Data-Driven at Sea: Forecasting and Revenue Management at Molslinjen

被引:0
作者
Pinson, Pierre [1 ,2 ,3 ]
Bjorn, Mikkel [1 ]
Kristiansen, Simon [1 ]
Nielsen, Claus B. [1 ]
Janerka, Lasse [4 ]
Skovgaard, Jesper [4 ]
Durhuus, Kristian [4 ]
机构
[1] Halfspace, DK-1306 Copenhagen, Denmark
[2] Imperial Coll London, Dyson Sch Design Engn, London SW7 2AZ, England
[3] Tech Univ Denmark, Dept Technol Management & Econ, DK-2800 Lyngby, Denmark
[4] Molslinjen, DK-8000 Aarhus, Denmark
来源
INFORMS JOURNAL ON APPLIED ANALYTICS | 2025年 / 55卷 / 01期
关键词
ferry operations; demand forecasting; revenue management; machine learning; Franz Edelman award; OPTIMIZATION;
D O I
10.1287/inte.2024.0177
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Molslinjen, one of the world's largest operators of fast-moving catamaran ferries, based in Denmark, adopted a focus on digitalization to profoundly change its operations and business practices. Molslinjen partnered with Halfspace, a data, analytics, and artificial intelligence (AI) company based in Copenhagen, Denmark, to support that transition. Half- space and Molslinjen jointly developed and deployed a successful forecasting and revenue management toolbox for the data-driven operation of ferries in Denmark since 2020. This has resulted in $2.6-3.2 million yearly savings (and a total of $5 million savings as of December 2023), a significant reduction in the number of delayed departures and average delays, and a 3% reduction in fuel costs and emissions. This toolbox relies on some of the latest advances in machine learning for forecasting and in analytics approaches to revenue management. The potential for generalizing our toolbox to the global ferry industry is significant, with an impact on both revenues and environmental, societal, and governance criteria.
引用
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页数:18
相关论文
共 22 条
[1]  
[Anonymous], 1987, Air travel demand and airline seat inventory management
[2]  
Belobaba P P., 1992, P AGIFORS RESERVATIO
[3]   APPLICATION OF A PROBABILISTIC DECISION-MODEL TO AIRLINE SEAT INVENTORY CONTROL [J].
BELOBABA, PP .
OPERATIONS RESEARCH, 1989, 37 (02) :183-197
[4]   Kaggle forecasting competitions: An overlooked learning opportunity [J].
Bojer, Casper Solheim ;
Meldgaard, Jens Peder .
INTERNATIONAL JOURNAL OF FORECASTING, 2021, 37 (02) :587-603
[5]   XGBoost: A Scalable Tree Boosting System [J].
Chen, Tianqi ;
Guestrin, Carlos .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794
[6]   Liner shipping network design [J].
Christiansen, Marielle ;
Hellsten, Erik ;
Pisinger, David ;
Sacramento, David ;
Vilhelmsen, Charlotte .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2020, 286 (01) :1-20
[7]  
den Boer AV., 2015, SURVEYS OPER RES MAN, V20, P1, DOI DOI 10.1016/J.SORMS.2015.03.001
[8]   Dynamic pricing of airline offers [J].
Fiig, Thomas ;
Le Guen, Remy ;
Gauchet, Mathilde .
JOURNAL OF REVENUE AND PRICING MANAGEMENT, 2018, 17 (06) :381-393
[9]   Optimization of mixed fare structures: Theory and applications [J].
Fiig, Thomas ;
Isler, Karl ;
Hopperstad, Craig ;
Belobaba, Peter .
JOURNAL OF REVENUE AND PRICING MANAGEMENT, 2010, 9 (1-2) :152-170
[10]   Greedy function approximation: A gradient boosting machine [J].
Friedman, JH .
ANNALS OF STATISTICS, 2001, 29 (05) :1189-1232