Estimation of distribution algorithm for solving the slab stack shuffling and relocation problem

LI Tieke, LUAN Zhiwei, WANG Bailin, DONG Guangjing

Systems Engineering - Theory & Practice ›› 2017, Vol. 37 ›› Issue (11) : 2955-2964.

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PDF(894 KB)
Systems Engineering - Theory & Practice ›› 2017, Vol. 37 ›› Issue (11) : 2955-2964. DOI: 10.12011/1000-6788(2017)11-2955-10

Estimation of distribution algorithm for solving the slab stack shuffling and relocation problem

  • LI Tieke1,2, LUAN Zhiwei1,2, WANG Bailin1,2, DONG Guangjing3
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Abstract

The slab stack shuffling (SSS) problem is studied in the slab yard of iron and steel industry. Different from the previous studies, in the process of slab stack shuffling, the obstacles slabs may not be moved back to the original stack. Thus, the movement times are put forward to measure the working load of slab warehouse and the integer programming model is established to minimize the number of the slab movement. An estimation of distribution algorithm (EDA) based on the probability model is proposed to determine the corresponding slabs for rolling units and a heuristic algorithm based on radiation neighborhood to find the best position for the obstacles slabs relocation. The influence of parameters on the performance of the algorithm is discussed through the experiments of different scales. Simulative experiments illustrate the effectiveness of the proposed method compared with modified genetic algorithm and partheno-genetic algorithm.

Key words

slab stack shuffling / movement times / relocation / radiation neighborhood / estimation of distribution algorithm

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LI Tieke , LUAN Zhiwei , WANG Bailin , DONG Guangjing. Estimation of distribution algorithm for solving the slab stack shuffling and relocation problem. Systems Engineering - Theory & Practice, 2017, 37(11): 2955-2964 https://doi.org/10.12011/1000-6788(2017)11-2955-10

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Funding

Fundamental Research Funds for the Central Universities, China (FRF-BD-16-006A); National Natural Science Foundation of China (71231001, 71701016); Beijing Municipal Natural Science Foundation (9174038)
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