An MILP framework for gas supply chain infrastructure planning with endogenous logistics schemes

20846 6 110945 2 335801 document 110945 1-s2.0-S2949908925002067-main.pdf application/pdf 9c25331be302343fff8a87f839374e8a MD5 8326230 2025-09-01 07:43:43 https://pure.iiasa.ac.at/id/eprint/20846/1/1-s2.0-S2949908925002067-main.pdf 20846 1 1 application/pdf text en public cc_by_4

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published 110946 2 335807 document 110946 lightbox.jpg image/png 99118268ce707b789239cfa93445808f MD5 56153 2025-09-01 07:53:47 https://pure.iiasa.ac.at/id/eprint/20846/2/lightbox.jpg 20846 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/110945 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/110945 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/110945 archive 353 disk0/00/02/08/46 2025-09-01 07:45:00 2025-09-01 07:45:00 2025-09-01 07:45:00 article show Pratama Y. 9258 0000-0002-7660-4666 Reyseliani N. Purwanto W.W. An MILP framework for gas supply chain infrastructure planning with endogenous logistics schemes pub ECE IACC Liquefied natural gas, Optimisation, Supply chain design, Mixed-integer linear programming 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. 2025-11 published Elsevier doi:10.1016/j.jgsce.2025.205742 3529 public Gas Science and Engineering 143 e205742 1 TRUE 29499089 info:eu-repo/semantics/article