@article{Jarumaneeroj2025,

title = {Eco-friendly long-haul perishable product transportation with multi-compartment vehicles},

author = {Pisit Jarumaneeroj and Supisara Krairiksh and Puwadol Oak Dusadeerungsikul and Dong Li and Çağatay Iris},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217719766&doi=10.1016%2fj.cie.2025.110934&partnerID=40&md5=c69a7e194c2581a7efb0eaa0834acf4f},

doi = {10.1016/j.cie.2025.110934},

issn = {03608352},

year  = {2025},

date = {2025-01-01},

journal = {Computers and Industrial Engineering},

volume = {202},

publisher = {Elsevier Ltd},

abstract = {Multi-compartment refrigerated vehicles (MCVs) have been recently utilized in long-haul perishable product transportation, thanks to their flexibility in storage capacity with different temperature settings. To better understand trade-offs between economic and environmental aspects of long-haul transportation of perishable products with refrigerated vehicles, a Multi-Compartment Vehicle Loading and Scheduling Problem (MCVLSP) that minimizes three objectives—transportation cost, carbon emissions, and total food loss—is herein solved by mathematical modeling and genetic algorithm (GA) approaches. Our computational results indicate that larger MCVLSP instances cannot be solved to optimality using the mathematical model with off-the-shelf optimization software packages. The proposed GA delivers strong computational performance for MCVLSP with respect to solution quality and computational time. We find that, among three objectives, the environmental objective is the most sensitive one as slight difference in either vehicle loading or scheduling decisions could result in solutions with significantly varying carbon emissions. Moreover, solutions with fewer MCVs are not necessarily environmentally sustainable. Rather, deploying larger MCV fleets could potentially result in lower carbon emissions and food weight loss for perishable products—albeit a slight increase in total transportation cost—due to the changes in vehicle loading and scheduling decisions. © 2025 The Author(s)},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dusadeerungsikul2024,

title = {A Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling},

author = {P. O. Dusadeerungsikul and S. Y. Nof},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200259818&doi=10.15837%2fIJCCC.2024.4.6686&partnerID=40&md5=19daa3a6813dd9fa9a7d897b51e830f9},

doi = {10.15837/IJCCC.2024.4.6686},

year  = {2024},

date = {2024-01-01},

journal = {International Journal of Computers, Communications and Control},

volume = {19},

number = {4},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dusadeerungsikul20236552,

title = {Cyber collaborative warehouse with dual-cycle operations design},

author = {Puwadol Oak Dusadeerungsikul and Shimon Y. Nof},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141096732&doi=10.1080%2f00207543.2022.2132313&partnerID=40&md5=775348034638cc51222b8e3774ed09d4},

doi = {10.1080/00207543.2022.2132313},

year  = {2023},

date = {2023-01-01},

journal = {International Journal of Production Research},

volume = {61},

number = {19},

pages = {6552 – 6564},

note = {Cited by: 0},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dusadeerungsikul20231481,

title = {Automation Statistics},

author = {Puwadol Oak Dusadeerungsikul and Win P. V. Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163048235&doi=10.1007%2f978-3-030-96729-1_74&partnerID=40&md5=fbb5f3e6c8f4a64d6f4d8002b134ae7e},

doi = {10.1007/978-3-030-96729-1_74},

year  = {2023},

date = {2023-01-01},

journal = {Springer Handbooks},

volume = {Part F674},

pages = {1481 – 1501},

note = {Cited by: 0},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nguyen2022723,

title = {Plant stress propagation detection and monitoring with disruption propagation network modelling and Bayesian network inference},

author = {Win P. V. Nguyen and Puwadol Oak Dusadeerungsikul and Shimon Y. Nof},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121432455&doi=10.1080%2f00207543.2021.2009139&partnerID=40&md5=3d76c1959717576876e1f3f256dbef5c},

doi = {10.1080/00207543.2021.2009139},

year  = {2022},

date = {2022-01-01},

journal = {International Journal of Production Research},

volume = {60},

number = {2},

pages = {723 – 741},

note = {Cited by: 3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nguyen202243,

title = {Collaborative Control, Task Administration, and Fault Tolerance for Supply Chain Network-Dynamics},

author = {Win P. V. Nguyen and Puwadol Oak Dusadeerungsikul and Shimon Y. Nof},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139819636&doi=10.1007%2f978-3-031-09179-7_3&partnerID=40&md5=5cb7ce64d9ed8c9c435e6fe6384845f7},

doi = {10.1007/978-3-031-09179-7_3},

year  = {2022},

date = {2022-01-01},

journal = {Springer Series in Supply Chain Management},

volume = {20},

pages = {43 – 78},

note = {Cited by: 1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dusadeerungsikul20226072,

title = {Multi-agent system optimisation in factories of the future: cyber collaborative warehouse study},

author = {Puwadol Oak Dusadeerungsikul and Xiang He and Maitreya Sreeram and Shimon Y. Nof},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116906691&doi=10.1080%2f00207543.2021.1979680&partnerID=40&md5=0c679dce43c4535264cfb8eb95c2f7aa},

doi = {10.1080/00207543.2021.1979680},

year  = {2022},

date = {2022-01-01},

journal = {International Journal of Production Research},

volume = {60},

number = {20},

pages = {6072 – 6086},

note = {Cited by: 8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jarumaneeroj2022,

title = {An epidemiology-based model for the operational allocation of COVID-19 vaccines: A case study of Thailand},

author = {Pisit Jarumaneeroj and Puwadol Oak Dusadeerungsikul and Tharin Chotivanich and Tanawin Nopsopon and Krit Pongpirul},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125255877&doi=10.1016%2fj.cie.2022.108031&partnerID=40&md5=c7d2cce8059203f8391287bd20603765},

doi = {10.1016/j.cie.2022.108031},

issn = {03608352},

year  = {2022},

date = {2022-01-01},

journal = {Computers and Industrial Engineering},

volume = {167},

publisher = {Elsevier Ltd},

abstract = {This paper addresses a framework for the operational allocation and administration of COVID-19 vaccines in Thailand, based on both COVID-19 transmission dynamics and other vital operational restrictions that might affect the effectiveness of vaccination strategies in the early stage of vaccine rollout. In this framework, the SIQRV model is first developed and later combined with the COVID-19 Vaccine Allocation Problem (CVAP) to determine the optimal allocation/administration strategies that minimize total weighted strain on the whole healthcare system. According to Thailand's second pandemic wave data (17th January 2021, to 15th February 2021), we find that the epicenter-based strategy is surprisingly the worst allocation strategy, due largely to the negligence of provincial demographics, vaccine efficacy, and overall transmission dynamics that lead to higher number of infectious individuals. We also find that early vaccination seems to significantly contribute to the reduction in the number of infectious individuals, whose effects tend to increase with more vaccine supply. With these insights, healthcare policy-makers should therefore focus not only on the procurement of COVID-19 vaccines at strategic levels but also on the allocation and administration of such vaccines at operational levels for the best of their limited vaccine supply. © 2022 Elsevier Ltd},

note = {Cited by: 23; All Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}