The definition of assembly line balancing difficulty and evaluation of balance solution quality

2001 ◽  
Vol 17 (1-2) ◽  
pp. 81-86 ◽  
Author(s):  
J. Driscoll ◽  
D. Thilakawardana
Keyword(s):  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Solution Quality ◽  
Balance Solution ◽  
Definition Of

scholarly journals Assessment of metaheuristic algorithms to optimize of mixed-model assembly line balancing problem with resource constraints

2020 ◽  
Vol 4 (2) ◽  
pp. 73-83
Author(s):  
M.M. Razali ◽  
M.F.F. Ab. Rashid ◽  
M.R.A. Make
Keyword(s):  
Assembly Line ◽  
Mixed Model ◽  
Assembly Line Balancing ◽  
Metaheuristic Algorithms ◽  
Line Balancing ◽  
Future Research ◽  
Solution Quality ◽  
Assembly Line Balancing Problem ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

Mixed-model assembly line balancing problem (MMALBP) is an NP-hard problem which requires an effective algorithm for solution. In this study, an assessment of metaheuristic algorithms to optimize MMALBP is conducted using four popular metaheuristics for this problem, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Three categories of test problem (small, medium, and large) were used ranging from 8 to 100 number of tasks. For computational experiment, MATLAB software is used in investigate the metaheuristic algorithms performances to optimize the designated objective functions. The results reveal that ACO algorithm performed better in term of finding the best fitness functions when dealing with a large number of tasks. Averagely, it has produces better solution quality than PSO by 5.82%, GA by 9.80%, and SA by 7.66%. However, PSO more superior in term of processing time compared to ACO by 29.25%, GA by 40.54%, and SA by 73.23%. Hence, future research directions such as using the actual manufacturing assembly line data to test the algorithm performances are likely to happen.

Hybrid Genetic Algorithm for Solving Assembly Line Balancing Problem in Footwear Industry

2014 ◽  
Vol 939 ◽  
pp. 623-629 ◽  
Author(s):  
James C. Chen ◽  
Chien Wei Wu ◽  
Tran Dinh Duy Thao ◽  
Ling Huey Su ◽  
Wen Haiung Hsieh ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Hybrid Genetic Algorithm ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Production Cycle ◽  
Product Model ◽  
Solution Quality ◽  
Footwear Industry ◽  
Assembly Line Balancing Problem

This research develops a heuristic algorithm for assembly line balancing problem (ALBP) of stitching lines in footwear industry. The proposed algorithm can help to design the stitching line with workstations, machines and operators for the production of every new product model. Rank-positional-weighted heuristics and hybrid genetic algorithms are proposed to solve ALBP. First, the heuristics assign tasks and machines to workstations. This solution is then used as an initiative population for hybrid genetic algorithm for further improvement. Real data from footwear manufacturers and experimental designs are used to verify the performance of the proposed algorithm, comparing with one existing bidirectional heuristic. Results indicate that when the size and shape of shoes increase, the proposed genetic algorithm achieves better solution quality than existing heuristics.Production managers can use the research results to quickly design stitching lines for short production cycle time and high labor utilization.

scholarly journals Assembly Line Balancing Based on Beam Ant Colony Optimisation

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Jiage Huo ◽  
Zhengxu Wang ◽  
Felix T. S. Chan ◽  
Carman K. M. Lee ◽  
Jan Ola Strandhagen
Keyword(s):  
Ant Colony Algorithm ◽  
Assembly Line ◽  
Hybrid Approach ◽  
Assembly Line Balancing ◽  
Ant Colony ◽  
Line Balancing ◽  
Time Variability ◽  
Problem Size ◽  
Solution Quality ◽  
Comparison Results

We use a hybrid approach which executes ant colony algorithm in combination with beam search (ACO-BS) to solve the Simple Assembly Line Balancing Problem (SALBP). The objective is to minimise the number of workstations for a given fixed cycle time, in order to improve the solution quality and speed up the searching process. The results of 269 benchmark instances show that 95.54% of the problems can reach their optimal solutions within 360 CPU time seconds. In addition, we choose order strength and time variability as indicators to measure the complexity of the SALBP instances and then generate 27 instances with a total of 400 tasks (the problem size being much larger than that of the largest benchmark instance) randomly, with the order strength at 0.2, 0.6 and 0.9 three levels and the time variability at 5-15, 65-75, and 135-145 levels. However, the processing times are generated following a unimodal or a bimodal distribution. The comparison results with solutions obtained by priority rule show that ACO-BS makes significant improvements on the quality of the best solutions.

A constraint programming approach to type-2 assembly line balancing problem with assignment restrictions

2019 ◽  
Vol 39 (5) ◽  
pp. 813-826 ◽  
Author(s):  
Mehmet Pinarbasi ◽  
Hacı Mehmet Alakas ◽  
Mustafa Yuzukirmizi
Keyword(s):  
Constraint Programming ◽  
Cycle Time ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Programming Approach ◽  
Solution Quality ◽  
Content Type ◽  
Assembly Line Balancing Problem

Purpose Main constraints for an assembly line balancing problem (ALBP) are cycle time/number of stations and task precedence relations. However, due to the technological and organizational limitations, several other restrictions can be encountered in real production systems. These restrictions are called as assignment restrictions and can be task assignment, station, resource and distance limitations. The purpose of the study is to evaluate the effects of these restrictions on ALBP using constraint programming (CP) model. Design/methodology/approach A novel CP model is proposed and compared to mixed-integer programming (MIP) as a benchmark. The objective is to minimize the cycle time for a given number of stations. The authors also provide explicit anthology of the assignment restriction effects on line efficiency, the solution quality and the computation time. Findings The proposed approach is verified with the literature test instances and a real-life problem from a furniture manufacturing company. Computational experiments show that, despite the fact that additional assignment restrictions are problematic in mathematical solutions, CP is a versatile exact solution alternative in modelling and the solution quality. Practical implications Assembly line is a popular manufacturing system in the making of standardized high volume products. The problem of assembly line balancing is a crucial challenge in these settings and consists of assigning tasks to the stations by optimizing one or more objectives. Type-2 AR-ALBP is a specific case with the objective function of minimizing the cycle time for a given number of stations. It further assumes assignment restrictions that can be confronted due to the technological limitations or the strategic decisions of the company management. This is especially encountered in rebalancing lines. Originality/value Several solution approaches such as mathematical modelling, heuristic and meta-heuristic are proposed to solve the ALBP in the literature. In this study, a new approach has been presented using CP. Efficient models are developed for Type-2 ALBP with several assignment restrictions. Previous studies have not considered the problem to the presented extent. Furthermore, to the best of the authors’ knowledge, the paper is the first study that solves ALBP with assignment restrictions using CP.

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