{"title":"具有内生物流方案的天然气供应链基础设施规划的MILP框架","authors":"Yoga Wienda Pratama , Nadhilah Reyseliani , Widodo Wahyu Purwanto","doi":"10.1016/j.jgsce.2025.205742","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a mixed-integer linear programming (MILP) framework to minimise the costs of gas supply chains. Distinct from existing approaches in the literature, which often rely on pre-defined logistics schemes and treat storage sizing at receiving terminals in isolation, this framework integrates these into a single optimisation model. By setting these elements as decision variables, the framework allows for simultaneous optimisation of shipping strategies and receiving terminals design. Here, the liquefied natural gas (LNG) supply chain in Indonesia's Maluku Islands was used as a case study. Additionally, the framework was applied to the Finnish coastline and the Caribbean Islands, which differ substantially in terms of demand levels, distances between locations, and geographical contexts, to demonstrate its applicability to problems with differing characteristics. The results show that clustering demands to increase project sizes can lead to significant cost reductions. However, the marginal gains of these economies of scale diminish rapidly as project size grows, especially with longer shipping distances. Finally, the proposed framework was also shown to provide substantially lower-cost solutions compared to methods that rely on pre-determined shipping strategies or optimise shipping and storage capacities separately.</div></div>","PeriodicalId":100568,"journal":{"name":"Gas Science and Engineering","volume":"143 ","pages":"Article 205742"},"PeriodicalIF":5.5000,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An MILP framework for gas supply chain infrastructure planning with endogenous logistics schemes\",\"authors\":\"Yoga Wienda Pratama , Nadhilah Reyseliani , Widodo Wahyu Purwanto\",\"doi\":\"10.1016/j.jgsce.2025.205742\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper presents a mixed-integer linear programming (MILP) framework to minimise the costs of gas supply chains. Distinct from existing approaches in the literature, which often rely on pre-defined logistics schemes and treat storage sizing at receiving terminals in isolation, this framework integrates these into a single optimisation model. By setting these elements as decision variables, the framework allows for simultaneous optimisation of shipping strategies and receiving terminals design. Here, the liquefied natural gas (LNG) supply chain in Indonesia's Maluku Islands was used as a case study. Additionally, the framework was applied to the Finnish coastline and the Caribbean Islands, which differ substantially in terms of demand levels, distances between locations, and geographical contexts, to demonstrate its applicability to problems with differing characteristics. The results show that clustering demands to increase project sizes can lead to significant cost reductions. However, the marginal gains of these economies of scale diminish rapidly as project size grows, especially with longer shipping distances. Finally, the proposed framework was also shown to provide substantially lower-cost solutions compared to methods that rely on pre-determined shipping strategies or optimise shipping and storage capacities separately.</div></div>\",\"PeriodicalId\":100568,\"journal\":{\"name\":\"Gas Science and Engineering\",\"volume\":\"143 \",\"pages\":\"Article 205742\"},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2025-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Gas Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949908925002067\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gas Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949908925002067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
An MILP framework for gas supply chain infrastructure planning with endogenous logistics schemes
This paper presents a mixed-integer linear programming (MILP) framework to minimise the costs of gas supply chains. Distinct from existing approaches in the literature, which often rely on pre-defined logistics schemes and treat storage sizing at receiving terminals in isolation, this framework integrates these into a single optimisation model. By setting these elements as decision variables, the framework allows for simultaneous optimisation of shipping strategies and receiving terminals design. Here, the liquefied natural gas (LNG) supply chain in Indonesia's Maluku Islands was used as a case study. Additionally, the framework was applied to the Finnish coastline and the Caribbean Islands, which differ substantially in terms of demand levels, distances between locations, and geographical contexts, to demonstrate its applicability to problems with differing characteristics. The results show that clustering demands to increase project sizes can lead to significant cost reductions. However, the marginal gains of these economies of scale diminish rapidly as project size grows, especially with longer shipping distances. Finally, the proposed framework was also shown to provide substantially lower-cost solutions compared to methods that rely on pre-determined shipping strategies or optimise shipping and storage capacities separately.