Evaluation of Loading Bay Restrictions for the Installation of Offshore Wind Farms Using a Combination of Mixed-Integer Linear Programming and Model Predictive Control

The installation of offshore wind farms poses particular challenges due to expensive resources and quickly changing weather conditions. Model-based decision-support systems are required to achieve an efficient installation. In the literature, there exist several models for scheduling offshore operations, which focus on vessels but neglect the influence of resource restrictions at the base port and uncertainties involved with weather predictions. This article proposes a Mixed-Integer Linear Programming model for the scheduling of installation activities, which handles several installation vessels as well as restrictions about available cargo bridges at the port. Additionally, the article explains how this model can be combined with a Model Predictive Control scheme to provide decision support for the scheduling of offshore installation operations. The article presents numerical studies of the effects induced by resource restrictions and of different parametrizations for this approach. Results show that even small planning windows, paired with comparably low computational times, achieve reasonably good results. Moreover, the results show that an increase in vessels comes at diminishing returns concerning the installation efficiency. Therefore, the results indicate that available good-weather windows primarily limit efficiency.

[1]  Helena Szczerbicka,et al.  Simulation and Optimization of Operations for Offshore Installations Planning Using a Model Predictive Control Scheme , 2019, 2019 Winter Simulation Conference (WSC).

[2]  Marianthi G. Ierapetritou,et al.  Integration of scheduling and control under uncertainties: Review and challenges , 2016 .

[3]  Jürgen Pannek,et al.  Nonlinear Model Predictive Control : Theory and Algorithms. 2nd Edition , 2017 .

[4]  L. B. Savenije,et al.  Commercial Proof of Innovative Offshore Wind Installation Concepts using ECN Install Tool , 2015 .

[5]  Willy Herroelen,et al.  Robust and reactive project scheduling: a review and classification of procedures , 2004 .

[6]  Sanja Petrovic,et al.  SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS , 2006 .

[7]  Dorota Warżołek,et al.  The classification of scheduling problems under production uncertainty , 2014 .

[8]  Jian Zhang,et al.  Review of job shop scheduling research and its new perspectives under Industry 4.0 , 2017, Journal of Intelligent Manufacturing.

[9]  K. Thoben,et al.  A Study of New Installation Concepts of Offshore Wind Farms by Means of Simulation Model , 2017 .

[10]  Minh Quan Duong,et al.  Determination of Optimal Location and Sizing of Solar Photovoltaic Distribution Generation Units in Radial Distribution Systems , 2019, Energies.

[11]  Geir Moe,et al.  Status, plans and technologies for offshore wind turbines in Europe and North America , 2009 .

[12]  M. Lütjen,et al.  Simulation-based aggregate Installation Planning of Offshore Wind Farms , 2022 .

[13]  Erik Blasch,et al.  Handbook of Dynamic Data Driven Applications Systems , 2018, Springer International Publishing.

[14]  Michael Lütjen,et al.  Planning and control of logistics for offshore wind farms , 2010 .

[15]  Chandra Ade Irawan,et al.  Bi-objective optimisation model for installation scheduling in offshore wind farms , 2017, Comput. Oper. Res..

[16]  Abderrahim Ait-Alla,et al.  Simulation Based Investigation of the Impact of Information Sharing on the Offshore Wind Farm Installation Process , 2017 .

[17]  Hans-Dietrich Haasis,et al.  Planning Maritime Logistics Concepts for Offshore Wind Farms: A Newly Developed Decision Support System , 2012, ICCL.

[18]  Yohannes Tekle Muhabie,et al.  A discrete-event simulation approach to evaluate the effect of stochastic parameters on offshore wind farms assembly strategies , 2018 .

[19]  Öncü Hazır A review of analytical models, approaches and decision support tools in project monitoring and control , 2015 .

[20]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[21]  Wim Turkenburg,et al.  Cost Reduction Prospects for Offshore Wind Farms , 2004 .

[22]  Bing Dong,et al.  Occupancy behavior based model predictive control for building indoor climate—A critical review , 2016 .

[23]  Salwani Abdullah,et al.  Job Shop Scheduling: Classification, Constraints and Objective Functions , 2017 .

[24]  Evrim Ursavas,et al.  A benders decomposition approach for solving the offshore wind farm installation planning at the North Sea , 2017, Eur. J. Oper. Res..

[25]  K. Thoben,et al.  A Simulation Study of Feeder-Based Installation Concepts for Offshore Wind Farms , 2018 .

[26]  Louis-Philippe Kerkhove,et al.  Optimised scheduling for weather sensitive offshore construction projects , 2016 .

[27]  Iris F. A. Vis,et al.  Assessment approaches to logistics for offshore wind energy installation , 2016 .

[28]  Helena Szczerbicka,et al.  A Review on the Planning Problem for the Installation of Offshore Wind Farms , 2019, IFAC-PapersOnLine.

[29]  Abid Ali Khan,et al.  A research survey: review of flexible job shop scheduling techniques , 2016, Int. Trans. Oper. Res..

[30]  Jürgen Pannek,et al.  Numeric Evaluation of Game-Theoretic Collaboration Modes in Supplier Development , 2019, Applied Sciences.

[31]  Chandra Ade Irawan,et al.  An optimisation model for scheduling the decommissioning of an offshore wind farm , 2019, OR Spectr..

[32]  Yunus Demir,et al.  Evaluation of mathematical models for flexible job-shop scheduling problems , 2013 .

[33]  Boris V. Sokolov,et al.  Applicability of optimal control theory to adaptive supply chain planning and scheduling , 2012, Annu. Rev. Control..

[34]  Xiaojing Sun,et al.  The current state of offshore wind energy technology development , 2012 .

[35]  Yanjun Huang,et al.  Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints , 2017, IEEE Transactions on Vehicular Technology.