scholarly journals Cycle time enhancement by simulated annealing for a practical assembly line balancing problem

Informatica ◽  
2020 ◽  
Vol 44 (2) ◽  
Author(s):  
Huong Mai Dinh ◽  
Dung Viet Nguyen ◽  
Long Van Truong ◽  
Thuan Phan Do ◽  
Thao Thanh Phan ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Cycle Time ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Assembly Line Balancing Problem

Study and Analysis of GA-Based Heuristic Applied to Assembly Line Balancing Problem

2014 ◽  
Vol 13 (02) ◽  
pp. 113-131 ◽  
Author(s):  
P. Sivasankaran ◽  
P. Shahabudeen
Keyword(s):  
Cycle Time ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Optimal Solution ◽  
Vital Role ◽  
Line Balancing ◽  
Problem Size ◽  
Minimum Number ◽  
Assembly Line Balancing Problem ◽  
Balancing Efficiency

Balancing assembly line in a mass production system plays a vital role to improve the productivity of a manufacturing system. In this paper, a single model assembly line balancing problem (SMALBP) is considered. The objective of this problem is to group the tasks in the assembly network into a minimum number of workstations for a given cycle time such that the balancing efficiency is maximized. This problem comes under combinatorial category. So, it is essential to develop efficient heuristic to find the near optimal solution of the problem in less time. In this paper, an attempt has been made to design four different genetic algorithm (GA)-based heuristics, and analyze them to select the best amongst them. The analysis has been carried out using a complete factorial experiment with three factors, viz. problem size, cycle time, and algorithm, and the results are reported.

Assembly Line Balancing Type-2 Using Unequal Multiple Operators

2013 ◽  
Vol 816-817 ◽  
pp. 1169-1173
Author(s):  
Usman Attique ◽  
Abdul Ghafoor ◽  
Riaz Ahmed ◽  
Shahid Ikramullah
Keyword(s):  
Cycle Time ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Computational Effort ◽  
Line Balancing ◽  
Heuristic Methods ◽  
Novel Approach ◽  
Assembly Line Balancing Problem

Various exact and heuristic methods have been proposed for assembly line balancing problem (ALBP) but unequal multiple operators have not been considered much. In this paper we present a novel approach of assembly line balancing Type-2 with unequal multiple operators by using an already developed code in Matlab (Tomlab modeling platform). The adopted approach can be applied at line balancing problems ranging from few to hundreds of work elements to achieve minimum cycle time with very less computational effort.

Mixed-model multi-manned assembly line balancing problem: a mathematical model and a simulated annealing approach

2017 ◽  
Vol 37 (1) ◽  
pp. 34-50 ◽  
Author(s):  
Abdolreza Roshani ◽  
Farnaz Ghazi Nezami
Keyword(s):  
Simulated Annealing ◽  
Assembly Line ◽  
Mixed Model ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Mixed Integer ◽  
Computational Time ◽  
Content Type ◽  
Mip Model ◽  
Assembly Line Balancing Problem

Purpose This paper aims to study a generalized type of mixed-model assembly line with multi-manned workstations where multiple workers simultaneously perform different tasks on the same product. This special kind of assembly line is usually utilized to assemble different models of large products, such as buses and trucks, on the same production line. Design/methodology/approach To solve the mixed-model multi-manned assembly line balancing problem optimally, a new mixed-integer-programming (MIP) model is presented. The proposed MIP model is nondeterministic polynomial-time (NP)-hard, and as a result, a simulated annealing (SA) algorithm is developed to find the optimal or near-optimal solution in a small amount of computation time. Findings The performance of the proposed algorithm is examined for several test problems in terms of solution quality and running time. The experimental results show that the proposed algorithm has a satisfactory performance from computational time efficiency and solution accuracy. Originality/value This research is the very first study that minimizes the number of workers and workstations simultaneously, with a higher priority set for the number of workers, in a mixed-model multi-manned assembly line setting using a novel MIP model and an SA algorithm.

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