{"id":5568,"date":"2024-10-02T18:20:12","date_gmt":"2024-10-02T18:20:12","guid":{"rendered":"https:\/\/ienext.eng.chula.ac.th\/?page_id=5568"},"modified":"2024-10-02T18:22:26","modified_gmt":"2024-10-02T18:22:26","slug":"naragain-phumchusri","status":"publish","type":"page","link":"https:\/\/ienext.eng.chula.ac.th\/?page_id=5568&lang=en","title":{"rendered":"Naragain Phumchusri"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"5568\" class=\"elementor elementor-5568\" data-elementor-post-type=\"page\">\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-5442381 e-flex e-con-boxed e-con e-parent\" data-id=\"5442381\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2df925d elementor-widget elementor-widget-html\" data-id=\"2df925d\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t<!-- Flexy Breadcrumb -->\r\n\t\t\t<div class=\"fbc fbc-page\">\r\n\r\n\t\t\t\t<!-- Breadcrumb wrapper -->\r\n\t\t\t\t<div class=\"fbc-wrap\">\r\n\r\n\t\t\t\t\t<!-- Ordered list-->\r\n\t\t\t\t\t<ol class=\"fbc-items\" itemscope itemtype=\"https:\/\/schema.org\/BreadcrumbList\">\r\n\t\t\t\t\t\t            <li itemprop=\"itemListElement\" itemscope itemtype=\"https:\/\/schema.org\/ListItem\">\r\n                <span itemprop=\"name\">\r\n                    <!-- Home Link -->\r\n                    <a itemprop=\"item\" href=\"https:\/\/ienext.eng.chula.ac.th\">\r\n                    \r\n                                                    <i class=\"fa fa-home\" aria-hidden=\"true\"><\/i>Home                    <\/a>\r\n                <\/span>\r\n                <meta itemprop=\"position\" content=\"1\" \/><!-- Meta Position-->\r\n             <\/li><li><span class=\"fbc-separator\">\/<\/span><\/li><li class=\"active\" itemprop=\"itemListElement\" itemscope itemtype=\"https:\/\/schema.org\/ListItem\"><span itemprop=\"name\" title=\"Naragain Phumchusri\">Naragain Phumchusri<\/span><meta itemprop=\"position\" content=\"2\" \/><\/li>\t\t\t\t\t<\/ol>\r\n\t\t\t\t\t<div class=\"clearfix\"><\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-76dc323 e-flex e-con-boxed e-con e-parent\" data-id=\"76dc323\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-492e764 e-con-full e-flex e-con e-child\" data-id=\"492e764\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1a880c1 elementor-widget elementor-widget-image\" data-id=\"1a880c1\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.22.0 - 26-06-2024 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"500\" height=\"498\" src=\"https:\/\/ienext.eng.chula.ac.th\/wp-content\/uploads\/2024\/04\/Naragain-e1713877982779.jpg\" class=\"attachment-full size-full wp-image-426\" alt=\"\" srcset=\"https:\/\/ienext.eng.chula.ac.th\/wp-content\/uploads\/2024\/04\/Naragain-e1713877982779.jpg 500w, https:\/\/ienext.eng.chula.ac.th\/wp-content\/uploads\/2024\/04\/Naragain-e1713877982779-300x300.jpg 300w, https:\/\/ienext.eng.chula.ac.th\/wp-content\/uploads\/2024\/04\/Naragain-e1713877982779-150x150.jpg 150w\" sizes=\"(max-width: 500px) 100vw, 500px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-fa5e16e e-con-full e-flex e-con e-child\" data-id=\"fa5e16e\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a7102db elementor-widget elementor-widget-heading\" data-id=\"a7102db\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.22.0 - 26-06-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">\t\t\t<span class=\"ctc-inline-copy \" aria-label=\"Copied\">\n\t\t\t\t<span class=\"ctc-inline-copy-text \">Assoc. Prof. Naragain Phumchusri, Ph.D.<\/span>\n\t\t\t\t<textarea style=\"display: none;\" class=\"ctc-inline-copy-textarea\" readonly=\"readonly\">Assoc. Prof. Naragain Phumchusri, Ph.D.<\/textarea>\n\t\t\t\t<span class=\"ctc-inline-copy-icon\" role=\"button\" aria-label=\"Copied\">\n\t\t\t\t\t<svg aria-hidden=\"true\" focusable=\"false\" role=\"img\" class=\"copy-icon\" viewBox=\"0 0 16 16\" width=\"16\" height=\"16\" fill=\"currentColor\"><path d=\"M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z\"><\/path><path d=\"M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z\"><\/path><\/svg>\t\t\t\t\t<svg aria-hidden=\"true\" height=\"16\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" data-view-component=\"true\" class=\"check-icon\" fill=\"currentColor\"><path d=\"M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 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data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-9396753 e-con-full e-flex elementor-invisible e-con e-parent\" data-id=\"9396753\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-82bd76a elementor-widget elementor-widget-text-editor\" data-id=\"82bd76a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.22.0 - 26-06-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<h4><strong><span class=\"ant-typography StyledText css-15ni9ka\">Overview <\/span><\/strong><\/h4>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-39c869e elementor-widget elementor-widget-text-editor\" data-id=\"39c869e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>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 &amp; supply chain management, data analysis for tourism industry and promotion optimization in retails.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-e90f508 e-flex e-con-boxed e-con e-parent\" data-id=\"e90f508\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-921b7f0 e-con-full e-flex elementor-invisible e-con e-parent\" data-id=\"921b7f0\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1bbfa79 elementor-widget elementor-widget-text-editor\" data-id=\"1bbfa79\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4><strong><span class=\"ant-typography StyledText css-15ni9ka\">Education<\/span><\/strong><\/h4>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e26a24 elementor-widget elementor-widget-text-editor\" data-id=\"3e26a24\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Ph.D. in Industrial Engineering<br \/>Georgia Institute of Technology, United States, 2010<\/p><p>Master of Science in Industrial Engineering<br \/>Georgia Institute of Technology, United States, 2006<\/p><p>B.Eng. in Industrial Engineering<br \/>Chulalongkorn University, Thailand, 2004<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-b43563c e-flex e-con-boxed e-con e-parent\" data-id=\"b43563c\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-2484244 e-con-full e-flex elementor-invisible e-con e-parent\" data-id=\"2484244\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-85ced04 elementor-widget elementor-widget-text-editor\" data-id=\"85ced04\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4><strong><span class=\"ant-typography StyledText css-15ni9ka\">Expertise<\/span><\/strong><\/h4>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7004c8 elementor-widget elementor-widget-text-editor\" data-id=\"a7004c8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span class=\"ant-typography StyledText css-1oc51h5\">Statistics &amp; Data Analysis<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-dd10e04 e-flex e-con-boxed e-con e-parent\" data-id=\"dd10e04\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-f3bff30 e-con-full e-flex elementor-invisible e-con e-parent\" data-id=\"f3bff30\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-70ddead elementor-widget elementor-widget-text-editor\" data-id=\"70ddead\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4><strong><span class=\"ant-typography StyledText css-15ni9ka\">Publications<br \/><\/span><\/strong><\/h4><p><div class=\"teachpress_pub_list\"><form name=\"tppublistform\" method=\"get\"><a name=\"tppubs\" id=\"tppubs\"><\/a><\/form><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">43 entries<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 of 5 <a href=\"https:\/\/ienext.eng.chula.ac.th\/?page_id=5568&#038;lang=en&amp;limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"next page\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/ienext.eng.chula.ac.th\/?page_id=5568&#038;lang=en&amp;limit=5&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"last page\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><div class=\"teachpress_publication_list\"><h3 class=\"tp_h3\" id=\"tp_h3_2026\">2026<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Yanvaroj Pongsethpaisal, Naragain Phumchusri, Paveena Chaovalitwongse<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('646','tp_links')\" style=\"cursor:pointer;\">Mutation-Augmented NEAT (M-NEAT): Improving Neuroevolutionary Performance in Divergent Multi-Echelon Inventory Systems<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Engineering Journal, <\/span><span class=\"tp_pub_additional_volume\">vol. 30, <\/span><span class=\"tp_pub_additional_number\">no. 4, <\/span><span class=\"tp_pub_additional_pages\">pp. 63 \u2013 79, <\/span><span class=\"tp_pub_additional_year\">2026<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 0)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_646\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('646','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_646\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('646','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_646\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Pongsethpaisal202663,<br \/>\r\ntitle = {Mutation-Augmented NEAT (M-NEAT): Improving Neuroevolutionary Performance in Divergent Multi-Echelon Inventory Systems},<br \/>\r\nauthor = {Yanvaroj Pongsethpaisal and Naragain Phumchusri and Paveena Chaovalitwongse},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105038697693&doi=10.4186%2fej.2026.30.4.63&partnerID=40&md5=5f0795494f1692175142df0fe80be8cd},<br \/>\r\ndoi = {10.4186\/ej.2026.30.4.63},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\njournal = {Engineering Journal},<br \/>\r\nvolume = {30},<br \/>\r\nnumber = {4},<br \/>\r\npages = {63 \u2013 79},<br \/>\r\nnote = {Cited by: 0},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('646','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_646\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105038697693&amp;doi=10.4186%2fej.2026.30.4.63&amp;partnerID=40&amp;md5=5f0795494f1692175142df0fe80be8cd\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105038697693&amp;doi=10.4186[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105038697693&amp;doi=10.4186[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.4186\/ej.2026.30.4.63\" title=\"Follow DOI:10.4186\/ej.2026.30.4.63\" target=\"_blank\">doi:10.4186\/ej.2026.30.4.63<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('646','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2025\">2025<\/h3><div class=\"tp_publication tp_publication_conference\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">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<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('645','tp_links')\" style=\"cursor:pointer;\">Preface<\/a> <span class=\"tp_pub_type tp_  conference\">Conference<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_year\">2025<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 0)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_645\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('645','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_645\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('645','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_645\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@conference{Gui2025,<br \/>\r\ntitle = {Preface},<br \/>\r\nauthor = {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},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105017650120&doi=10.1109%2fDMCIS65888.2025.11138398&partnerID=40&md5=650ae395469cc176701affc9370db465},<br \/>\r\ndoi = {10.1109\/DMCIS65888.2025.11138398},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\njournal = {2025 2nd International Conference on Digital Media, Communication and Information Systems, DMCIS 2025},<br \/>\r\nnote = {Cited by: 0},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {conference}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('645','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_645\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105017650120&amp;doi=10.1109%2fDMCIS65888.2025.11138398&amp;partnerID=40&amp;md5=650ae395469cc176701affc9370db465\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105017650120&amp;doi=10.1109[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105017650120&amp;doi=10.1109[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/DMCIS65888.2025.11138398\" title=\"Follow DOI:10.1109\/DMCIS65888.2025.11138398\" target=\"_blank\">doi:10.1109\/DMCIS65888.2025.11138398<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('645','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Chutima Binsriavanich, Naragain Phumchusri<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('600','tp_links')\" style=\"cursor:pointer;\">An analysis of retail promotional pricing effectiveness using agent-based modeling<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Journal of Revenue and Pricing Management, <\/span><span class=\"tp_pub_additional_volume\">vol. 24, <\/span><span class=\"tp_pub_additional_number\">no. 1, <\/span><span class=\"tp_pub_additional_pages\">pp. 60 \u2013 79, <\/span><span class=\"tp_pub_additional_year\">2025<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 2)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_600\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('600','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_600\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('600','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_600\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('600','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_600\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Binsriavanich202560,<br \/>\r\ntitle = {An analysis of retail promotional pricing effectiveness using agent-based modeling},<br \/>\r\nauthor = {Chutima Binsriavanich and Naragain Phumchusri},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85211935209&doi=10.1057%2fs41272-024-00512-7&partnerID=40&md5=fb87bdd84c73aeee7fa60475fb557f63},<br \/>\r\ndoi = {10.1057\/s41272-024-00512-7},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\njournal = {Journal of Revenue and Pricing Management},<br \/>\r\nvolume = {24},<br \/>\r\nnumber = {1},<br \/>\r\npages = {60 \u2013 79},<br \/>\r\npublisher = {Palgrave Macmillan},<br \/>\r\nabstract = {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. \u00a9 The Author(s), under exclusive licence to Springer Nature Limited 2024.},<br \/>\r\nnote = {Cited by: 2},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('600','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_600\" style=\"display:none;\"><div class=\"tp_abstract_entry\">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. \u00a9 The Author(s), under exclusive licence to Springer Nature Limited 2024.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('600','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_600\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85211935209&amp;doi=10.1057%2fs41272-024-00512-7&amp;partnerID=40&amp;md5=fb87bdd84c73aeee7fa60475fb557f63\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85211935209&amp;doi=10.1057%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85211935209&amp;doi=10.1057%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1057\/s41272-024-00512-7\" title=\"Follow DOI:10.1057\/s41272-024-00512-7\" target=\"_blank\">doi:10.1057\/s41272-024-00512-7<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('600','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Santi Wongkamphu, Naragain Phumchusri<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('601','tp_links')\" style=\"cursor:pointer;\">Hybrid Approaches to Machine Learning for Improved Battery Sales Forecasting: A Case Study in Thailand<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Engineering Journal, <\/span><span class=\"tp_pub_additional_volume\">vol. 29, <\/span><span class=\"tp_pub_additional_number\">no. 2, <\/span><span class=\"tp_pub_additional_pages\">pp. 27 \u2013 43, <\/span><span class=\"tp_pub_additional_year\">2025<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 5; All Open Access, Gold Open Access)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_601\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('601','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_601\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('601','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_601\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('601','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_601\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Wongkamphu202527,<br \/>\r\ntitle = {Hybrid Approaches to Machine Learning for Improved Battery Sales Forecasting: A Case Study in Thailand},<br \/>\r\nauthor = {Santi Wongkamphu and Naragain Phumchusri},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000148099&doi=10.4186%2fej.2025.29.2.27&partnerID=40&md5=ddfdc08c3e9395cf76d0a3106ac47ba8},<br \/>\r\ndoi = {10.4186\/ej.2025.29.2.27},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\njournal = {Engineering Journal},<br \/>\r\nvolume = {29},<br \/>\r\nnumber = {2},<br \/>\r\npages = {27 \u2013 43},<br \/>\r\npublisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},<br \/>\r\nabstract = {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\u2019s 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. \u00a9 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},<br \/>\r\nnote = {Cited by: 5; All Open Access, Gold Open Access},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('601','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_601\" style=\"display:none;\"><div class=\"tp_abstract_entry\">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\u2019s 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. \u00a9 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('601','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_601\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000148099&amp;doi=10.4186%2fej.2025.29.2.27&amp;partnerID=40&amp;md5=ddfdc08c3e9395cf76d0a3106ac47ba8\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000148099&amp;doi=10.4186[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000148099&amp;doi=10.4186[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.4186\/ej.2025.29.2.27\" title=\"Follow DOI:10.4186\/ej.2025.29.2.27\" target=\"_blank\">doi:10.4186\/ej.2025.29.2.27<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('601','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Yanvaroj Pongsethpaisal, Naragain Phumchusri, Paveena Chaovalitwongse<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('604','tp_links')\" style=\"cursor:pointer;\">Employing Neuroevolution of Augmenting Topologies (NEAT) in Linear Multi-Echelon Inventory Systems<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Engineering Journal, <\/span><span class=\"tp_pub_additional_volume\">vol. 29, <\/span><span class=\"tp_pub_additional_number\">no. 3, <\/span><span class=\"tp_pub_additional_pages\">pp. 11 \u2013 26, <\/span><span class=\"tp_pub_additional_year\">2025<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 1; All Open Access, Gold Open Access)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_604\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('604','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_604\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('604','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_604\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('604','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_604\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Pongsethpaisal202511,<br \/>\r\ntitle = {Employing Neuroevolution of Augmenting Topologies (NEAT) in Linear Multi-Echelon Inventory Systems},<br \/>\r\nauthor = {Yanvaroj Pongsethpaisal and Naragain Phumchusri and Paveena Chaovalitwongse},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105002468604&doi=10.4186%2fej.2025.29.3.11&partnerID=40&md5=d7799e5a5f4fdc7c974b4d5f64f797af},<br \/>\r\ndoi = {10.4186\/ej.2025.29.3.11},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\njournal = {Engineering Journal},<br \/>\r\nvolume = {29},<br \/>\r\nnumber = {3},<br \/>\r\npages = {11 \u2013 26},<br \/>\r\npublisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},<br \/>\r\nabstract = {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) \u2013 a hybrid of model-based reinforcement learning and evolutionary algorithms \u2013 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\u2019s 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\u2019s learning trajectory. \u00a9 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},<br \/>\r\nnote = {Cited by: 1; All Open Access, Gold Open Access},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('604','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_604\" style=\"display:none;\"><div class=\"tp_abstract_entry\">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) \u2013 a hybrid of model-based reinforcement learning and evolutionary algorithms \u2013 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\u2019s 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\u2019s learning trajectory. \u00a9 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('604','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_604\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105002468604&amp;doi=10.4186%2fej.2025.29.3.11&amp;partnerID=40&amp;md5=d7799e5a5f4fdc7c974b4d5f64f797af\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105002468604&amp;doi=10.4186[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105002468604&amp;doi=10.4186[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.4186\/ej.2025.29.3.11\" title=\"Follow DOI:10.4186\/ej.2025.29.3.11\" target=\"_blank\">doi:10.4186\/ej.2025.29.3.11<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('604','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_conference\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">P. Chavalpatanapan, P. Phlaingam, N. Phumchusri<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('647','tp_links')\" style=\"cursor:pointer;\">Dashboard Development and Aspect-Based Sentiment Analysis for a Case-study Tour Operator in Thailand<\/a> <span class=\"tp_pub_type tp_  conference\">Conference<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_year\">2025<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 0)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_647\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('647','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_647\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('647','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_647\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@conference{Chavalpatanapan2025420,<br \/>\r\ntitle = {Dashboard Development and Aspect-Based Sentiment Analysis for a Case-study Tour Operator in Thailand},<br \/>\r\nauthor = {P. Chavalpatanapan and P. Phlaingam and N. Phumchusri},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105033976097&doi=10.1109%2fIEEM63636.2025.11357761&partnerID=40&md5=44421a9fa392302f7a9c2f3a9c70f08c},<br \/>\r\ndoi = {10.1109\/IEEM63636.2025.11357761},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\njournal = {IEEE International Conference on Industrial Engineering and Engineering Management},<br \/>\r\npages = {420 \u2013 424},<br \/>\r\nnote = {Cited by: 0},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {conference}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('647','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_647\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105033976097&amp;doi=10.1109%2fIEEM63636.2025.11357761&amp;partnerID=40&amp;md5=44421a9fa392302f7a9c2f3a9c70f08c\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105033976097&amp;doi=10.1109[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105033976097&amp;doi=10.1109[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/IEEM63636.2025.11357761\" title=\"Follow DOI:10.1109\/IEEM63636.2025.11357761\" target=\"_blank\">doi:10.1109\/IEEM63636.2025.11357761<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('647','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2024\">2024<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Naragain Phumchusri, Nichakan Phupaichitkun<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('291','tp_links')\" style=\"cursor:pointer;\">Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Journal of Revenue and Pricing Management, <\/span><span class=\"tp_pub_additional_year\">2024<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 1)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_291\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('291','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_291\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('291','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_291\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Phumchusri2024,<br \/>\r\ntitle = {Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver},<br \/>\r\nauthor = {Naragain Phumchusri and Nichakan Phupaichitkun},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85189920531&doi=10.1057%2fs41272-024-00477-7&partnerID=40&md5=6daa0bdde1e4e129b142d701f3e1a5c2},<br \/>\r\ndoi = {10.1057\/s41272-024-00477-7},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Journal of Revenue and Pricing Management},<br \/>\r\nnote = {Cited by: 1},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('291','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_291\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85189920531&amp;doi=10.1057%2fs41272-024-00477-7&amp;partnerID=40&amp;md5=6daa0bdde1e4e129b142d701f3e1a5c2\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85189920531&amp;doi=10.1057%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85189920531&amp;doi=10.1057%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1057\/s41272-024-00477-7\" title=\"Follow DOI:10.1057\/s41272-024-00477-7\" target=\"_blank\">doi:10.1057\/s41272-024-00477-7<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('291','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Thanaporn Kusomrosananan, Naragain Phumchusri<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('599','tp_links')\" style=\"cursor:pointer;\">Inventory policy improvement with periodic review for perishable goods: A case study of a retail coffee shop in thailand<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Engineering Journal, <\/span><span class=\"tp_pub_additional_volume\">vol. 28, <\/span><span class=\"tp_pub_additional_number\">no. 6, <\/span><span class=\"tp_pub_additional_pages\">pp. 59 \u2013 73, <\/span><span class=\"tp_pub_additional_year\">2024<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 3; All Open Access, Gold Open Access, Green Open Access)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_599\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('599','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_599\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('599','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_599\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('599','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_599\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Kusomrosananan202459,<br \/>\r\ntitle = {Inventory policy improvement with periodic review for perishable goods: A case study of a retail coffee shop in thailand},<br \/>\r\nauthor = {Thanaporn Kusomrosananan and Naragain Phumchusri},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85205096590&doi=10.4186%2fej.2024.28.6.59&partnerID=40&md5=171ab74c9a64b60013212ec4fea2de34},<br \/>\r\ndoi = {10.4186\/ej.2024.28.6.59},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Engineering Journal},<br \/>\r\nvolume = {28},<br \/>\r\nnumber = {6},<br \/>\r\npages = {59 \u2013 73},<br \/>\r\npublisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},<br \/>\r\nabstract = {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. \u00a9 2024, Eng. J. All rights reserved.},<br \/>\r\nnote = {Cited by: 3; All Open Access, Gold Open Access, Green Open Access},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('599','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_599\" style=\"display:none;\"><div class=\"tp_abstract_entry\">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. \u00a9 2024, Eng. J. All rights reserved.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('599','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_599\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85205096590&amp;doi=10.4186%2fej.2024.28.6.59&amp;partnerID=40&amp;md5=171ab74c9a64b60013212ec4fea2de34\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85205096590&amp;doi=10.4186%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85205096590&amp;doi=10.4186%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.4186\/ej.2024.28.6.59\" title=\"Follow DOI:10.4186\/ej.2024.28.6.59\" target=\"_blank\">doi:10.4186\/ej.2024.28.6.59<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('599','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Naragain Phumchusri, Thiti Chewcharat, Supawish Kanokpongsakorn<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('271','tp_links')\" style=\"cursor:pointer;\">Price promotion optimization model for multiperiod planning: a case study of beauty category products sold in a convenience store chain<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Journal of Revenue and Pricing Management, <\/span><span class=\"tp_pub_additional_volume\">vol. 23, <\/span><span class=\"tp_pub_additional_number\">no. 2, <\/span><span class=\"tp_pub_additional_pages\">pp. 164 \u2013 178, <\/span><span class=\"tp_pub_additional_year\">2024<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 4)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_271\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('271','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_271\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('271','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_271\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('271','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_271\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Phumchusri2024164,<br \/>\r\ntitle = {Price promotion optimization model for multiperiod planning: a case study of beauty category products sold in a convenience store chain},<br \/>\r\nauthor = {Naragain Phumchusri and Thiti Chewcharat and Supawish Kanokpongsakorn},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85168125852&doi=10.1057%2fs41272-023-00438-6&partnerID=40&md5=00ab18dc167d2f437dfe5892f31f43d4},<br \/>\r\ndoi = {10.1057\/s41272-023-00438-6},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Journal of Revenue and Pricing Management},<br \/>\r\nvolume = {23},<br \/>\r\nnumber = {2},<br \/>\r\npages = {164 \u2013 178},<br \/>\r\npublisher = {Palgrave Macmillan},<br \/>\r\nabstract = {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\u00a0is 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. \u00a9 The Author(s), under exclusive licence to Springer Nature Limited 2023.},<br \/>\r\nnote = {Cited by: 4},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('271','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_271\" style=\"display:none;\"><div class=\"tp_abstract_entry\">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\u00a0is 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. \u00a9 The Author(s), under exclusive licence to Springer Nature Limited 2023.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('271','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_271\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85168125852&amp;doi=10.1057%2fs41272-023-00438-6&amp;partnerID=40&amp;md5=00ab18dc167d2f437dfe5892f31f43d4\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85168125852&amp;doi=10.1057%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85168125852&amp;doi=10.1057%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1057\/s41272-023-00438-6\" title=\"Follow DOI:10.1057\/s41272-023-00438-6\" target=\"_blank\">doi:10.1057\/s41272-023-00438-6<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('271','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\">Manlika Kiatthadasirikul, Paveena Chaovalitwongse, Naragain Phumchusri, Siravit Swangnop<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('602','tp_links')\" style=\"cursor:pointer;\">Constraint Programming in Single Machine Scheduling for Minimizing Makespan with Multiple Constraints<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Engineering Journal, <\/span><span class=\"tp_pub_additional_volume\">vol. 28, <\/span><span class=\"tp_pub_additional_number\">no. 11, <\/span><span class=\"tp_pub_additional_pages\">pp. 81 \u2013 97, <\/span><span class=\"tp_pub_additional_year\">2024<\/span><span class=\"tp_pub_additional_note\">, (Cited by: 0; All Open Access, Gold Open Access, Green Open Access)<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_602\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('602','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_602\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('602','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_602\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('602','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_602\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Kiatthadasirikul202481,<br \/>\r\ntitle = {Constraint Programming in Single Machine Scheduling for Minimizing Makespan with Multiple Constraints},<br \/>\r\nauthor = {Manlika Kiatthadasirikul and Paveena Chaovalitwongse and Naragain Phumchusri and Siravit Swangnop},<br \/>\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85211950460&doi=10.4186%2fej.2024.28.11.81&partnerID=40&md5=f790481bcdb8ce3f751e66de472af0c6},<br \/>\r\ndoi = {10.4186\/ej.2024.28.11.81},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Engineering Journal},<br \/>\r\nvolume = {28},<br \/>\r\nnumber = {11},<br \/>\r\npages = {81 \u2013 97},<br \/>\r\npublisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},<br \/>\r\nabstract = {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%. \u00a9 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},<br \/>\r\nnote = {Cited by: 0; All Open Access, Gold Open Access, Green Open Access},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('602','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_602\" style=\"display:none;\"><div class=\"tp_abstract_entry\">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&#8217;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%. \u00a9 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('602','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_602\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85211950460&amp;doi=10.4186%2fej.2024.28.11.81&amp;partnerID=40&amp;md5=f790481bcdb8ce3f751e66de472af0c6\" title=\"https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85211950460&amp;doi=10.4186%[...]\" target=\"_blank\">https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85211950460&amp;doi=10.4186%[&#8230;]<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.4186\/ej.2024.28.11.81\" title=\"Follow DOI:10.4186\/ej.2024.28.11.81\" target=\"_blank\">doi:10.4186\/ej.2024.28.11.81<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a 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