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- Naragain Phumchusri
Assoc. Prof. Naragain Phumchusri, Ph.D.
- 5th Floor of Engineering 4 Bldg., Room 504
- +66-2218-6822
- naragain.p@chula.ac.th
Overview
Dr. Naragain Phumchusri is an associate professor at the Department of Industrial Engineering, Chulalongkorn University, Thailand. She received her Ph.D. in Industrial Engineering from Georgia Institute of Technology, Atlanta, GA, USA in 2010. Her current research interests include stochastic models for revenue management, machine learning for demand forecasting, inventory optimization, warehouse & supply chain management, data analysis for tourism industry and promotion optimization in retails.
Education
Ph.D. in Industrial Engineering
Georgia Institute of Technology, United States, 2010
Master of Science in Industrial Engineering
Georgia Institute of Technology, United States, 2006
B.Eng. in Industrial Engineering
Chulalongkorn University, Thailand, 2004
Expertise
Statistics & Data Analysis
Publications
2026
Yanvaroj Pongsethpaisal, Naragain Phumchusri, Paveena Chaovalitwongse
Mutation-Augmented NEAT (M-NEAT): Improving Neuroevolutionary Performance in Divergent Multi-Echelon Inventory Systems Journal Article
In: Engineering Journal, vol. 30, no. 4, pp. 63 – 79, 2026, (Cited by: 0).
@article{Pongsethpaisal202663,
title = {Mutation-Augmented NEAT (M-NEAT): Improving Neuroevolutionary Performance in Divergent Multi-Echelon Inventory Systems},
author = {Yanvaroj Pongsethpaisal and Naragain Phumchusri and Paveena Chaovalitwongse},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105038697693&doi=10.4186%2fej.2026.30.4.63&partnerID=40&md5=5f0795494f1692175142df0fe80be8cd},
doi = {10.4186/ej.2026.30.4.63},
year = {2026},
date = {2026-01-01},
journal = {Engineering Journal},
volume = {30},
number = {4},
pages = {63 – 79},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2025
Guan Gui, Mouquan Shen, Liquan Chen, Sos S. Agaian, G. Jovanovic Dolecek, Yi Lou, Qi Zhu, Gyu Myoung Lee, Gabriel Gomes Oliveira, Jun Lin, Xin Si, Cheng Siong Lee, Aslina Baharum, Dongming Li, Xiangping Zhai, Liqing Shan, Ruoyu Zhang, Xinzhou Xu, Yu Jiang, Lingtong Min, Dawei Wang, Tianchong Gao, Xuecai Bao, Giridhar Reddy Bojja, Fenghui Zhang, Biyun Chen, Paul Wen, Chen Gong, Tianrui Li, Yudong Zhang, Jingshan Huang, Alireza Vali Pour Baboli, Michele Melchiori, Guandong Xu, Xiaoxiao Wang, Yuancheng Li, Naragain Phumchusri, Gajendra Sharma, Thomas Lee, Yijun Bei, Fernanda Otilia Figueiredo, Tien-Ying Kuo, Oras Baker, Khondker Shajadul Hasan, Ainul Azila Che Fauzi, Zhuo Li, Nakul Sharma, Meng Wang, Xiaoliang Wang
Preface Conference
2025, (Cited by: 0).
@conference{Gui2025,
title = {Preface},
author = {Guan Gui and Mouquan Shen and Liquan Chen and Sos S. Agaian and G. Jovanovic Dolecek and Yi Lou and Qi Zhu and Gyu Myoung Lee and Gabriel Gomes Oliveira and Jun Lin and Xin Si and Cheng Siong Lee and Aslina Baharum and Dongming Li and Xiangping Zhai and Liqing Shan and Ruoyu Zhang and Xinzhou Xu and Yu Jiang and Lingtong Min and Dawei Wang and Tianchong Gao and Xuecai Bao and Giridhar Reddy Bojja and Fenghui Zhang and Biyun Chen and Paul Wen and Chen Gong and Tianrui Li and Yudong Zhang and Jingshan Huang and Alireza Vali Pour Baboli and Michele Melchiori and Guandong Xu and Xiaoxiao Wang and Yuancheng Li and Naragain Phumchusri and Gajendra Sharma and Thomas Lee and Yijun Bei and Fernanda Otilia Figueiredo and Tien-Ying Kuo and Oras Baker and Khondker Shajadul Hasan and Ainul Azila Che Fauzi and Zhuo Li and Nakul Sharma and Meng Wang and Xiaoliang Wang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017650120&doi=10.1109%2fDMCIS65888.2025.11138398&partnerID=40&md5=650ae395469cc176701affc9370db465},
doi = {10.1109/DMCIS65888.2025.11138398},
year = {2025},
date = {2025-01-01},
journal = {2025 2nd International Conference on Digital Media, Communication and Information Systems, DMCIS 2025},
note = {Cited by: 0},
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pubstate = {published},
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Chutima Binsriavanich, Naragain Phumchusri
An analysis of retail promotional pricing effectiveness using agent-based modeling Journal Article
In: Journal of Revenue and Pricing Management, vol. 24, no. 1, pp. 60 – 79, 2025, (Cited by: 2).
@article{Binsriavanich202560,
title = {An analysis of retail promotional pricing effectiveness using agent-based modeling},
author = {Chutima Binsriavanich and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211935209&doi=10.1057%2fs41272-024-00512-7&partnerID=40&md5=fb87bdd84c73aeee7fa60475fb557f63},
doi = {10.1057/s41272-024-00512-7},
year = {2025},
date = {2025-01-01},
journal = {Journal of Revenue and Pricing Management},
volume = {24},
number = {1},
pages = {60 – 79},
publisher = {Palgrave Macmillan},
abstract = {In contemporary urban contexts, retail establishments have emerged as essential components of city life, engaging in fierce competition to capture consumer attention and augment their financial gains. Implementing a price promotion strategy is essential for efficaciously appealing customers. Nonetheless, the complex interaction between consumer preferences and product attributes, particularly in environments characterized by competitive product offerings, complicates the development of effective promotional strategies. This paper aims to present an agent-based simulation model for capturing the results of strategic approaches for retails offering competitive products, thereby sidestepping the need for empirical testing or data collection in real-world settings. In this model, consumer decision-making processes are initially influenced by two primary factors: the effectiveness of advertising and the impact of word-of-mouth communication. Subsequent decisions are then depending on the degree of price reduction encountered in-store. The simulation assesses various promotional tactics, examining the depth of price reductions, the frequency and timing of promotions, and the resultant impact on store profitability. The outcomes reveal that distinct strategies yield varying levels of effectiveness depending on the price elasticity of products. Moreover, non-overlapping promotional happenings yield superior profit margins as compared to concurrent promotions. The insights garnered from this study are anticipated to provide helpful guidance for future strategic planning for retails. © The Author(s), under exclusive licence to Springer Nature Limited 2024.},
note = {Cited by: 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In contemporary urban contexts, retail establishments have emerged as essential components of city life, engaging in fierce competition to capture consumer attention and augment their financial gains. Implementing a price promotion strategy is essential for efficaciously appealing customers. Nonetheless, the complex interaction between consumer preferences and product attributes, particularly in environments characterized by competitive product offerings, complicates the development of effective promotional strategies. This paper aims to present an agent-based simulation model for capturing the results of strategic approaches for retails offering competitive products, thereby sidestepping the need for empirical testing or data collection in real-world settings. In this model, consumer decision-making processes are initially influenced by two primary factors: the effectiveness of advertising and the impact of word-of-mouth communication. Subsequent decisions are then depending on the degree of price reduction encountered in-store. The simulation assesses various promotional tactics, examining the depth of price reductions, the frequency and timing of promotions, and the resultant impact on store profitability. The outcomes reveal that distinct strategies yield varying levels of effectiveness depending on the price elasticity of products. Moreover, non-overlapping promotional happenings yield superior profit margins as compared to concurrent promotions. The insights garnered from this study are anticipated to provide helpful guidance for future strategic planning for retails. © The Author(s), under exclusive licence to Springer Nature Limited 2024.
Santi Wongkamphu, Naragain Phumchusri
Hybrid Approaches to Machine Learning for Improved Battery Sales Forecasting: A Case Study in Thailand Journal Article
In: Engineering Journal, vol. 29, no. 2, pp. 27 – 43, 2025, (Cited by: 5; All Open Access, Gold Open Access).
@article{Wongkamphu202527,
title = {Hybrid Approaches to Machine Learning for Improved Battery Sales Forecasting: A Case Study in Thailand},
author = {Santi Wongkamphu and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000148099&doi=10.4186%2fej.2025.29.2.27&partnerID=40&md5=ddfdc08c3e9395cf76d0a3106ac47ba8},
doi = {10.4186/ej.2025.29.2.27},
year = {2025},
date = {2025-01-01},
journal = {Engineering Journal},
volume = {29},
number = {2},
pages = {27 – 43},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {Battery sales forecasting is a critical component of demand planning in the automotive battery industry, directly influencing production, inventory management, and supply chain optimization. This study presents a comprehensive evaluation of traditional forecasting methods and machine learning techniques to predict monthly sales for a battery manufacturer in Thailand. Utilizing a dataset of monthly sales for the 10 best-selling products from January 2018 to December 2023, the research investigates the performance of traditional models such as Holt’s Linear Trend, Holt-Winters Seasonal, ARIMA, SARIMA, and SARIMAX. Advanced machine learning approaches, including Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANN), are also explored. Additionally, hybrid models combining traditional and machine learning techniques are developed to leverage their respective strengths. The study integrates external factors such as economic indicators, industry-specific variables, and lagged data during feature selection to enhance predictive accuracy. Model performance is rigorously evaluated using Mean Absolute Percentage Error (MAPE). The results demonstrate that the hybrid ANN-LSTM model achieves the highest accuracy, with an average MAPE of 8.83%, significantly outperforming individual models, including the best-performing traditional model, ANN, at 9.43%. This research contributes to the field by providing a robust analytics framework that integrates traditional and advanced machine learning methodologies, offering actionable insights for battery sales forecasting and enhancing decision-making processes in the automotive industry. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 5; All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Battery sales forecasting is a critical component of demand planning in the automotive battery industry, directly influencing production, inventory management, and supply chain optimization. This study presents a comprehensive evaluation of traditional forecasting methods and machine learning techniques to predict monthly sales for a battery manufacturer in Thailand. Utilizing a dataset of monthly sales for the 10 best-selling products from January 2018 to December 2023, the research investigates the performance of traditional models such as Holt’s Linear Trend, Holt-Winters Seasonal, ARIMA, SARIMA, and SARIMAX. Advanced machine learning approaches, including Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANN), are also explored. Additionally, hybrid models combining traditional and machine learning techniques are developed to leverage their respective strengths. The study integrates external factors such as economic indicators, industry-specific variables, and lagged data during feature selection to enhance predictive accuracy. Model performance is rigorously evaluated using Mean Absolute Percentage Error (MAPE). The results demonstrate that the hybrid ANN-LSTM model achieves the highest accuracy, with an average MAPE of 8.83%, significantly outperforming individual models, including the best-performing traditional model, ANN, at 9.43%. This research contributes to the field by providing a robust analytics framework that integrates traditional and advanced machine learning methodologies, offering actionable insights for battery sales forecasting and enhancing decision-making processes in the automotive industry. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
Yanvaroj Pongsethpaisal, Naragain Phumchusri, Paveena Chaovalitwongse
Employing Neuroevolution of Augmenting Topologies (NEAT) in Linear Multi-Echelon Inventory Systems Journal Article
In: Engineering Journal, vol. 29, no. 3, pp. 11 – 26, 2025, (Cited by: 1; All Open Access, Gold Open Access).
@article{Pongsethpaisal202511,
title = {Employing Neuroevolution of Augmenting Topologies (NEAT) in Linear Multi-Echelon Inventory Systems},
author = {Yanvaroj Pongsethpaisal and Naragain Phumchusri and Paveena Chaovalitwongse},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002468604&doi=10.4186%2fej.2025.29.3.11&partnerID=40&md5=d7799e5a5f4fdc7c974b4d5f64f797af},
doi = {10.4186/ej.2025.29.3.11},
year = {2025},
date = {2025-01-01},
journal = {Engineering Journal},
volume = {29},
number = {3},
pages = {11 – 26},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {Reinforcement learning has emerged as a leading algorithmic approach due to its successful applications across various domains. While many implementations favour the model-free approach for its aptitude for handling complex problems, its learning curve tends to be slower. Given the intricacies of the Linear Multi-Echelon Inventory System, a model-based approach might be more fitting, offering faster learning rates. This study seeks to integrate Neuroevolution of Augment Topologies (NEAT) – a hybrid of model-based reinforcement learning and evolutionary algorithms – into such an inventory system. Furthermore, the research delves into hyperparameter tuning, experimenting with seven specific hyperparameters to discern the most efficient combination and understand their inter-play. Benchmarking against the model-free Proximal Policy Optimisation (PPO) serves as a measure of NEAT’s effectiveness. Findings indicate that when optimally tuned, NEAT can slash total costs by 25.02% compared to PPO. Impressively, NEAT achieves this peak performance in a mere 1,000 generations, significantly outpacing PPO’s learning trajectory. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 1; All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Reinforcement learning has emerged as a leading algorithmic approach due to its successful applications across various domains. While many implementations favour the model-free approach for its aptitude for handling complex problems, its learning curve tends to be slower. Given the intricacies of the Linear Multi-Echelon Inventory System, a model-based approach might be more fitting, offering faster learning rates. This study seeks to integrate Neuroevolution of Augment Topologies (NEAT) – a hybrid of model-based reinforcement learning and evolutionary algorithms – into such an inventory system. Furthermore, the research delves into hyperparameter tuning, experimenting with seven specific hyperparameters to discern the most efficient combination and understand their inter-play. Benchmarking against the model-free Proximal Policy Optimisation (PPO) serves as a measure of NEAT’s effectiveness. Findings indicate that when optimally tuned, NEAT can slash total costs by 25.02% compared to PPO. Impressively, NEAT achieves this peak performance in a mere 1,000 generations, significantly outpacing PPO’s learning trajectory. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
P. Chavalpatanapan, P. Phlaingam, N. Phumchusri
Dashboard Development and Aspect-Based Sentiment Analysis for a Case-study Tour Operator in Thailand Conference
2025, (Cited by: 0).
@conference{Chavalpatanapan2025420,
title = {Dashboard Development and Aspect-Based Sentiment Analysis for a Case-study Tour Operator in Thailand},
author = {P. Chavalpatanapan and P. Phlaingam and N. Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105033976097&doi=10.1109%2fIEEM63636.2025.11357761&partnerID=40&md5=44421a9fa392302f7a9c2f3a9c70f08c},
doi = {10.1109/IEEM63636.2025.11357761},
year = {2025},
date = {2025-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
pages = {420 – 424},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2024
Naragain Phumchusri, Nichakan Phupaichitkun
Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver Journal Article
In: Journal of Revenue and Pricing Management, 2024, (Cited by: 1).
@article{Phumchusri2024,
title = {Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver},
author = {Naragain Phumchusri and Nichakan Phupaichitkun},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189920531&doi=10.1057%2fs41272-024-00477-7&partnerID=40&md5=6daa0bdde1e4e129b142d701f3e1a5c2},
doi = {10.1057/s41272-024-00477-7},
year = {2024},
date = {2024-01-01},
journal = {Journal of Revenue and Pricing Management},
note = {Cited by: 1},
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Thanaporn Kusomrosananan, Naragain Phumchusri
Inventory policy improvement with periodic review for perishable goods: A case study of a retail coffee shop in thailand Journal Article
In: Engineering Journal, vol. 28, no. 6, pp. 59 – 73, 2024, (Cited by: 3; All Open Access, Gold Open Access, Green Open Access).
@article{Kusomrosananan202459,
title = {Inventory policy improvement with periodic review for perishable goods: A case study of a retail coffee shop in thailand},
author = {Thanaporn Kusomrosananan and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205096590&doi=10.4186%2fej.2024.28.6.59&partnerID=40&md5=171ab74c9a64b60013212ec4fea2de34},
doi = {10.4186/ej.2024.28.6.59},
year = {2024},
date = {2024-01-01},
journal = {Engineering Journal},
volume = {28},
number = {6},
pages = {59 – 73},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {Inventory management is a fundamental component of successful retail operations. Effective techniques in retail inventory management are important in fulfilling customer demands, minimizing costs, and enhancing profitability for business in the competitive environment. This study aims to improve the inventory management strategy for perishable goods in a Thai coffee shop case study. The primary goals include minimizing occurrences of inventory surplus or shortage and indicating the most suitable inventory management approach for each stock-keeping unit (SKU). The most efficient inventory strategy is determined by evaluating the total inventory costs, composing of waste costs, potential loss costs, and holding costs. To this end, computational experiments are employed, deploying three varied periodic inventory policies per SKU. These policies differ in term of utilizing mean weekly demand, average daily demand, and modifying delivery schedules and frequencies. In addition to exploring various policies, the service level for each SKU is adjusted according to profit-cost ratio of each SKU to determine the most suitable service level corresponding to the most effective inventory management strategy. Following the experiments, an effective inventory policy for each SKU is determined. Results show that the new proposed policies can reduce costs by 60.74%, or about 256,922 Baht yearly, compared to the current policy. The new policy, based on daily demand and delivery adjustments, leads to smaller order, more frequent deliveries, allowing the perishable goods to be more refreshed. © 2024, Eng. J. All rights reserved.},
note = {Cited by: 3; All Open Access, Gold Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Inventory management is a fundamental component of successful retail operations. Effective techniques in retail inventory management are important in fulfilling customer demands, minimizing costs, and enhancing profitability for business in the competitive environment. This study aims to improve the inventory management strategy for perishable goods in a Thai coffee shop case study. The primary goals include minimizing occurrences of inventory surplus or shortage and indicating the most suitable inventory management approach for each stock-keeping unit (SKU). The most efficient inventory strategy is determined by evaluating the total inventory costs, composing of waste costs, potential loss costs, and holding costs. To this end, computational experiments are employed, deploying three varied periodic inventory policies per SKU. These policies differ in term of utilizing mean weekly demand, average daily demand, and modifying delivery schedules and frequencies. In addition to exploring various policies, the service level for each SKU is adjusted according to profit-cost ratio of each SKU to determine the most suitable service level corresponding to the most effective inventory management strategy. Following the experiments, an effective inventory policy for each SKU is determined. Results show that the new proposed policies can reduce costs by 60.74%, or about 256,922 Baht yearly, compared to the current policy. The new policy, based on daily demand and delivery adjustments, leads to smaller order, more frequent deliveries, allowing the perishable goods to be more refreshed. © 2024, Eng. J. All rights reserved.
Naragain Phumchusri, Thiti Chewcharat, Supawish Kanokpongsakorn
Price promotion optimization model for multiperiod planning: a case study of beauty category products sold in a convenience store chain Journal Article
In: Journal of Revenue and Pricing Management, vol. 23, no. 2, pp. 164 – 178, 2024, (Cited by: 4).
@article{Phumchusri2024164,
title = {Price promotion optimization model for multiperiod planning: a case study of beauty category products sold in a convenience store chain},
author = {Naragain Phumchusri and Thiti Chewcharat and Supawish Kanokpongsakorn},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168125852&doi=10.1057%2fs41272-023-00438-6&partnerID=40&md5=00ab18dc167d2f437dfe5892f31f43d4},
doi = {10.1057/s41272-023-00438-6},
year = {2024},
date = {2024-01-01},
journal = {Journal of Revenue and Pricing Management},
volume = {23},
number = {2},
pages = {164 – 178},
publisher = {Palgrave Macmillan},
abstract = {Pricing strategy is vital in the retail sector as prices play an important role in driving revenues and profits. However, few studies have been conducted on retail promotion optimization, particularly amid the COVID-19 situation. This study aims to leverage statistical models to examine the effects of price promotion and other factors on sales during the COVID-19 period. In addition, an optimization model is proposed to maximize the profitability of a retail store through strategies for optimal promotional pricing. In this study, monthly sales data in four product categories with 245 stock keeping units from July 2020 to June 2022 from a case study convenience store chain were retrieved and preprocessed. Subsequently, statistical models, such as the autoregressive distributed lag model OWN and the autoregressive distributed lag model CROSS, were implemented to examine the effects of price, promotion and other factors on sales. In addition, factors such as price elasticity and cannibalization were extracted and analyzed from the demand models. An optimization model was built in accordance with the demand model to maximize the total profit of the retailer over a certain period by determining the strategy for optimal promotional pricing. Finally, sensitivity analyses were performed to explain the dynamics of the parameters involved in the optimization model. The methodology, results and insights from this research provide a preliminary framework to facilitate Thai retailers in optimizing their pricing strategies and achieving key business objectives. © The Author(s), under exclusive licence to Springer Nature Limited 2023.},
note = {Cited by: 4},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pricing strategy is vital in the retail sector as prices play an important role in driving revenues and profits. However, few studies have been conducted on retail promotion optimization, particularly amid the COVID-19 situation. This study aims to leverage statistical models to examine the effects of price promotion and other factors on sales during the COVID-19 period. In addition, an optimization model is proposed to maximize the profitability of a retail store through strategies for optimal promotional pricing. In this study, monthly sales data in four product categories with 245 stock keeping units from July 2020 to June 2022 from a case study convenience store chain were retrieved and preprocessed. Subsequently, statistical models, such as the autoregressive distributed lag model OWN and the autoregressive distributed lag model CROSS, were implemented to examine the effects of price, promotion and other factors on sales. In addition, factors such as price elasticity and cannibalization were extracted and analyzed from the demand models. An optimization model was built in accordance with the demand model to maximize the total profit of the retailer over a certain period by determining the strategy for optimal promotional pricing. Finally, sensitivity analyses were performed to explain the dynamics of the parameters involved in the optimization model. The methodology, results and insights from this research provide a preliminary framework to facilitate Thai retailers in optimizing their pricing strategies and achieving key business objectives. © The Author(s), under exclusive licence to Springer Nature Limited 2023.
Manlika Kiatthadasirikul, Paveena Chaovalitwongse, Naragain Phumchusri, Siravit Swangnop
Constraint Programming in Single Machine Scheduling for Minimizing Makespan with Multiple Constraints Journal Article
In: Engineering Journal, vol. 28, no. 11, pp. 81 – 97, 2024, (Cited by: 0; All Open Access, Gold Open Access, Green Open Access).
@article{Kiatthadasirikul202481,
title = {Constraint Programming in Single Machine Scheduling for Minimizing Makespan with Multiple Constraints},
author = {Manlika Kiatthadasirikul and Paveena Chaovalitwongse and Naragain Phumchusri and Siravit Swangnop},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211950460&doi=10.4186%2fej.2024.28.11.81&partnerID=40&md5=f790481bcdb8ce3f751e66de472af0c6},
doi = {10.4186/ej.2024.28.11.81},
year = {2024},
date = {2024-01-01},
journal = {Engineering Journal},
volume = {28},
number = {11},
pages = {81 – 97},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {This study focuses on developing a scheduling model for sequencing a set of jobs with different release times in a single machine to meet non-similar due dates as well as to reduce total sequence-dependent setup time. A constraint programming (CP) model is proposed to solve the scheduling problem by minimizing makespan under multiple constraints, namely release times, sequence-dependent setup time, and due dates. The proposed constraint programming model is tested and compared with the baseline method derived from as-is scheduling of alloy wheels manufactures. The computational experiments show the proposed constraint programming model outperforms the baseline method in the average improvement in makespan and total setup time. For small-size problems, the proposed scheduling model were optimally solved in a short time, achieving the best average improvement in makespan of 4.8826% and the best average improvement in total setup time of 45.7924%. Despite increasing problem sizes, the proposed scheduling model's computational time deteriorates but continues to provide the best solutions, achieving the best average improvement in makespan of 7.4891% and the best average improvement in total setup time of 55.4033%. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 0; All Open Access, Gold Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study focuses on developing a scheduling model for sequencing a set of jobs with different release times in a single machine to meet non-similar due dates as well as to reduce total sequence-dependent setup time. A constraint programming (CP) model is proposed to solve the scheduling problem by minimizing makespan under multiple constraints, namely release times, sequence-dependent setup time, and due dates. The proposed constraint programming model is tested and compared with the baseline method derived from as-is scheduling of alloy wheels manufactures. The computational experiments show the proposed constraint programming model outperforms the baseline method in the average improvement in makespan and total setup time. For small-size problems, the proposed scheduling model were optimally solved in a short time, achieving the best average improvement in makespan of 4.8826% and the best average improvement in total setup time of 45.7924%. Despite increasing problem sizes, the proposed scheduling model’s computational time deteriorates but continues to provide the best solutions, achieving the best average improvement in makespan of 7.4891% and the best average improvement in total setup time of 55.4033%. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.