Multidisciplinary Design Optimization for Space Applications

Multidisciplinary Design Optimization (MDO) has been increasingly studied in aerospace engineering with the main purpose of reducing monetary and schedule costs. The traditional design approach of optimizing each discipline separately and manually iterating to achieve good solutions is substituted by exploiting the interactions between the disciplines and concurrently optimizing every subsystem. The target of the research was the development of a flexible software suite capable of concurrently optimizing the design of a rocket propellant launch vehicle for multiple objectives. The possibility of combining the advantages of global and local searches have been exploited in both the MDO architecture and in the selected and self developed optimization methodologies. Those have been compared according to computational efficiency and performance criteria. Results have been critically analyzed to identify the most suitable optimization approach for the targeted MDO problem

Barge Prioritization, Assignment, and Scheduling During Inland Waterway Disruption Responses

Inland waterways face natural and man-made disruptions that may affect navigation and infrastructure operations leading to barge traffic disruptions and economic losses. This dissertation investigates inland waterway disruption responses to intelligently redirect disrupted barges to inland terminals and prioritize offloading while minimizing total cargo value loss. This problem is known in the literature as the cargo prioritization and terminal allocation problem (CPTAP). A previous study formulated the CPTAP as a non-linear integer programming (NLIP) model solved with a genetic algorithm (GA) approach. This dissertation contributes three new and improved approaches to solve the CPTAP. The first approach is a decomposition based sequential heuristic (DBSH) that reduces the time to obtain a response solution by decomposing the CPTAP into separate cargo prioritization, assignment, and scheduling subproblems. The DBSH integrates the Analytic Hierarchy Process and linear programming to prioritize cargo and allocate barges to terminals. Our findings show that compared to the GA approach, the DBSH is more suited to solve large sized decision problems resulting in similar or reduced cargo value loss and drastically improved computational time. The second approach formulates CPTAP as a mixed integer linear programming (MILP) model improved through the addition of valid inequalities (MILP\u27). Due to the complexity of the NLIP, the GA results were validated only for small size instances. This dissertation fills this gap by using the lower bounds of the MILP\u27 model to validate the quality of all prior GA solutions. In addition, a comparison of the MILP\u27 and GA solutions for several real world scenarios show that the MILP\u27 formulation outperforms the NLIP model solved with the GA approach by reducing the total cargo value loss objective. The third approach reformulates the MILP model via Dantzig-Wolfe decomposition and develops an exact method based on branch-and-price technique to solve the model. Previous approaches obtained optimal solutions for instances of the CPTAP that consist of up to five terminals and nine barges. The main contribution of this new approach is the ability to obtain optimal solutions of larger CPTAP instances involving up to ten terminals and thirty barges in reasonable computational time

Convex modeling for optimal battery sizing and control of an electric variable transmission powertrain

Hybrid Electric Vehicles are being considered a convenient intermediate product in the conversion process from conventional to pure electric vehicles, due to their compromise on cost, fuel consumption, and driving range. Convex modeling steps for the problem of optimal battery sizing and energy management of a plug-in hybrid electric vehicle with an electric variable transmission are presented. Optimal energy management was achieved by a switched model control, with driving modes identified by the engine on/off state. In pure electric mode, convex optimization was employed to find the optimal torque split between two electric machines, to maximize powertrain efficiency. In hybrid mode, optimization was carried out in a bilevel program. One level optimizes speed of a compound unit that includes the engine and electric machines. Another level optimizes the power split between the compound unit and the battery. The proposed method is used to minimize the total cost of ownership of a passenger vehicle for a daily commuter, including costs for battery, fossil fuel and electricity

Robust Modeling for Optimal Control of Parallel Hybrids With Dynamic Programming

The aim of this work is to provide insight and guidelines for engineers and researchers when developing hybrid powertrain models to be employed in a dynamic programming optimal control algorithm. In particular, we focus on the advantages and disadvantages of the various control sets that can be used to characterize the power flow (e.g. the engine torque or a torque-split coefficient). Dynamic programming is the reference optimal control technique for hybrid electric vehicles. However, its practical implementation is not exempt from numerical issues which may hamper its accuracy. Amongst these, some are directly related to the different modeling choices that can be made when defining the system dynamics of the powertrain. To treat these issues, we first define four relevant evaluation criteria: control bounds definition, numerical efficiency, model complexity and interpretability. Then, we introduce eight different control sets and we discuss and compare them in light of these criteria. This discussion is supported by an extensive set of numerical experiments on a p2 parallel hybrid. Finally, we revisit our analysis and simulation results to draw modeling recommendations

무인항공기 운영을 위한 덮개 모델 기반의 대규모 최적화 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 문일경.There is increasing interest in the unmanned aerial vehicle (UAV) in various fields of the industry, starting from the surveillance to the logistics. After introducing the smart city, there are attempts to utilize UAVs in the public service sector by connecting individual components of the system with both information and physical goods. In this dissertation, the UAV operation problems in the public service sector is modeled in the set covering approach. There is a vast literature on the facility location and set covering problems. However, when operating UAVs in the system, the plan has to make the most of the flexibility of the UAV, but also has to consider its physical limitation. We noticed a gap between the related, existing approaches and the technologies required in the field. That is, the new characteristics of the UAV hinder the existing solution algorithms, or a brand-new approach is required. In this dissertation, two operation problems to construct an emergency wireless network in a disaster situation by UAV and one location-allocation problem of the UAV emergency medical service (EMS) facility are proposed. The reformulation to the extended formulation and the corresponding branch-and-price algorithm can overcome the limitations and improve the continuous or LP relaxation bounds, which are induced by the UAV operation. A brief explanation of the UAV operation on public service, the related literature, and the brief explanation of the large-scale optimization techniques are introduced in Chapter 1, along with the research motivations and contributions, and the outline of the dissertations. In Chapter 2, the UAV set covering problem is defined. Because the UAV can be located without predefined candidate positions, more efficient operation becomes feasible, but the continuous relaxation bound of the standard formulation is weakened. The large-scale optimization techniques, including the Dantzig-Wolfe decomposition and the branch-and-price algorithm, could improve the continuous relaxation bound and reduce the symmetries of the branching tree and solve the realistic-scaled problems within practical computation time. To avoid numerical instability, two approximation models are proposed, and their approximation ratios are analyzed. In Chapter 3, UAV variable radius set covering problem is proposed with an extra decision on the coverage radius. While implementing the branch-and-price algorithm to the problem, a solvable equivalent formulation of the pricing subproblem is proposed. A heuristic based on the USCP is designed, and the proposed algorithm outperformed the benchmark genetic algorithm proposed in the literature. In Chapter 4, the facility location-allocation problem for UAV EMS is defined. The quadratic variable coverage constraint is reformulated to the linear equivalent formulation, and the nonlinear problem induced by the robust optimization approach is linearized. While implementing the large-scale optimization techniques, the structure of the subproblem is analyzed, and two solution approaches for the pricing subproblem are proposed, along with a heuristic. The results of the research can be utilized when implementing in the real applications sharing the similar characteristics of UAVs, but also can be used in its abstract formulation.현재, 지역 감시에서 물류까지, 무인항공기의 다양한 산업에의 응용이 주목받고 있다. 특히, 스마트 시티의 개념이 대두된 이후, 무인항공기를 공공 서비스 영역에 활용하여 개별 사회 요소를 연결, 정보와 물자를 교환하고자 하는 시도가 이어지고 있다. 본 논문에서는 공공 서비스 영역에서의 무인항공기 운영 문제를 집합덮개문제 관점에서 모형화하였다. 설비위치결정 및 집합덮개문제 영역에 많은 연구가 진행되어 있으나, 무인항공기를 운영하는 시스템의 경우 무인항공기가 갖는 자유도를 충분히 활용하면서도 무인항공기의 물리적 한계를 고려한 운영 계획을 필요로 한다. 우리는 본 문제와 관련된 기존 연구와 현장이 필요로 하는 기술의 괴리를 인식하였다. 이는 다시 말해, 무인항공기가 가지는 새로운 특성을 고려하면 기존의 문제 해결 방법을 통해 풀기 어렵거나, 혹은 새로운 관점에서의 문제 접근이 필요하다는 것이다. 본 논문에서는 재난이 발생한 지역에 무인항공기를 이용하여 긴급무선네트워크를 구성하는 두가지 문제와, 무인항공기를 이용하여 응급의료서비스를 제공하는 시설의 위치설정 및 할당문제를 제안한다. 확장문제로의 재공식화와 분지평가법을 활용하여, 무인항공기의 활용으로 인해 발생하는 문제 해결 방법의 한계를 극복하고 완화한계를 개선하였다. 공공 서비스 영역에서의 무인항공기 운영, 관련된 기존 연구와 본 논문에서 사용하는 대규모 최적화 기법에 대한 개괄적인 설명, 연구 동기 및 기여와 논문의 구성을 1장에서 소개한다. 2장에서는 무인항공기 집합덮개문제를 정의한다. 무인항공기는 미리 정해진 위치 없이 자유롭게 비행할 수 있기 때문에 더 효율적인 운영이 가능하나, 약한 완화한계를 갖게 된다. Dantzig-Wolfe 분해와 분지평가법을 포함한 대규모 최적화 기법을 통해 완화한계를 개선할 수 있으며, 분지나무의 대칭성을 줄여 실제 규모의 문제를 실용적인 시간 안에 해결할 수 있었다. 수치적 불안정성을 피하기 위하여, 두 가지 선형 근사 모형이 제안되었으며, 이들의 근사 비율을 분석하였다. 3장에서는 무인항공기 집합덮개문제를 일반화하여 무인항공기 가변반경 집합덮개문제를 정의한다. 분지평가법을 적용하면서 해결 가능한 평가 부문제를 제안하였으며, 휴리스틱을 설계하였다. 제안한 풀이 방법들이 기존 연구에서 제안한 벤치마크 유전 알고리즘을 능가하는 결과를 나타내었다. 4장에서는 무인항공기 응급의료서비스를 운영하는 시설의 위치설정 및 할당문제를 정의하였다. 2차 가변반경 범위제약이 선형의 동치인 수식으로 재공식화되었으며, 강건최적화 기법으로 인해 발생하는 비선형 문제를 선형화하였다. 대규모 최적화 기법을 적용하면서, 평가 부문제의 구조를 분석하여 두 가지 풀이 기법과 휴리스틱을 제안하였다. 본 연구의 결과는 무인항공기와 비슷한 특징을 가지는 실제 사례에 적용될 수 있으며, 추상적인 문제로써 다양한 분야에 그대로 활용될 수도 있다.Abstract i Contents vii List of Tables ix List of Figures xi Chapter 1 Introduction 1 1.1 Unmanned aerial vehicle operation on public services 1 1.2 Facility location problems 3 1.3 Large-scale optimization techniques 4 1.4 Research motivations and contributions 6 1.5 Outline of the dissertation 12 Chapter 2 Unmanned aerial vehicle set covering problem considering fixed-radius coverage constraint 14 2.1 Introduction 14 2.2 Problem definition 20 2.2.1 Problem description 22 2.2.2 Mathematical formulation 23 2.2.3 Discrete approximation model 26 2.3 Branch-and-price approach for the USCP 28 2.3.1 An extended formulation of the USCP 29 2.3.2 Branching strategies 34 2.3.3 Pairwise-conflict constraint approximation model based on Jung's theorem 35 2.3.4 Comparison of the approximation models 40 2.3.5 Framework of the solution algorithm for the PCBP model 42 2.4 Computational experiments 44 2.4.1 Datasets used in the experiments 44 2.4.2 Algorithmic performances 46 2.5 Solutions and related problems of the USCP 61 2.6 Summary 64 Chapter 3 Unmanned aerial vehicle variable radius set covering problem 66 3.1 Introduction 66 3.2 Problem definition 70 3.2.1 Mathematical model 72 3.3 Branch-and-price approach to the UVCP 76 3.4 Minimum covering circle-based approach 79 3.4.1 Formulation of the pricing subproblem II 79 3.4.2 Equivalence of the subproblem 82 3.5 Fixed-radius heuristic 84 3.6 Computational experiments 86 3.6.1 Datasets used in the experiments 88 3.6.2 Solution algorithms 91 3.6.3 Algorithmic performances 94 3.7 Summary 107 Chapter 4 Facility location-allocation problem for unmanned aerial vehicle emergency medical service 109 4.1 Introduction 109 4.2 Related literature 114 4.3 Location-allocation model for UEMS facility 117 4.3.1 Problem definition 118 4.3.2 Mathematical formulation 120 4.3.3 Linearization of the quadratic variable coverage distance function 124 4.3.4 Linear reformulation of standard formulation 125 4.4 Solution algorithms 126 4.4.1 An extended formulation of the ULAP 126 4.4.2 Branching strategy 129 4.4.3 Robust disjunctively constrained integer knapsack problem 131 4.4.4 MILP reformulation approach 132 4.4.5 Decomposed DP approach 133 4.4.6 Restricted master heuristic 136 4.5 Computational experiments 137 4.5.1 Datasets used in the experiments 137 4.5.2 Algorithmic performances 140 4.5.3 Analysis of the branching strategy and the solution approach of the pricing subproblem 150 4.6 Summary 157 Chapter 5 Conclusions and future research 160 5.1 Summary 160 5.2 Future research 163 Appendices 165 A Comparison of the computation times and objective value of the proposed algorithms 166 Bibliography 171 국문초록 188 감사의 글 190Docto

Production and Logistics Systems Improvements - Biim Ultrasound AS

A crucial aspect of the supply chain network design process is deciding on optimal locations to situate new facilities. Facility location decisions rely on many factors, some of which might be conflicting with each other. The decision factors can be either quantitative or qualitative, thus a brute-force prioritization of one over another could be detrimental overall. To ensure the efficacy of the selection process, decision makers must consider both the quantitative and qualitative factors in tandem. Some of the common methods employed in the literature by organizations to facilitate their decision-making process include: optimization models and algorithms, decision support systems and computerized analytics tools. To this end, this thesis proposes a hybrid Multi-Criteria Decision Making (MCDM) model to aid the selection of an optimal location that suits the strategic fit of an organization. The proposed model integrates the Analytic Hierarchy Process (AHP) methodology for Multi-Attribute Decision Making (MADM) with Mixed Integer Programming (MIP). The solution is modeled and implemented with the AIMMS modeling language as well as the Gurobi Optimization tool in Python. This thesis work is based on a case study from Biim Ultrasound

Multi-parametric programming : novel theory and algorithmic developments

Imperial Users onl

Mixed-model Sequencing with Stochastic Failures: A Case Study for Automobile Industry

In the automotive industry, the sequence of vehicles to be produced is determined ahead of the production day. However, there are some vehicles, failed vehicles, that cannot be produced due to some reasons such as material shortage or paint failure. These vehicles are pulled out of the sequence, and the vehicles in the succeeding positions are moved forward, potentially resulting in challenges for logistics or other scheduling concerns. This paper proposes a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provides improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. Moreover, we provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances.Comment: 30 pages, 9 figure

Hypersonic Research Vehicle (HRV) real-time flight test support feasibility and requirements study. Part 2: Remote computation support for flight systems functions

The requirements are assessed for the use of remote computation to support HRV flight testing. First, remote computational requirements were developed to support functions that will eventually be performed onboard operational vehicles of this type. These functions which either cannot be performed onboard in the time frame of initial HRV flight test programs because the technology of airborne computers will not be sufficiently advanced to support the computational loads required, or it is not desirable to perform the functions onboard in the flight test program for other reasons. Second, remote computational support either required or highly desirable to conduct flight testing itself was addressed. The use is proposed of an Automated Flight Management System which is described in conceptual detail. Third, autonomous operations is discussed and finally, unmanned operations