Qiang Liu , Xiao Wang , Huashi Liu , Lei Meng , Chenxu Hao
{"title":"海上运输不确定性下电厂煤炭可持续采购优化研究","authors":"Qiang Liu , Xiao Wang , Huashi Liu , Lei Meng , Chenxu Hao","doi":"10.1016/j.clscn.2025.100204","DOIUrl":null,"url":null,"abstract":"<div><div>This study introduces a stochastic optimization model designed for enhancing coal procurement and logistics management at port-based coal-fired power plants. Addressing the dual pressures of economic efficiency and environmental sustainability, this model incorporates the complexities of maritime transportation uncertainties, varying demand, and stringent carbon emission controls. Through this framework, we effectively align procurement strategies with inventory management to optimize operations under dynamic conditions. The model’s distinctiveness lies in its integration of real-time maritime variables and adaptive responses to environmental policies, setting a new standard for green logistics in the energy sector. Key results demonstrate the effectiveness of mixed coal procurement strategies across different demand scenarios, highlighting significant savings in carbon emissions and operational costs. Our findings provide valuable insights for policymakers and industry stakeholders aiming to achieve sustainable development goals in the energy sector.</div></div>","PeriodicalId":100253,"journal":{"name":"Cleaner Logistics and Supply Chain","volume":"14 ","pages":"Article 100204"},"PeriodicalIF":6.8000,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of sustainable coal procurement for power plants under maritime transportation uncertainty\",\"authors\":\"Qiang Liu , Xiao Wang , Huashi Liu , Lei Meng , Chenxu Hao\",\"doi\":\"10.1016/j.clscn.2025.100204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study introduces a stochastic optimization model designed for enhancing coal procurement and logistics management at port-based coal-fired power plants. Addressing the dual pressures of economic efficiency and environmental sustainability, this model incorporates the complexities of maritime transportation uncertainties, varying demand, and stringent carbon emission controls. Through this framework, we effectively align procurement strategies with inventory management to optimize operations under dynamic conditions. The model’s distinctiveness lies in its integration of real-time maritime variables and adaptive responses to environmental policies, setting a new standard for green logistics in the energy sector. Key results demonstrate the effectiveness of mixed coal procurement strategies across different demand scenarios, highlighting significant savings in carbon emissions and operational costs. Our findings provide valuable insights for policymakers and industry stakeholders aiming to achieve sustainable development goals in the energy sector.</div></div>\",\"PeriodicalId\":100253,\"journal\":{\"name\":\"Cleaner Logistics and Supply Chain\",\"volume\":\"14 \",\"pages\":\"Article 100204\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2025-01-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cleaner Logistics and Supply Chain\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772390925000034\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cleaner Logistics and Supply Chain","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772390925000034","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
Optimization of sustainable coal procurement for power plants under maritime transportation uncertainty
This study introduces a stochastic optimization model designed for enhancing coal procurement and logistics management at port-based coal-fired power plants. Addressing the dual pressures of economic efficiency and environmental sustainability, this model incorporates the complexities of maritime transportation uncertainties, varying demand, and stringent carbon emission controls. Through this framework, we effectively align procurement strategies with inventory management to optimize operations under dynamic conditions. The model’s distinctiveness lies in its integration of real-time maritime variables and adaptive responses to environmental policies, setting a new standard for green logistics in the energy sector. Key results demonstrate the effectiveness of mixed coal procurement strategies across different demand scenarios, highlighting significant savings in carbon emissions and operational costs. Our findings provide valuable insights for policymakers and industry stakeholders aiming to achieve sustainable development goals in the energy sector.