학위논문(석사) -- 서울대학교대학원 : 공과대학 조선해양공학과, 2023. 2. 노명일.Currently, the military is planning to reduce the number of troops for reasons such as a decrease in the youth population and a shortened service period. However, battleships require more crew than before due to increased size, mounted weapons, and equipment. Therefore, deploying the appropriate number of crew members on the battleships is important. In addition, since battleships must consider various operating situations (combat, maintenance, etc.) and crew members have various specialties, it is essential to optimize the crew's composition to suit the battleships' characteristics. To this end, the Navy relies on experts with relevant know-how and data based on legacy ships. Still, additional optimization is required for reasons such as changes in military policy, enlargement of new battleships, and diversification of weapons.
In this paper, given the specifications of the design ship and major mounted equipment, the crew composition is primarily calculated using the data of the militarys legacy ship currently in operation. Since the result was calculated based on the past, the expert system was additionally used to calculate the result reflecting the characteristics of the ship I designed and the current operation of the ship. Afterward, a method of optimizing the composition of the crew was studied using the simulation method.
The estimation method based on legacy ship data estimates crew members with various specialties in consideration of ship specifications and loaded weapons and estimates the crew composition suitable for the design ship using regression analysis. The estimation method of an expert system uses rule-based expert systems to re-estimate the crew member composition.
The estimation method based on simulation optimizes the composition of the crew by comparing and analyzing mission execution time and efficiency using Discrete Event System specification (DEVS) simulation in consideration of scenarios that mimic the actual operating situation of the ship.
Finally, a self-developed program was implemented for verification, and the performance was verified by inputting the specifications of the US Navy ship and the number of crew members into the program.현재 군은 청년 인구 감소, 복무기간 단축 등을 이유로 병력 감축의 계획의 세우고 있다. 하지만 전투함은 대형화, 탑재 무장, 장비의 증가 등으로 인해 이전보다 많은 운영 인원이 필요하다. 그러므로 적절한 승조원의 수를 전투함에 배치하는 것이 중요하다. 또한 전투함은 여러 운용 상황(전투, 정비 등)을 고려해야 하고 승조원의 특기가 다양하므로 승조원의 구성을 전투함의 특성에 맞게 최적화하는 것이 필수적이다. 이를 위해 해군은 관련 노하우를 갖춘 전문가와 실적선 기반의 자료에 의존하고 있으나, 군 정책의 변화, 신형 전투함의 대형화, 무장의 다양화 등의 이유로 추가적인 최적화가 필요하다.
본 논문에서는 설계 함정의 제원과 주요 탑재 장비가 주어질 때, 현재 군이 시행 중인 실적선 자료를 활용해 운영 기반의 승조원 구성을 일차적으로 산출하였다. 해당 결과는 과거기반의 승조원 구성을 산출하였기 때문에 추가적으로 전문가시스템을 활용하여 내가 설계하는 함정의 특성과 현재 함정 운영에 대한 사항을 반영한 결과를 산출하였다. 이후 그 결과를 시뮬레이션 방법을 이용하여 전투함의 승조원 구성을 최적화하는 방법을 연구하였다. 실적선 자료 기반의 승조원 추정 방법은 다양한 특기를 가진 승조원을 함정의 제원, 탑재된 무장 등을 고려하여 분류하고, 회귀 분석 등을 이용하여 설계 함정에 맞는 승조원 구성을 추정하게 된다. 전문가 시스템 기반의 승조원 추정 방법은 Rule-based expert systems를 활용하여 함정 운용을 고려하여 설계한 CEM(Crew manning Expert system Model)을 통해 승조원 구성을 재추정하게된다. 시뮬레이션 기반의 최적화 방법은 함정의 실제 운영 상황을 모사한 시나리오를 고려하여 이산 사건 (DEVS: Discrete EVent System specification) 시뮬레이션을 이용해 임무 수행 시간 및 효율을 비교 분석하여 승조원 구성을 최적화한다. 최종적으로 검증을 위해 자체 개발 프로그램을 구현하였고, 미 해군의 함정의 제원 및 승조원의 수를 프로그램에 입력하여 성능을 검증하였다.Abstract 10
1. Introduction 12
1.1. Research background 12
1.2 Related works 14
1.3 Target of the study 16
2. The first estimation based on legacy ship data 20
2.1. Overview of the crew on board the naval ship 20
2.1.1. Boatswains Mate (BM) 20
2.1.2. Quartermasters (QM) 21
2.1.3. Information Technician (IT) 21
2.1.4. Operation Specialist (OS) 21
2.1.5. Electronic Warfare (EW) 21
2.1.6. Electronic Technicians (ET) 22
2.1.7. Fire Controlmen (FC) 22
2.1.8. Sonar Technician (ST) 22
2.1.9. Gunners Mate (GM) 22
2.1.10. Gasturbine System (GS) / Enginermen (EN) 22
2.1.11. Electricians Mate (EM) 23
2.1.12. Machinery Repairman (MR) 23
2.1.13. Culinary Specialist (CS) 23
2.1.14. Yeoman (YN) 23
2.1.15. Hospital Corpsman (HM) 23
2.1.16. Division of naval ship 24
2.2. Key consideration for estimation of crew manning 25
2.2.13. Analysis of the availability of navigation watch 27
2.2.14. Analysis of availability of crew deployment in a combat situation 28
2.2.15. Analysis considering the special task 30
2.3. System configuration of the first estimation 31
2.3.13. Input data of the first estimation 33
2.3.14. System configuration of the first estimation 34
2.3.15. Assignment of crew 34
2.3.16. Output data of the first estimation 36
3. The second estimation based on the Expert system 37
3.1. Knowledge representation 37
3.1.1. Production rule 37
3.1.2 Semantic net 38
3.1.3 Frame 40
3.1.4 Hybrid knowledge representation 41
3.2. Rule-based expert system 43
3.2.1. Knowledge base 43
3.2.2. Inference engine 44
3.2.3. User interface 44
3.3 Model using expert system 45
3.3.1. Object information 46
3.3.2. Relation information 48
3.3.3. Expert system for crew deployment 50
4. The final estimation using DEVS 51
4.1. System specification formalisms 51
4.2. DEVS formalism 52
4.2.1. Atomic model 53
4.2.2. Coupled model 57
4.3. Configuration of model 60
4.4. The first detailed DEVS model (For the naval ships combat situation) 62
4.4.1. Total scenario composition 63
4.4.2. Sub-scenario composition – AAW 64
4.4.3. Sub-scenario composition – Close ASUW 66
4.4.4. Sub-scenario composition – ASW 67
4.4.5. DEVS Model composition 68
4.5. The second detailed DEVS model (For the naval ships emergency situation) 72
4.5.1. Scenario composition 73
4.5.2. Composition of the DEVS model 76
5. User interface 78
5.1. Tool for estimation based on legacy ship data 79
5.2. Tool for estimation based on expert system 80
5.3. Tool for estimation based on DEVS 81
6. Application of the method for crew deployment 83
6.1. Description of an example 83
6.2. The first estimation based on legacy ship data for application 84
6.3. The second estimation based on experts knowledge for application 90
6.4. The final estimation based on DEVS for application 95
6.4.1. Result of DEVS model for a combat situation 96
6.4.2. Result of DEVS model for emergency situation 99
7. Conclusions and future works 102
References 104
APPENDIX 106
A. Detailed data of combat scenarios 107
국문 초록 109석
