@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},

issn = {01258281},

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: 0},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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},

issn = {01258281},

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},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Niamchuen20231371,

title = {Design of a Production Planning System in a Pharmaceutical Factory: Spreadsheet Model and Case Study},

author = {Paweethida Niamchuen and Paveena Chaovalitwongse and Punnamee Sachakamol},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158101380&doi=10.1007%2fs12247-023-09736-3&partnerID=40&md5=a6cf48fe5fce115f38fbe5f8d4abdc54},

doi = {10.1007/s12247-023-09736-3},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Pharmaceutical Innovation},

volume = {18},

number = {3},

pages = {1371 – 1380},

note = {Cited by: 0},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sukjamnong202355,

title = {A Hierarchical Analytic Process Framework for Manufacturing System Improvement},

author = {Jongkon Sukjamnong and Paveena Chaovalitwongse and Siravit Swangnop and Poom Luangjarmekorn},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174270053&doi=10.4186%2fej.2023.27.9.55&partnerID=40&md5=38b5d6bcafcd3977ef5e33fed549d9ae},

doi = {10.4186/ej.2023.27.9.55},

year  = {2023},

date = {2023-01-01},

journal = {Engineering Journal},

volume = {27},

number = {9},

pages = {55 – 69},

note = {Cited by: 0; All Open Access, Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chaovalitwongse2017433,

title = {A decision support model for staff allocation of mobile medical service},

author = {Paveena Chaovalitwongse and Krongsin Somprasonk and Naragain Phumchusri and Joseph Heim and Zelda B. Zabinsky and W. Art Chaovalitwongse},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940704057&doi=10.1007%2fs10479-015-1991-5&partnerID=40&md5=bd0507a4b6febe0357508fd405821c02},

doi = {10.1007/s10479-015-1991-5},

issn = {02545330},

year  = {2017},

date = {2017-01-01},

journal = {Annals of Operations Research},

volume = {249},

number = {1-2},

pages = {433 – 448},

publisher = {Springer New York LLC},

abstract = {Princess Mother’s Medical Volunteer (PMMV) Foundation is the most recognized and significant free-of-charge mobile medical service (MMS) provider in Thailand. They require volunteers from partner hospitals to give medical care to poor populations residing in remote areas of the country where access to general medical services is limited. Volunteers usually include four types of staff: doctors, dentists, nurses, and pharmacists. According to their operational plan, the PMMV and their working partners need to properly allocate/assign volunteer medical staff to operation sites according to site requirements. In current planning process, the PMMV has to organize massive amounts of data from different organizations in the country, resulting in a long processing time for allocation decisions. In addition, the current process does not allow decision makers to efficiently allocate medical staff with acceptable transportation cost. There is a significant opportunity to improve this process by using analytical models to support this decision making. Thus, this paper proposes a decision support model for staff allocation. The proposed model is in a form of computer information system (CIS) that is carefully developed to facilitate the access to heterogeneous data and ease of use by decision makers. The proposed CIS will assist the PMMV central offices and partners to manage massive data more efficiently and effectively, while the decision algorithm can facilitate planners to achieve the lowest possible cost associated with their decisions. The outcomes of this research were verified by potential users through a focus group manner. The result showed that the potential users were very satisfied with the overall performance of this system. © 2015, Springer Science+Business Media New York.},

note = {Cited by: 2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Puasakul201691,

title = {The review of mark planning problem},

author = {Kritsada Puasakul and Paveena Chaovalitwongse},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983470920&doi=10.4186%2fej.2016.20.3.91&partnerID=40&md5=32a2f0a74059bd3918ce9d67193d7712},

doi = {10.4186/ej.2016.20.3.91},

year  = {2016},

date = {2016-01-01},

journal = {Engineering Journal},

volume = {20},

number = {3},

pages = {91 – 112},

note = {Cited by: 2; All Open Access, Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kositphon201667,

title = {An application of systems thinking for solving inventory management problems},

author = {Siraprapa Kositphon and Paveena Chaovalitwongse},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84997417679&doi=10.4186%2fej.2016.20.5.67&partnerID=40&md5=bf7caf6983a372fa55469b83fcbb9360},

doi = {10.4186/ej.2016.20.5.67},

year  = {2016},

date = {2016-01-01},

journal = {Engineering Journal},

volume = {20},

number = {5},

pages = {67 – 88},

note = {Cited by: 3; All Open Access, Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Swangnop201551,

title = {Joint requirement of two multi-skill resource types in multi-period multi-site assignment problems},

author = {Siravit Swangnop and Paveena Chaovalitwongse},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922495985&doi=10.4186%2fej.2015.19.1.51&partnerID=40&md5=df155a71deb103660226b73e9a9099f6},

doi = {10.4186/ej.2015.19.1.51},

year  = {2015},

date = {2015-01-01},

journal = {Engineering Journal},

volume = {19},

number = {1},

pages = {51 – 65},

note = {Cited by: 2; All Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Swangnop201483,

title = {A two-step Tabu search heuristic for multi-period multi-site assignment problem with joint requirement of multiple resource types},

author = {Siravit Swangnop and Paveena Chaovalitwongse},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904354436&doi=10.4186%2fej.2014.18.3.83&partnerID=40&md5=c192fd4914af75230d0a81a31bc49ff6},

doi = {10.4186/ej.2014.18.3.83},

year  = {2014},

date = {2014-01-01},

journal = {Engineering Journal},

volume = {18},

number = {3},

pages = {83 – 97},

note = {Cited by: 4; All Open Access, Bronze Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}