Multi-Site Aggregate Production Planning Using Particle Swarm Optimization

Wibawa, Aji Prasetya; Mahmudy, Wayan Firdaus; Rizki, Agung Mustika; Yuliastuti, Gusti Eka; Tama, Ishardita Pambudi

doi:10.32738/jeppm-2022-0006

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The main problem in VRP is how to determine the route of the vehicle so that each customer is served exactly one vehicle, customer demand is met, and the total distance travelled is a minimum for all vehicles. Thus, the use of GA is important to find an optimal solution for a relatively short time [66]. The components needed to implement GA are the parameters, initial population, evaluation of fitness function, selection process and genetic operators such as crossover and mutation [67].…”

Section: Vehicle Routing Problem (Vrp)mentioning

confidence: 99%

A Review of Genetic Algorithm: Operations and Applications

Faizatulhaida Md Isa,

Wan Nor Munirah Ariffin,

Muhammad Shahar Jusoh

et al. 2024

ARASET

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This article presents a review of the Genetic Algorithm (GA), a prominent optimization technique inspired by natural selection and genetics. In the context of rapidly evolving computational methodologies, GA have gained considerable attention for their efficacy in solving complex optimization problems across various domains. The background highlights the growing significance of optimization techniques in addressing real-world challenges. However, the inherent complexity and diversity of problems necessitate versatile approaches like GA. The problem statement underscores the need to explore the underlying operations and applications of GA to provide a nuanced understanding of their capabilities and limitations. The objectives of this review encompass delving into the fundamental genetic operators, such as selection, crossover, and mutation, while examining their role in maintaining diversity and converging toward optimal solutions. Methodology-wise, a systematic analysis of existing literature is undertaken to distil key insights and trends in GA applications. The main findings show the adaptability of GA in tackling problems spanning engineering, economics, bioinformatics, and beyond. By facilitating the discovery of optimal or near-optimal solutions within large solution spaces, GA proves its mettle in scenarios where traditional methods fall short. The conclusion underscores the enduring relevance of GA in the optimization landscape, emphasizing their potential to remain a pivotal tool for addressing intricate real-world challenges, provided their parameters are fine-tuned judiciously to balance exploration and exploitation.

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Section: Vehicle Routing Problem (Vrp)mentioning

confidence: 99%

A Review of Genetic Algorithm: Operations and Applications

Faizatulhaida Md Isa,

Wan Nor Munirah Ariffin,

Muhammad Shahar Jusoh

et al. 2024

ARASET

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“…Utilize the PSO algorithm to determine the ideal parameter value. Figure 1 depicts the PSO algorithm's steps (Vijayakumar & Vinothkanna, 2020); (Gad, 2022), (Wibawa et al, 2022).…”

Section: Forecasting Systemmentioning

confidence: 99%

NFT Investments Analysis: A Strategic Approach with Ranking Insights and Sales Forecasting System for Informed Decision-Making

Fitria,

Afandi,

Aripriharta

et al. 2023

ajtm

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Abstract. The non-fungible token (NFT) is a unique token used to represent digital assets such as art, music, videos, and other collections. NFT has gained significant attention from the business and industry sectors in recent years. This study reports an increase in the number of active NFT users from 77,000 to 222,000 in early 2021. Investment in NFT has advantages and disadvantages, and one of the challenges faced by investors is that they may not have enough knowledge about investing risks and may find it difficult to recognize and evaluate potential dangers. To address this problem, this study proposes a system that provides information on NFT collection sales rankings and volume sales forecasts. The simple additive weighting (SAW) method is used to determine the NFT collection rankings, and exponential smoothing is used to forecast sales volume. The Particle Swarm Optimization (PSO) method is applied to optimize the parameter alpha of the Exponential Smoothing method. With an accuracy rate of 80.38%, the combination of using the Single Exponential Smoothing method with PSO optimization can provide good predictions for future NFT sales. The proposed system aims to provide investors with accurate information to make informed decisions when investing in NFT. Keywords: Forecasting system, pso, ranking system, saw, single exponential smoothing

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