Optimal Short-Term Forecasting Using GA-Based Holt-Winters Method

Navarro, Maricar M.; Navarro, Bryan B.

doi:10.1109/ieem44572.2019.8978638

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…An ant colony algorithm was developed by Solimanpur et al (2005) to address the sequence-dependent single-row machine layout problem. Both limited and unconstrained dynamic layout concerns were addressed by Baykasoglu and Gindy (2001) 2020), Lin et al (2015), , Misola and Navarro (2013), , , Navarro et al (2023). Additionally, several studies in the literature seek to maximize production employing a pertinent Design Trade-Off Duque et al (2016), Sison et al (2018), Palisoc et al (2019), and with the use of Pro Model simulation, Navarro et al (2015) and Bangayan et al(2016) The shoe industry is highly competitive due to globalization in production and the presence of numerous large companies competing for market share.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Facility Layout for Enhanced Productivity using Pro Model: A Case Study of a Shoe Manufacturing Company in Marikina, Philippines

S. Cabusas,

A. Helis,

Malonda

et al. 2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Through the optimization of business operations and the fulfillment of employee needs, an effective facility layout is essential to the success of manufacturing industrial output. It guarantees a smooth system operation, reduces wasted space, and maximizes resource efficiency. The goal of this study is to identify and address problems that lead to waste and reduce productivity by examining the facility layout of a shoe manufacturing company in Marikina. To accomplish this goal, the main causes of issues with the current facility design were found using the Ishikawa Diagram. Pro Model simulation software was likewise employed to examine and recommend a creative facility layout design that maximizes output and eliminates waste in the company's production process. The findings of the study indicated that lower output levels were significantly attributed to the current facility layout. As a result, a new facility plan was suggested, which led to a significant increase in production output of 16.67%. The shoe factory's manufacturing processes might be optimized, waste reduced, and efficiency increased by putting the suggested facility layout concept into practice. By highlighting the significance of facility layout optimization in boosting efficiency and overall operational performance, this work makes a significant contribution to the field of facility layout optimization.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing Facility Layout for Enhanced Productivity using Pro Model: A Case Study of a Shoe Manufacturing Company in Marikina, Philippines

S. Cabusas,

A. Helis,

Malonda

et al. 2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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show abstract

“…(2) Winters multiplier method In addition to some cooperative data with a stable seasonal component and no trend, we also found data with a linear trend and a seasonal component that changed over time, so we considered introducing a Winters multiplication model for fitting [8].…”

Section: Determination Of Order Allocation Weight Based On Time Serie...mentioning

confidence: 99%

Research on Ordering and Transportation Strategy of Raw Materials for Manufacturing Companies Based on Time Series Model and Monte Carlo Stochastic Simulation

Fu¹,

Zhang²

2022

BCPBM

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A company needs to develop a series of ordering and forwarding strategies to maximize its profitability when it purchases three raw materials from a supplier for the production of decorative building panels, which are delivered to its warehouse by a forwarder. In this paper, we analyze the past cooperation data between the company and its suppliers and forwarders to build a mathematical model and find the optimal solution. The problem is based on quantitative analysis, so it can largely avoid unnecessary expenses caused by subjective and empirical judgments of enterprise managers, and has strong practical significance. In this paper, the 50 most important suppliers selected by considering the past supply characteristics of 402 suppliers are used as the base data. To determine the minimum number of suppliers required. Integrating factors such as raw material cost and transportation loss rate, the weekly ordering and transshipment plan for the next six months will be developed based on the selected suppliers. Based on this, we introduce the goal of purchasing as much as possible A and as little as possible C, develop a new ordering and forwarding program, and conduct a predictive analysis of the program's implementation effects.

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“…Single and double exponential smoothing models were used for this purpose in [57], while Brown's double exponential smoothing method was used in [26]. Different variants of the Holt-Winters additive model (HWAM), which is an extension of Holt's exponential smoothing, has also been exploited on financial data in [44]. A hybrid methodology between Empirical Mode Decomposition with the Holt-Winters method (EMD-HW) was used to improve forecasting performances in financial time series in [3], while EDM-HW bagging was presented in [4].…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Contemporary Methods on Univariate Time Series Forecasting

Karanikola

Liapis

Kotsiantis

2021

Learning and Analytics in Intelligent Systems

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In data science, time series forecasting is the process of utilizing past or present (known) observations of a target variable to make predictions about future (unknown) observations. Due to the usefulness of forecasting applications in numerous real-life problems, various Statistical and Machine Learning forecasting models have been proposed over recent years. The purpose of this chapter is to compare the performance of several contemporary forecasting models that are considered state of the art. These include Autoregressive Integrated Moving Average (ARIMA), Neural Basis Expansion Analysis (NBEATS), Probabilistic Time Series Modeling focusing on deep learning-based models and others. In the first section of this work a brief theoretical background of the methods is provided. Then, the experimental procedure is being described. For the comparison, 40 univariate time series of financial data that cover a 1-year period were used. A python repository of automated time series forecasting models (AtsPy) was exploited to run the experiments. For the final comparison three different metrics (RMSE, MAE and MAPE) were taken into consideration. The results of this extended experimental procedure are presented through various explanatory diagrams of the methods' performance in the final section.

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Optimal Short-Term Forecasting Using GA-Based Holt-Winters Method

Cited by 12 publications

References 16 publications

Optimizing Facility Layout for Enhanced Productivity using Pro Model: A Case Study of a Shoe Manufacturing Company in Marikina, Philippines

Optimizing Facility Layout for Enhanced Productivity using Pro Model: A Case Study of a Shoe Manufacturing Company in Marikina, Philippines

Research on Ordering and Transportation Strategy of Raw Materials for Manufacturing Companies Based on Time Series Model and Monte Carlo Stochastic Simulation

A Comparison of Contemporary Methods on Univariate Time Series Forecasting

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