Data-driven two-stage fuzzy random mixed integer optimization model for facility location problems under uncertain environment

AIMS Mathematics

article

Zhimin Liu¹  Ripeng Huang²  Songtao Shao³ 

[1] School of Mathematics Science, Liaocheng University;School of Mathematics and Finance, Chuzhou University;Shaanxi University of Science and Technology

关键词: two-stage fuzzy random mixed integer optimization;    facility location;    fuzzy random;    hybrid intelligent algorithm;   

DOI  :  10.3934/math.2022734

学科分类：地球科学(综合)

来源: AIMS Press

【 摘 要 】

This paper studies the problem of facility location in a hybrid uncertain environment with both randomness and fuzziness. We establish a data-driven two-stage fuzzy random mixed integer optimization model, by considering the uncertainty of transportation cost and customer demand. Given the complexity of the model, this paper based on particle swarm optimization (PSO), beetle antenna search algorithm (BAS) and interior point algorithm, a hybrid intelligent algorithm (HIA) is proposed to solve two-stage fuzzy random mixed integer optimization model, yielding the optimal facility location and maximal expected return of supply chain simultaneously. Finally, taking the supply chain of medical mask in Shanghai as an example, the influence of uncertainty on the location of processing factory was studied. We compare the HIA with hybrid PSO and hybrid genetic algorithm (GA), to validate the proposed algorithm based on the computational time and the convergence rate.

【 授权许可】

CC BY