scholarly journals Development of cost model for the single-model stochastic assembly line balancing problem

1990 ◽  
Vol 28 (7) ◽  
pp. 1305-1316 ◽  
Author(s):  
SUBHASH C. SARIN ◽  
ERDAL EREL
2006 ◽  
Vol 39 (3) ◽  
pp. 419-424
Author(s):  
Liya Gu ◽  
Sophie Hennequin ◽  
Alexandru Sava ◽  
Xiaolan Xie

2019 ◽  
Vol 39 (1) ◽  
pp. 124-139 ◽  
Author(s):  
Ahad Foroughi ◽  
Hadi Gökçen

Purpose This research aims to address the cost-oriented stochastic assembly line balancing problem (ALBP) and propose a chance-constrained programming model. Design/methodology/approach The cost-oriented stochastic ALBP is solved for small- to medium-sized problems. Owing to the non-deterministic polynomial-time (NP)-hardness problem, a multiple rule-based genetic algorithm (GA) is proposed for large-scale problems. Findings The experimental results show that the proposed GA has superior performance and efficiency compared to the global optimum solutions obtained by the IBM ILOG CPLEX optimization software. Originality/value To the best of the authors’ knowledge, only one study has discussed the cost-oriented stochastic ALBP using the new concept of cost. Owing to the NP-hard nature of the problem, it was necessary to develop a heuristic or meta-heuristic algorithm for large data sets; this research paper contributes to filling this gap.


2014 ◽  
Vol 607 ◽  
pp. 99-102 ◽  
Author(s):  
Han Ye Zhang ◽  
Hai Jiang Liu ◽  
Li Yun Chen ◽  
Ling Yu Li

In this paper, the optimal assembly sequence is considered as precedence graph which reduces the complexity of the problem, and an exact algorithm named task-oriented enumeration is proposed to solve the single-model stochastic assembly line balancing problems of type-1. The results show the proposed algorithm can solve the single-model stochastic assembly line balancing problems of type-1.


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