Matheuristics for mixed-model assembly line balancing problem with fuzzy stochastic processing time

被引：4

作者：

Anh, Truong Tran Mai ^{[1
]}

Hop, Nguyen Van ^{[2
]}

机构：

[1] Univ Florida, Dept Ind & Syst Engn, Gainesville, FL 32611 USA

[2] VNU HCM, Int Univ, Sch Ind Engn & Management, Quarter 6,Linh Trung Ward, Ho Chi Minh, Vietnam

来源：

APPLIED SOFT COMPUTING | 2024年 / 160卷

关键词：

Fuzzy random variables; Matheuristics; Genetic algorithm; Particle swarm optimization; Mixed-model assembly line balancing problem; VEHICLE-ROUTING PROBLEM; EVOLUTIONARY ALGORITHM; OPTIMIZATION MODELS; SCHEDULING PROBLEMS; HEURISTIC SOLUTION; GENETIC ALGORITHM; PROGRAMMING-MODEL; BOUND METHOD;

D O I：

10.1016/j.asoc.2024.111694

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Our work aims to investigate methods for solving the mixed-model assembly line balancing problem (MALBP) under uncertainty with the objective of minimizing the number of workstations. Specifically, we model task processing time as fuzzy stochastic variables (FRVs) due to the inherent uncertainties and variations in the manufacturing environment. Additionally, we introduce a ranking method for FRVs and propose a mathematical model to address MALBP. The recently developed Red Fox Optimization (RFO) algorithm is also discretized for the first time to support solving this problem. Finally, matheuristic algorithms combine a metaheuristic such as the popular Genetic Algorithm (GA), Particle Swarm Optimization (PSO), or the Discretized Red Fox Optimization (DRFO) algorithm with the Mixed-Integer Programming (MIP) model to generate the best solution in a reasonable time. Our comparative results demonstrate that the GA-MIP combination outperforms the others in both objective value and computational time.

引用

页数：23

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