{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:45:43Z","timestamp":1723016743290},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>As a fundamental problem in Operations Research, sparse process flexibility design (SPFD) aims to design a manufacturing network across industries that achieves a trade-off between the efficiency and robustness of supply chains.\n\nIn this study, we propose a novel solution to this problem with the help of computational optimal transport techniques.\n\nGiven a set of supply-demand pairs, we formulate the SPFD task approximately as a group sparse optimal transport (GSOT) problem, in which a group of couplings between the supplies and demands is optimized with a group sparse regularizer.\n\nWe solve this optimization problem via an algorithmic framework of alternating direction method of multipliers (ADMM), in which the target network topology is updated by soft-thresholding shrinkage, and the couplings of the OT problems are updated via a smooth OT algorithm in parallel.\n\nThis optimization algorithm has guaranteed convergence and provides a generalized framework for the SPFD task, which is applicable regardless of whether the supplies and demands are balanced.\n\nExperiments show that our GSOT-based method can outperform representative heuristic methods in various SPFD tasks.\n\nAdditionally, when implementing the GSOT method, the proposed ADMM-based optimization algorithm is comparable or superior to the commercial software Gurobi.\n\nThe code is available at https:\/\/github.com\/Dixin-s-Lab\/GSOT.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/679","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"6121-6129","source":"Crossref","is-referenced-by-count":1,"title":["Group Sparse Optimal Transport for Sparse Process Flexibility Design"],"prefix":"10.24963","author":[{"given":"Dixin","family":"Luo","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology"}]},{"given":"Tingting","family":"Yu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology"}]},{"given":"Hongteng","family":"Xu","sequence":"additional","affiliation":[{"name":"Renmin University of China"},{"name":"Beijing Key Laboratory of Big Data Management and Analysis Methods"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:53:41Z","timestamp":1691744021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/679"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/679","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}