A genetic algorithm for solving the economic lot scheduling problem in flow shops

Huang, Jia Yen; Yao, Ming-Jong

doi:10.1080/00207540600665893

Cited by 16 publications

(5 citation statements)

References 44 publications

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“…The priority order for each chromosome during the evolution is based on its fitness function value, which is set to be the prediction accuracy of machine learning. Referencing Huang and Yao’s [ 25 ] elitism strategy, this study adopted the roulette wheel mechanism to select chromosomes for reproduction in the HGA. It directly copied 10% of the chromosomes with the best fitness values to the next generation.…”

Section: Methodsmentioning

confidence: 99%

Using Social Network Sentiment Analysis and Genetic Algorithm to Improve the Stock Prediction Accuracy of the Deep Learning-Based Approach

Huang

Tung

Wang

2023

Int J Comput Intell Syst

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Traditionally, most investment tools used to predict stocks are based on quantitative variables, such as finance and capital flow. With the widespread impact of the Internet, investors and investment institutions designing investment strategies are also referring to online comments and discussions. However, multiple information sources, along with uncertainties accompanying international political and economic events and the recent pandemic, have left investors concerned about information interpretation approaches that could aid investment decision-making. To this end, this study proposes a method that combines social media sentiment, genetic algorithm (GA), and deep learning to predict changes in stock prices. First, it employs a hybrid genetic algorithm (HGA) combined with machine learning to identify chip-based indicators closely related to fluctuations in stock prices and then uses them as input for long short-term memory (LSTM) to establish a prediction model. Next, this study proposes five sentiment variables to analyze PTT social media on TSMC’s stock price and performs a grey relational analysis (GRA) to identify the sentiment variables most closely related to stock price fluctuations. The sentiment variables are then combined with the selected chip-based indicators as input to build the LSTM prediction model. To improve the efficiency of the LSTM analysis, this study applies the Taguchi method to optimize the hyper-parameters. The results show that the proposed method of using HGA-screened chip-based variables and social media sentiment variables as input to establish an LSTM prediction model can effectively improve the prediction accuracy of stock price fluctuations.

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Section: Methodsmentioning

confidence: 99%

Using Social Network Sentiment Analysis and Genetic Algorithm to Improve the Stock Prediction Accuracy of the Deep Learning-Based Approach

Huang

Tung

Wang

2023

Int J Comput Intell Syst

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“…From solution point of view, several heuristics were devised to address the flow shop problem [25]. Moreover, the computational complexity of FSSPs results in the application of metaheuristic algorithms such as genetic algorithm [7,11], particle swarm optimization [29], immune algorithm [35], ant colony system (ACS), tabu search [33]. A comprehensive literature review has been provided by Gupta [6] for the FSSPs over last five decades.…”

Section: Related Work On Fsspmentioning

confidence: 99%

Research on group search optimizers for a reconfigurable flow shop sequencing problem

Mokhtari

2015

Neural Comput & Applic

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A reconfigurable manufacturing system is usually designed for quick re-adjusting of production capacity in response to market changes. In this paper, we study a flow shop sequencing problem (FSSP) with controllable processing times as a special case of reconfigurable manufacturing system. It is possible to speed up the processing times through assigning additional resources or control of machine speed. After formulating this problem mathematically, a novel evolutionary procedure, entitled group search optimizer (GSO), is devised as solution method. The adapted GSO is a population-based search tool which is devised based on the producer and scrounger behavior. GSO emphasizes on imitating searching model of real-world animals. The basic GSO with four promising improvements is elaborated and discussed for addressing the FSSP with controllable processing times. A set of computational experiments is also conducted to demonstrate the applicability of proposed FSSP and performance of improved GSOs.

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“…Ouenniche and Boctor (2001a, b) developed two heuristics to solve the flow shop version of the ELSP using two different basic period approaches (namely the two‐group approach and power‐of‐two approach, respectively). Huang and Yao (2007, 2008) developed another two algorithms and compared the algorithms to Ouenniche and Boctor studies (2001a, b). Huang and Yao (2007) modified Ouenniche and Boctor's heuristics (2001b) with the objective to achieve evenly loaded assignment, and Huang and Yao (2008) applied a GA to take peak loading into consideration so that its performance is benchmarked with the two Ouenniche and Boctor studies (2001a, b).…”

Section: Key Research Themesmentioning

confidence: 99%

“…Huang and Yao (2007, 2008) developed another two algorithms and compared the algorithms to Ouenniche and Boctor studies (2001a, b). Huang and Yao (2007) modified Ouenniche and Boctor's heuristics (2001b) with the objective to achieve evenly loaded assignment, and Huang and Yao (2008) applied a GA to take peak loading into consideration so that its performance is benchmarked with the two Ouenniche and Boctor studies (2001a, b). Akrami et al (2006) and Jenabi et al (2007) presented another two studies utilizing computational intelligence techniques (GA, simulation‐annealing and tabu search) to solve the ELSP in a flow shop setting.…”

Section: Key Research Themesmentioning

confidence: 99%

“…With respect to the complexity of the ELSP problem discussed above, there is a noticeable number of studies employed heuristic approaches to find close‐to‐optimal solutions of the problem (Khouga et al , 1998; Moon et al , 2002a, b; Raza et al , 2006; Huang and Yao, 2008; Raza and Akgunduz, 2008; Brander and Segerstedt, 2009). This leads to diversified research focuses (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Recent research trend of economic‐lot scheduling problems

Chan

Chung

Lim

2013

Journal of Manufacturing Technology Management

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PurposeThis research note aims to present a summary of research concerning economic‐lot scheduling problem (ELSP).Design/methodology/approachThe paper's approach is to review over 100 selected studies published in the last 15 years (1997‐2012), which are then grouped under different research themes.FindingsFive research themes are identified and insights for future studies are reported at the end of this paper.Research limitations/implicationsThe motivation of preparing this research note is to summarize key research studies in this field since 1997, when the ELSP problems have been verified as NP‐hard.Originality/valueELSP is an important scheduling problem that has been studied since the 1950s. Because of its complexity in delivering a feasible analytical closed form solution, many studies in the last two decades employed heuristic algorithms in order to come up with good and acceptable solutions. As a consequence, the solution approaches are quite diversified. The major contribution of this paper is to provide researchers who are interested in this area with a quick reference guide on the reviewed studies.

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A genetic algorithm for solving the economic lot scheduling problem in flow shops

Cited by 16 publications

References 44 publications

Using Social Network Sentiment Analysis and Genetic Algorithm to Improve the Stock Prediction Accuracy of the Deep Learning-Based Approach

Using Social Network Sentiment Analysis and Genetic Algorithm to Improve the Stock Prediction Accuracy of the Deep Learning-Based Approach

Research on group search optimizers for a reconfigurable flow shop sequencing problem

Recent research trend of economic‐lot scheduling problems

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