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The Royal Canadian Navy Defence Research and Recherche et de´veloppement Development Canada pour la de´fense Canada CAN UNCLASSIFIED The Royal Canadian Navy (RCN) Intelligence, Surveillance, Target Acquisition and Reconnaissance Unmanned Aircraft System: schedule deconficting and time-on-station analysis Trevor Phair Michèle Fee Jean-Denis Caron Maritime Operational Research Team (MORT) DRDC – Centre for Operational Research and Analysis Terms of Release: This document is approved for public release. Defence Research and Development Canada Scientifc Report DRDC-RDDC-2021-R031 February 2021 CAN UNCLASSIFIED CAN UNCLASSIFIED IMPORTANT INFORMATIVE STATEMENTS This document was reviewed for Controlled Goods by DRDC using the Schedule to the Defence Production Act. Disclaimer: This publication was prepared by Defence Research and Development Canada an agency of the Department of National Defence. The information contained in this publication has been derived and determined through best practice and adherence to the highest standards of responsible conduct of scientifc research. This information is intended for the use of the Department of National Defence, the Canadian Armed Forces (“Canada") and Public Safety partners and, as permitted, may be shared with academia, industry, Canada’s allies, and the public (“Third Parties"). Any use by, or any reliance on or decisions made based on this publication by Third Parties, are done at their own risk and responsibility. Canada does not assume any liability for any damages or losses which may arise from any use of, or reliance on, the publication. Endorsement statement: This publication has been peer-reviewed and published by the Editorial Offce of Defence Research and Development Canada, an agency of the Department of National Defence of Canada. Inquiries can be sent to: [email protected]. ⃝c Her Majesty the Queen in Right of Canada, Department of National Defence, 2021 ⃝c Sa Majesté la Reine en droit du Canada, Ministère de la Défense nationale, 2021 CAN UNCLASSIFIED CAN UNCLASSIFIED Abstract The Royal Canadian Navy (RCN) Intelligence, Surveillance, Target Acquisition and Recon- naissance (ISTAR) Unmanned Aircraft System (UAS) project will acquire a minimum of six UASs which can be transferred within the feet to satisfy force generation and force employ- ment requirements. In February 2019, the Maritime Operational Research Team (MORT) was asked to assist in de-conficting the Maritime Helicopter (MH) and UAS operations at sea by generating fight schedules optimized to provide maximum Time-On-Station (TOS) while taking into account onboard space constraints. To address the problem, a two-step approach was developed. First, a Mixed-Integer Linear Programming (MILP) model was created to generate the optimized MH and UAS schedules while satisfying the various con- straints. Second, a Monte Carlo simulation was built to model the efect that serviceability may have on the TOS. In total, 44 unique Task Group (TG) confgurations were considered. Each TG was composed of up to four ships, up to two MHs with up to two crew each, and up to three UASs. This Scientifc Report, which is a follow-on to a previous Scientifc Letter, includes all the details about the approach, the assumptions made, and all the results and their interpretation. Signifcance for defence and security The results presented in this Scientifc Report provided analytical evidence to support the RCN ISTAR UAS project in determining the optimal number of UASs to acquire. In addition to inform decisions regarding the RCN ISTAR UAS project, the schedules generated also supported the operations of the Canadian Armed Forces UAS Provision of Service (CAF UPS). Finally, the approach documented in this paper may be used in the future by Defence Scientists within DRDC CORA to address similar scheduling problems. DRDC-RDDC-2021-R031 i CAN UNCLASSIFIED CAN UNCLASSIFIED Résumé Le projet, connu sous le nom anglais de RCN ISTAR UAS, sur les Systèmes d’aéronef sans pilote (SASPs) de la Marine royale canadienne pour le renseignement, la surveillance, l’acquisition d’objectifs et la reconnaissance vise à acquérir un minimum de six SASPs. Ces aéronefs pourront être transférés au sein de la fotte pour satisfaire aux exigences de la mise sur pied et de l’emploi des forces. En février 2019, l’Équipe de recherche opérationnelle de la Marine a été invitée à examiner les confits entre les opérations en mer des hélicoptères maritimes et des SASPs en optimisant les horaires de vol afn de fournir une durée maximale de Temps en vol (TEV) tout en tenant compte des contraintes d’espace à bord des navires. Pour adresser le problème, une approche en deux volets a été développée Tout d’abord, un modèle en programmation linéaire en nombres entiers a été créé pour générer les ho- raires optimisés de vol des hélicoptères et des SASPs en satisfaisant les diverses contraintes. Deuxièmement, une simulation de type Monte Carlo a été construite pour estimer l’efet que la fabilité des aéronefs peut avoir sur le TEV. Au total, 44 confgurations uniques de Groupe opérationnel (GO) naval ont été prises en considération. ChaqueGO était composé d’un maximum de quatre navires, jusqu’à deux hélicoptères avec au plus deux équipages chacun, et jusqu’à trois SASPs. Ce rapport scientifque, qui fait suite à une lettre scienti- fque, comprend tous les détails sur l’approche, les hypothèses avancées ainsi que tous les résultats et leur interprétation. Importance pour la défense et la sécurité Les résultats présentés dans ce rapport scientifque ont fourni des preuves analytiques pour le projet RCN ISTAR UAS afn de déterminer le nombre optimal de SASPs à acquérir. En plus d’informer les décisions concernant le projet RCN ISTAR UAS, les horaires de vol générés ont également soutenu les opérations du CAF UPS (en anglais : Canadian Armed Forces UAS Provision of Service). Enfn, l’approche documentée dans ce rapport peut être utilisée dans le futur par des scientifques de la défense au sein de RDDC CARO afn d’adresser des problèmes similaires. ii DRDC-RDDC-2021-R031 CAN UNCLASSIFIED CAN UNCLASSIFIED Table of contents Abstract...........................................i Signifcance for defence and security...........................i Résumé........................................... ii Importance pour la défense et la sécurité........................ ii Table of contents...................................... iii List of fgures........................................ vii List of tables........................................x Acknowledgements..................................... xi 1 Introduction.......................................1 1.1 Background...................................1 1.2 Previous work..................................2 1.3 Objective and scope...............................2 1.4 Outline of the report..............................3 2 Methodology......................................4 2.1 Overview.....................................4 2.2 Task group combinations............................4 2.3 Optimizing operational schedules.......................5 2.3.1 General activities in operational profle...............7 2.3.2 Crew rest and employment......................8 2.3.3 Duration of the diferent activities..................9 2.3.4 Asset profles considered....................... 10 2.3.5 Space constraints........................... 12 2.3.6 Mixed-Integer Linear Programming................. 13 DRDC-RDDC-2021-R031 iii CAN UNCLASSIFIED CAN UNCLASSIFIED 2.4 Serviceability rate................................ 13 2.5 Summary of Assumptions............................ 14 3 Results......................................... 17 3.1 Interpretation of the results tables....................... 17 3.2 Without Unmanned Aircraft System (UAS)................. 18 3.3 Both space constraints............................. 18 3.4 Deck fouling constraint only.......................... 20 3.5 Discussion.................................... 22 3.5.1 How many UAS?............................ 22 3.5.2 Reduced fying hours and space constraints............. 23 3.5.3 Impact of Serviceability Rates.................... 23 4 Conclusion....................................... 25 List of acronyms...................................... 27 References.......................................... 29 Annex A: Mixed-Integer Linear Programming (MILP) formulation......... 31 A.1 Problem................................. 31 A.2 Notional example............................ 31 A.3 Defnitions................................ 32 A.4 Constraints............................... 34 A.5 Objective function........................... 40 A.6 Implementation............................. 40 A.7 Variant of the problem......................... 42 iv DRDC-RDDC-2021-R031 CAN UNCLASSIFIED CAN UNCLASSIFIED Annex B: Modeling serviceability............................ 43 B.1 Problem................................. 43 B.2 Monte Carlo simulation......................... 44 B.3 Parameters................................ 46 B.4 Example................................. 46 Annex C: Discussion on changes to the main algorithm................ 47 Annex D: Sensitivity analysis on the UAS serviceability rate............. 49 Annex E: Tables of results................................ 51 E.1 All space constraints.......................... 51 E.2 Deck fouling constraints only..................... 55 Annex F: User guide................................... 59 F.1 Required software and packages.................... 59 F.2 Excel sheets............................... 60 F.2.1
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