{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:34:01Z","timestamp":1744202041944,"version":"3.37.3"},"reference-count":44,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","funder":[{"DOI":"10.13039\/501100014219","name":"National Science Fund for Distinguished Young Scholars","doi-asserted-by":"crossref","award":["71025005"],"award-info":[{"award-number":["71025005"]}],"id":[{"id":"10.13039\/501100014219","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71301006"],"award-info":[{"award-number":["71301006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91224001"],"award-info":[{"award-number":["91224001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Program for Support of Top-Notch Young Professionals"},{"name":"the Fundamental Research Funds for the Central Universities in BUCT"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Info. Tech. Dec. Mak."],"published-print":{"date-parts":[[2016,3]]},"abstract":"<jats:p> Due to the uncertainty in oil markets, this paper proposes a novel approach for oil purchasing and distribution optimization by incorporating price and demand prediction, i.e., the prediction-based oil purchasing-and-distribution optimization model. In particular, the proposed method bridges the latest information technology and decision-making technique by introducing the recently proposed information technology (i.e., extreme learning machine (ELM)) into the oil purchasing-and-distribution optimization model. Two main steps are involved: market prediction and planning optimization in the proposed model. In market prediction, the ELM technique is employed to provide fast training time and accurate forecasting results for oil prices and demands. In planning optimization, two objectives of general profit maximization and inventory risk minimization are considered; and the most popular multi-objective evolutionary algorithm (MOEA), nondominated sorting genetic algorithm II (NSGA-II), is implemented to search approximate Pareto optimal solutions. For illustration and verification, the motor gasoline market in the US is focused on as the study sample, and the experimental results demonstrate the superiority of the proposed prediction-based optimization approach over its benchmark models (without market prediction and\/or planning optimization), in terms of the highest profit and the lowest risk. <\/jats:p>","DOI":"10.1142\/s0219622016500097","type":"journal-article","created":{"date-parts":[[2016,1,21]],"date-time":"2016-01-21T07:51:08Z","timestamp":1453362668000},"page":"423-451","source":"Crossref","is-referenced-by-count":20,"title":["Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm"],"prefix":"10.1142","volume":"15","author":[{"given":"Lean","family":"Yu","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, P. R. 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