A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

Thammano, Arit; Teekeng, Wannaporn

doi:10.1080/03081079.2014.969252

Cited by 29 publications

(12 citation statements)

References 17 publications

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“…GA is a natural process of evolution in biological sciences, and it has the power of solving heuristics-related problems [23,27]. The authors in [28] used a modified version of genetic algorithms to solve flexible job-shop scheduling problems (JSPs). Genetic algorithms are a subset of stochastic-search algorithms that can serve as a very effective selection method for discovering optimal solutions to a wide range of problems.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

et al. 2020

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Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

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Section: Genetic Algorithmsmentioning

confidence: 99%

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

et al. 2020

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“…The probability of the individual being selected is proportional to its fitness function value, that is, the higher the fitness function value, the greater the probability of individuals being selected; on the contrary, the lower the fitness function value, the smaller the probability of individuals being selected. This can make the genetic algorithm evolve in a better direction, which is very helpful to improve the quality of the solution [48]. In this genetic algorithm, the fitness function of chromosomes is required to be non-negative, because the objective function of the VRP (Vehicle Routing Problem) is positive, and the objective function of this study is to minimize the total cost, so reciprocal of the objective function can be directly taken as the fitness function…”

Section: Fitness Conversionmentioning

confidence: 99%

A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission

Liao

Wang

2019

Sustainability

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The manufacturing industry is undergoing transformation and upgrading from traditional manufacturing to intelligent manufacturing, in which Internet of Things (IoT) technology plays a central role in promoting the development of intelligent manufacturing. In order to solve the problem that low production efficiency and machine utilization lead to serious pollution emissions in the workshop caused by untimely transmission of information in all links of the production and manufacturing process to whole supply chains, this study establishes an intelligent production scheduling and logistics delivery model with IoT technology to promote green and sustainable development of intelligent manufacturing. Firstly, an application framework of IoT technology in production–delivery supply chain systems was established to improve efficiency and achieve the integration of production and delivery. Secondly, an integrated production–delivery model was constructed, which takes into account time and low carbon constraints. Finally, a two-layer optimization algorithm was proposed to solve this integration problem. Through a case study, the results show this integration production–delivery model can reduce the cost of supply chains and improve customer satisfaction. Moreover, it proves that carbon emission cost is a major factor affecting total cost, and it could help enterprises to realize the profit and sustainable development of the environment. The production–delivery model could also support the last kilometer distribution problem and extension under E-commerce applications.

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“…(4) Select ( /2) individuals from the population ( ). This paper uses 'roulette wheel' selection as the selection operator; a detailed discussion of this selection type can be found in [26].…”

Section: The Proposed Improved Bega-based ML Doa Estimation Algorithmmentioning

confidence: 99%

Application of Bee Evolutionary Genetic Algorithm to Maximum Likelihood Direction‐of‐Arrival Estimation

Fan

Lin-bin

Shi

et al. 2019

Mathematical Problems in Engineering

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The maximum likelihood (ML) method achieves an excellent performance for DOA estimation. However, its computational complexity is too high for a multidimensional nonlinear solution search. To address this issue, an improved bee evolutionary genetic algorithm (IBEGA) is applied to maximize the likelihood function for DOA estimation. First, an opposition-based reinforcement learning method is utilized to achieve a better initial population for the BEGA. Second, an improved arithmetic crossover operator is proposed to improve the global searching performance. The experimental results show that the proposed algorithm can reduce the computational complexity of ML DOA estimation significantly without sacrificing the estimation accuracy.

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A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

Cited by 29 publications

References 17 publications

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission

Application of Bee Evolutionary Genetic Algorithm to Maximum Likelihood Direction‐of‐Arrival Estimation

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