An Optimization Approach to Integrated Requirements Development for Acquisition Programs.
Abstract not provided.
Dessanti, Alexander↗
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Ultra-High-Dimensional Optimization and Interactive Requirements Negotiation for New Acquisition Programs.
Abstract not provided.
Henry, Stephen Michael↗
Ultra-High Dimensional Optimization and Interactive Requirements Negotiation for New Acquisition Programs.
Abstract not provided.
Dessanti, Alexander↗
Analysis of Radioactive Waste and Contamination using 3D Position-Sensitive CdZnTe Detectors
INTRODUCTION The US has surplus plutonium. This material will be diluted in gloveboxes at SRS [Ref 1 and 2]. This process will create holdup. Holdup is material in an unexpected or unwanted location. Because this holdup material is primarily PuO2, it must be accounted for (measured). This measurement is typically done using a liquid-nitrogen cooled, HPGe detector. To enable more rapid processing, advanced holdup technologies are being explored [Ref 3 and 4]. METHODS For the surplus plutonium dilute and dispose program, the traditional detector system could slow down the optimal operational tempo of the program. A multiple-detector system, providing complete glovebox coverage, based on CdZnTe crystals, 3D-positioning electronics, and a coded aperture mask, is in the process of being developed by ORNL and SRNL [Ref 4].
Whiteside, Tad [Savannah River National Laboratory↗
PIPS-SBB: A Parallel Distributed-Memory Branch-and-Bound Algorithm for Stochastic Mixed-Integer Programs
Stochastic mixed-integer programs (SMIPs) deal with optimization under uncertainty at many levels of the decision-making process. When solved as extensive formulation mixed- integer programs, problem instances can exceed available memory on a single workstation. In order to overcome this limitation, we present PIPS-SBB: a distributed-memory parallel stochastic MIP solver that takes advantage of parallelism at multiple levels of the optimization process. We also show promising results on the SIPLIB benchmark by combining methods known for accelerating Branch and Bound (B&B) methods with new ideas that leverage the structure of SMIPs. Finally, we expect the performance of PIPS-SBB to improve further as more functionality is added in the future.
97 MATHEMATICS AND COMPUTING↗
Optimal Allocation of Water Resources Using a Two-Stage Stochastic Programming Method with Interval and Fuzzy Parameters
Efficient water allocation is one of the most prominent issues in water resources management. In this research, a two-stage interval-parameter stochastic fuzzy programming with type 2 membership functions was used to allocate water resources optimally to different users under uncertainty. This method can handle uncertainties expressed as probability distributions, discrete intervals, and fuzzy sets. The model considers treated wastewater as an allocable water resource in a scenario, in addition to water that is extracted from wellheads, springs, and qanats. Moreover, the loss rate of water during distribution, surplus water in the reservoir in the previous and the next period, and treated wastewater parameters have been incorporated into the model. This model was applied to a case study of water resources allocation within a multi-user and multi-reservoir context in the Zarand region of Kerman, Iran. The results indicate that reasonable solutions have been generated, in the form of interval and fuzzy information under different scenarios that could help managers provide optimal water resources allocation plans. The results also demonstrate that establishing a wastewater treatment station increases the system net benefits, surplus water in wellheads for the next period, and system reliability (level of satisfying the fuzzy goal and constraints), and decreases encountering water shortages.
58 GEOSCIENCES↗
TH-B-207B-01: Optimizing Pediatric CT in the Emergency Department
This imaging educational program will focus on solutions to common pediatric image quality optimization challenges. The speakers will present collective knowledge on best practices in pediatric imaging from their experience at dedicated children’s hospitals. One of the most commonly encountered pediatric imaging requirements for the non-specialist hospital is pediatric CT in the emergency room setting. Thus, this educational program will begin with optimization of pediatric CT in the emergency department. Though pediatric cardiovascular MRI may be less common in the non-specialist hospitals, low pediatric volumes and unique cardiovascular anatomy make optimization of these techniques difficult. Therefore, our second speaker will review best practices in pediatric cardiovascular MRI based on experiences from a children’s hospital with a large volume of cardiac patients. Learning Objectives: To learn techniques for optimizing radiation dose and image quality for CT of children in the emergency room setting. To learn solutions for consistently high quality cardiovascular MRI of children.
60 APPLIED LIFE SCIENCES↗
Optimal Allocation of Dynamic Var Sources Using the Voronoi Diagram Method Integrating Linear Programing
Dynamic reactive power (var) sources can effectively mitigate fault-induced delayed voltage recovery (FIDVR) issues. This paper optimizes the sizes of dynamic var sources at given locations against FIDVR issues under severe contingencies. First, the geometric characteristics about the non-convex solution space of this problem are studied. Accordingly, a Voronoi diagram approach integrating linear programming (LP) is proposed, which disperses a number of sample points of potential solutions in the searching space to construct a Voronoi diagram blending the local cost functions over the entire space by Barycentric interpolation in Voronoi regions. New sample points are then recursively added, including the tentative optimal point using LP, the most depopulated area point ensuring global fidelity, and the connecting point, until the stopping criterion is met. The new approach is demonstrated in detail on the WSCC 9-bus system. A case study on the NPCC 140-bus system also validates that the proposed approach can effectively estimate the boundary and the geometry of the feasible solution region in the searching space and find the optimal solution.
barycentric interpolation↗
Tsunami evacuation mathematical model for the city of Padang
Tsunami is a series of wave trains which travels with high speed on the sea surface. This traveling wave is caused by the displacement of a large volume of water after the occurrence of an underwater earthquake or volcano eruptions. The speed of tsunami decreases when it reaches the sea shore along with the increase of its amplitudes. Two large tsunamis had occurred in the last decades in Indonesia with huge casualties and large damages. Indonesian Tsunami Early Warning System has been installed along the west coast of Sumatra. This early warning system will give about 10-15 minutes to evacuate people from high risk regions to the safe areas. Here in this paper, a mathematical model for Tsunami evacuation is presented with the city of Padang as a study case. In the model, the safe areas are chosen from the existing and selected high rise buildings, low risk region with relatively high altitude and (proposed to be built) a flyover ring road. Each gathering points are located in the radius of approximately 1 km from the ring road. The model is formulated as an optimization problem with the total normalized evacuation time as the objective function. The constraints consist of maximum allowable evacuation time in each route, maximum capacity of each safe area, and the number of people to be evacuated. The optimization problem is solved numerically using linear programming method with Matlab. Numerical results are shown for various evacuation scenarios for the city of Padang.
54 ENVIRONMENTAL SCIENCES↗
Model Predictive Control for a Grid-interactive Efficient Thermal Storage-integrated Heat Pump System
Building heating and cooling systems can be used to overcome the mismatch between the intermittent supply of renewable power and the fluctuating demand for electricity. A novel underground thermal energy storage integrated with a dual-source heat pump has been proposed to mitigate the mismatch while meeting the thermal demand of buildings efficiently. Conventional thermostat control with heuristic rules cannot provide intelligent decisions to maximize the thermal efficiency and flexibility of the proposed system. Advanced control strategies like model predictive control (MPC) have provided a new paradigm for grid-interactive efficient building operation with the advancement of computation and sensing. This study developed an MPC for the proposed system to provide grid service for Demand Side Management and minimize the operating cost of building owners. A control-oriented dynamic model of the proposed system has been developed. Given an objective function and proper constraints, an optimization problem is formulated to determine the optimal control strategy of the system. Dynamic Programming is adopted to solve the optimization problem. A rule-based control (RBC) is also developed to achieve similar goals. Short-term simulations are conducted to compare the system performance resulting from the two controls. The simulation results indicate that the MPC performs more intelligently than the RBC in charging thermal energy storage and selecting heat pump sources by taking advantage of the predicted cooling demands of the building and the performance of the integrated system. As a result, the MPC could save energy and reduce operating costs compared with the RBC. A case study shows that, for a 3-day operation, the MPC saves 36.9% energy and reduces 38.5% operating cost compared with the RBC.
Shi, Liang↗
CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field
Here we report that proper treatment of nonbonded interactions is essential for the accuracy of molecular dynamics (MD) simulations, especially in studies of lipid bilayers. The use of the CHARMM36 force field (C36 FF) in different MD simulation programs can result in disagreements with published simulations performed with CHARMM due to differences in the protocols used to treat the long-range and 1-4 nonbonded interactions. In this study, we systematically test the use of the C36 lipid FF in NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM. A wide range of Lennard-Jones (LJ) cutoff schemes and integrator algorithms were tested to find the optimal simulation protocol to best match bilayer properties of six lipids with varying acyl chain saturation and head groups. MD simulations of a 1,2-dipalmitoyl-sn-phosphatidylcholine (DPPC) bilayer were used to obtain the optimal protocol for each program. MD simulations with all programs were found to reasonably match the DPPC bilayer properties (surface area per lipid, chain order parameters, and area compressibility modulus) obtained using the standard protocol used in CHARMM as well as from experiments. The optimal simulation protocol was then applied to the other five lipid simulations and resulted in excellent agreement between results from most simulation programs as well as with experimental data. AMBER compared least favorably with the expected membrane properties, which appears to be due to its use of the hard-truncation in the LJ potential versus a force-based switching function used to smooth the LJ potential as it approaches the cutoff distance. The optimal simulation protocol for each program has been implemented in CHARMM-GUI. This protocol is expected to be applicable to the remainder of the additive C36 FF including the proteins, nucleic acids, carbohydrates, and small molecules.
97 MATHEMATICS AND COMPUTING↗
Optimal charging management and infrastructure planning for free-floating shared electric vehicles
Recently, major car-sharing service providers have begun to include electric vehicles (EVs) in free-floating car-sharing fleets. For these EVs, downtime due to charging, including time spent traveling to and waiting in queues at charging stations in a sparse charging infrastructure network, is a major barrier to sustainable operation. An intuitive solution to overcome this barrier is to increase the number of fast-charging stations in the fleet service area. This paper studies the relationship between fleet vehicle downtime and the number of charging stations by modeling the fleet operations of a major car-sharing service provider. An integer programming model is developed that jointly optimizes charging station allocation, in terms of the number and location of charging stations, and the assignment of EVs to charging stations. Case study results showed that fleet vehicle charging time comprises 72-75% of the total downtime spent on charging trips. This indicates that charging time, as opposed to travel time or waiting time, is the dominant component of total fleet downtime for charging. The study also shows that by adding 5 to 20 new charging stations reduces total fleet downtime and travel time during charging trips by 2-4% and 26-49%, respectively. Although adding new charging stations to the fleet service area reduces total charging trip travel time significantly, it does not reduce total downtime significantly. Results also show that if the EV battery state of the charge (SOC) threshold—below which a charging trip is initiated—is less than 18%, not all EVs in the car-sharing fleet can be charged using the existing direct current fast chargers (DCFCs) in the fleet’s service area. Furthermore, this implies that adequate charging infrastructure coverage is required to ensure that EVs of a given range can operate in the car-sharing fleet.
25 ENERGY STORAGE↗
Existing Whole-House Case Study: Evaluation of Missed Energy Saving Opportunity Based on Illinois Home Performance Program Field Data: Homeowner Selected Upgrades vs. Cost-Optimized Solutions, Chicago, Illinois
Expanding on previous research by PARR, this study compares measure packages installed during 800 Illinois Home Performance with ENERGY STAR® (IHP) residential retrofits to those recommended as cost-optimal by Building Energy Optimization (BEopt) modeling software. In previous research, cost-optimal measure packages were identified for 15 Chicagoland single family housing archetypes, called housing groups. In the present study, 800 IHP homes are first matched to one of these 15 housing groups, and then the average measures being installed in each housing group are modeled using BEopt to estimate energy savings. For most housing groups, the differences between recommended and installed measure packages is substantial. By comparing actual IHP retrofit measures to BEopt-recommended cost-optimal measures, missed savings opportunities are identified in some housing groups; also, valuable information is obtained regarding housing groups where IHP achieves greater savings than BEopt-modeled, cost-optimal recommendations.
Air Sealing↗
Application of the Diminishing Returns Concept in the Hydroecologic Restoration of Riverscapes
Principles for optimizing the number and placement of ecological restoration actions on rivers and coasts would be useful in project engineering design and program planning. This study demonstrated that the yield of inundated floodplain habitat area from dike breaching conforms to a diminishing returns model. The aggregate effects of establishing hydrologic connections between a tidally influenced main stem river and the floodplain were experimentally examined using a hydrodynamic model. Restoration clusters of size 1, 4, 8, and more, replicated and randomized within the landscape, yielded average wetted floodplain area conforming with an exponential rise to maximum curve2(0.99)r=. Analysis of the average incremental change in floodplain inundation produced per breach showed that opening 25 % of the channels crossing the dike provided the maximum return on investment as measured by wetted area. Midstream breaches yielded 60 % and upstream breaches 2 % of the wetted area produced by downstream breaches. Dike-breach restoration programs therefore can be optimized by strategic determination of the spatial configuration and number of demolitions, though biological factors such as the accessibility of floodplain habitat and total length of channels connected also need to be considered. These findings have implications for cost-benefit analyses in restoration program planning.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty
The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. Here we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints.
42 ENGINEERING↗
An MINLP formulation for the optimization of multicomponent distillation configurations
Designing configurations for multicomponent distillation, a ubiquitous process in chemical and petrochemical industries, is often challenging. This is because, as the number of components increases, the number of admissible distillation configurations grows rapidly and these configurations vary substantially in their energy needs. Consequently, if a method could identify a few energy-efficient choices from this large set of alternatives, it would be extremely attractive to process designers. This paper develops here such a method by solving a Mixed Integer Nonlinear Program (MINLP) that is formulated to pick, among the regular-column configurations of Shah and Agrawal (2010b), those configurations that have a low vapor-duty requirement. To compute the minimum vapor-duty requirement for each column within the configuration, we use techniques that rely on the Underwood’s method. The combined difficulty arising from the nonlinearity of Underwood equations and the combinatorial explosion of the choice-set of alternatives poses unmistakable challenges for the branch-and-bound algorithm, the current method of choice to globally solve MINLPs. To address this difficulty, we exploit the structure of Underwood equations and derive valid cuts that expedite the convergence of branch-and-bound by enabling global solvers, such as BARON, infer tighter bounds on Underwood roots. This provides a quick way to identify a few lucrative alternative configurations for separation of a given non-azeotropic mixture. We illustrate the practicality of our approach on a case-study concerning heavy-crude distillation and on various other examples from the literature.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
QuComm: Optimizing Collective Communication for Distributed Quantum Computing
Distributed quantum computing (DQC) is a scalable way to build a large-scale quantum computing system while the error-prone nonlocal communication between DQC nodes may heavily degrade the fidelity of the distributed quantum program and thus demands specific compiler optimizations. Previous compilers on DQC communication optimization either assumes unlimited communication resource or a few communication qubits due to the hardware limitation. The former compilers may not be efficient when interfacing with communication-resource-constrained DQC hardware while the latter compilers lose the opportunities of optimizing collective communication and routing concurrent communication as they unnecessarily couple limited communication qubits with the implementation of expensive inter-node operations. In this paper, we invent the communication buffer, a communication facility consisting of idle qubits in each compute node, to decouple the execution of inter-node quantum operations from communication qubits: communication qubits are devoted to generating inter-node entanglement while internode operations are conducted in the communication buffer. The communication buffer provides an intermediate layer for inter-node communication and paves the way for collective communication optimization. We then propose QuComm, a buffer-based compiler framework that first performs smart buffer allocation according to communication characteristics of the distributed quantum program and then optimizes and collectively routes inter-node quantum operations. Experimental results on a hierarchical DQC system show that the proposed QuComm can reduce the most expensive inter-node communication request and the latency of various distributed quantum programs by 50.4% and 47.6% on average, respectively.
Wu, Anbang↗
Optimization Model and Algorithm for Capacity Planning and Operation of Reliable and Carbon-neutral Power Systems with High Penetration of Renewable Generation
In this work, we propose a Generalized Disjunctive Programming (GDP) model that optimizes both long-term capacity planning (such as the number and size of dispatchable/renewable generators, batteries, and transmission lines) and hourly operation (such as on/off schedules of dispatchable generators, power output from each generator, and power flow) to maximize power system reliability while minimizing total cost and CO2 emissions.
Cho, Seolhee↗