 1 to the left boundary. Then, the exact location of each machine is further optimized by an exact approach. The LS-EA is applied to six problem instances ranging from 8 to 50 machines. Experimental results show that the surrogate model is effective and LS-EA outperforms the comparison approaches.

Keywords: double row layout problem; stochastic layout; robust layout; surrogate model; local search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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