An improved NSGA - II algorithm for mixed model assembly line balancing

Yongming, Wu; Xu, Yuan; Luo, Ling; Zhang, Han; Zhao, Xudong

doi:10.1063/1.5039129

Cited by 2 publications

(2 citation statements)

References 6 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm has been used to optimize ALB with resource constraints, aiming to minimize the number of resources required in assembly, in addition to the number of stations and cycle time (Jusop and Rashid, 2016). Moreover, the NSGA-II was improved using the ecological evolution strategy by introducing a self-detector operator to detect solution changes (Wu et al, 2018). A hybrid NSGA-II, which was hybridized with fuzzy objectives to optimize the workstation and cycle time, was also used to improve performance (Alavidoost and Nayeri, 2014).…”

Section: Multiobjective Tiki-taka Algorithmmentioning

confidence: 99%

A new multiobjective tiki-taka algorithm for optimization of assembly line balancing

Rashid

Ramli

2023

View full text Add to dashboard Cite

PurposeThis study aims to propose a new multiobjective optimization metaheuristic based on the tiki-taka algorithm (TTA). The proposed multiobjective TTA (MOTTA) was implemented for a simple assembly line balancing type E (SALB-E), which aimed to minimize the cycle time and workstation number simultaneously.Design/methodology/approachTTA is a new metaheuristic inspired by the tiki-taka playing style in a football match. The TTA is previously designed for a single-objective optimization, but this study extends TTA into a multiobjective optimization. The MOTTA mimics the short passing and player movement in tiki-taka to control the game. The algorithm also utilizes unsuccessful ball pass and multiple key players to enhance the exploration. MOTTA was tested against popular CEC09 benchmark functions.FindingsThe computational experiments indicated that MOTTA had better results in 82% of the cases from the CEC09 benchmark functions. In addition, MOTTA successfully found 83.3% of the Pareto optimal solution in the SALB-E optimization and showed tremendous performance in the spread and distribution indicators, which were associated with the multiple key players in the algorithm.Originality/valueMOTTA exploits the information from all players to move to a new position. The algorithm makes all solution candidates have contributions to the algorithm convergence.

show abstract

Section: Multiobjective Tiki-taka Algorithmmentioning

confidence: 99%

A new multiobjective tiki-taka algorithm for optimization of assembly line balancing

Rashid

Ramli

2023

View full text Add to dashboard Cite

show abstract

“…Figure 1 represents the flowchart of the NSGAII algorithm, which is an improved NSGAII algorithm for mixed model assembly line balancing proposed by Wu et al [21]. In general, since NSGA II uses fast non-dominated sorting and crowded distance sorting mechanisms, it has a better distribution and convergence.…”

Section: Short Definitions Of Metaheuristicsmentioning

confidence: 99%

A New Multi-Heuristic Method to Optimize the Ammonia–Water Power/Cooling Cycle Combined with an HCCI Engine

2023

View full text Add to dashboard Cite

Nowadays, sustainability is one of the key elements which should be considered in energy systems. Such systems are essential in any manufacturing system to supply the energy requirements of those systems. To optimize the energy consumption of any manufacturing system, various applications have been developed in the literature, with a number of pros and cons. In addition, in the majority of such applications, multi-objective optimization (MOO) plays an outstanding role. In related studies, the MOO strategy has been mainly used to maximize the performance and minimize the total cost of a trigeneration system with an HCCI (homogeneous charge compression ignition) engine as a prime mover based on the NSGA-II (non-dominated sorting genetic algorithm-II) algorithm. The current study introduces a novel multi-heuristic system (MHS) that serves as a metaheuristics cooperation platform for selecting the best design parameters. The MHS operates on a proposed strategy and prefers short runs of various metaheuristics to a single long run of a metaheuristic. The proposed MHS consists of four multi-objective metaheuristics collaborating to work on a common population of solutions. The optimization aims to maximize the exergy efficiency and minimize the total system cost. By utilizing four local archives and one global archive, the system optimizes these two objective functions. The idea behind the proposed MHS is that metaheuristics will be able to compensate for each other’s shortcomings in terms of extracting the most promising regions of the search space. Comparing the findings of the developed MHS shows that implementing the suggested strategy decreases the total unit costs of the system products to 25.85 USD/GJ, where the total unit cost of the base system was 28.89 USD/GJ, and the exergy efficiency of the system is increased up to 39.37%, while this efficiency was 22.81% in the base system. The finding illustrates significant improvements in system results and proves the high performance of the proposed method.

show abstract

An improved NSGA - II algorithm for mixed model assembly line balancing

Cited by 2 publications

References 6 publications

A new multiobjective tiki-taka algorithm for optimization of assembly line balancing

A new multiobjective tiki-taka algorithm for optimization of assembly line balancing

A New Multi-Heuristic Method to Optimize the Ammonia–Water Power/Cooling Cycle Combined with an HCCI Engine

Contact Info

Product

Resources

About