{"title":"Service-oriented operational decision optimization for dry bulk shipping fleet under stochastic demand","authors":"","doi":"10.1007/s11081-024-09884-6","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Dry bulk shipping plays a crucial role in intercontinental bulk cargo transport, with operators managing fleets to meet shippers’ transportation demand. A primary challenge for these operators is making optimal operational decisions about ship scheduling, routing, and sailing speed in the face of stochastic demand. We address this problem by developing a stochastic integer programming model designed to maximize revenue while maintaining high service levels for shippers. We quantify service levels for shippers using the probability of demand being fully satisfied. To solve this model, we introduce an innovative offline–online Lagrange relaxation framework. This framework leverages training data to determine the optimal Lagrange multiplier, which subsequently guides decision-making with test data. Numerical experiments show that our method closely matches the performance of Sampling Average Approximation (SAA) solutions while reducing computational time.</p>","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optimization and Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11081-024-09884-6","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Dry bulk shipping plays a crucial role in intercontinental bulk cargo transport, with operators managing fleets to meet shippers’ transportation demand. A primary challenge for these operators is making optimal operational decisions about ship scheduling, routing, and sailing speed in the face of stochastic demand. We address this problem by developing a stochastic integer programming model designed to maximize revenue while maintaining high service levels for shippers. We quantify service levels for shippers using the probability of demand being fully satisfied. To solve this model, we introduce an innovative offline–online Lagrange relaxation framework. This framework leverages training data to determine the optimal Lagrange multiplier, which subsequently guides decision-making with test data. Numerical experiments show that our method closely matches the performance of Sampling Average Approximation (SAA) solutions while reducing computational time.
期刊介绍：
Optimization and Engineering is a multidisciplinary journal; its primary goal is to promote the application of optimization methods in the general area of engineering sciences. We expect submissions to OPTE not only to make a significant optimization contribution but also to impact a specific engineering application.
Topics of Interest:
-Optimization: All methods and algorithms of mathematical optimization, including blackbox and derivative-free optimization, continuous optimization, discrete optimization, global optimization, linear and conic optimization, multiobjective optimization, PDE-constrained optimization & control, and stochastic optimization. Numerical and implementation issues, optimization software, benchmarking, and case studies.
-Engineering Sciences: Aerospace engineering, biomedical engineering, chemical & process engineering, civil, environmental, & architectural engineering, electrical engineering, financial engineering, geosciences, healthcare engineering, industrial & systems engineering, mechanical engineering & MDO, and robotics.
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