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نتیجه جستجو - Cost function

تعداد مقالات یافته شده: 37
ردیف عنوان نوع
1 Topological-Graph Dependencies and Scaling Properties of a Heuristic Qubit-Assignment Algorithm
وابستگی‌های نمودار توپولوژیکی و ویژگی‌های مقیاس‌بندی الگوریتم تخصیص کیوبیت اکتشافی-2022
The qubit-mapping problem aims to assign and route qubits of a quantum circuit onto an noisy intermediate-scale quantum (NISQ) device in an optimized fashion, with respect to some cost function. Finding an optimal solution to this problem is known to scale exponentially in computational complexity; as such, it is imperative to investigate scalable qubit-mapping solutions for NISQ computation. In this work, a noise-aware heuristic qubit-assignment algorithm (which assigns initial placements for qubits in a quantum algorithm to qubits on an NISQ device, but does not route qubits during the quantum algorithm’s execution) is presented and compared against the optimal brute-force solution, as well as a trivial qubit assignment, with the aim to quantify the performance of our heuristic qubit-assignment algorithm. We find that for small, connected-graph algorithms, our heuristic-assignment algorithm faithfully lies in between the effective upper and lower bounds given by the brute-force and trivial qubit-assignment algorithms. Additionally, we find that the topological-graph properties of quantum algorithms with over six qubits play an important role in our heuristic qubit-assignment algorithm’s performance on NISQ devices. Finally, we investigate the scaling properties of our heuristic algorithm for quantum processors with up to 100 qubits; here, the algorithm was found to be scalable for quantum-algorithms that admit path-like graphs. Our findings show that as the size of the quantum processor in our simulation grows, so do the benefits from utilizing the heuristic qubitassignment algorithm, under particular constraints for our heuristic algorithm. This work, thus, characterizes the performance of a heuristic qubit-assignment algorithm with respect to the topological-graph and scaling properties of a quantum algorithm that one may wish to run on a given NISQ device.
INDEX TERMS: Quantum computing | qubit-mapping problem.
مقاله انگلیسی
2 A possibilistic mathematical programming model to control the flow of relief commodities in humanitarian supply chains
یک مدل برنامه ریزی ریاضی احتمالی برای کنترل جریان کالاهای امدادی در زنجیره های تأمین بشردوستانه-2021
In emergency situations, disaster relief organizations are faced with the difficult decision of how to allocate scarce resources in an efficient manner in order to provide the best possible relief action. This paper aims to provide an analytical model that will help relief organizations in reducing human suffering following a disaster while maintaining an acceptable level of cost efficiency. A mathematical model is introduced to optimize the relief distribution problem which considers the social cost —the total sum of logistics and deprivation costs. The fuzzy nature of the deprivation cost function is addressed with possibilistic mixed integer programming with fuzzy objectives to reflect variation in deprivation costs perceptions. The model is solved using the Rolling Horizon method in a sequence of iterations. In each iteration, part of the planning horizon is modeled in detail and the rest of the time horizon is represented in an aggregated manner. The model is tested both empirically and on a case study of internal displacement in northwest Syria. Computational results showed that considering the demographic structure in affected areas and reflecting it to the deprivation cost function helped to reach better prioritization in distribution of commodities. The rolling horizon methodology is also found to be efficient in solving large scale instances and in capturing the dynamic changes in demand and supply parameters.
Keywords: Humanitarian logistics | Possibilistic linear programming | Rolling horizon | Deprivation cost | Inventory allocation
مقاله انگلیسی
3 Buyer selection and service pricing in an electric fleet supply chain
انتخاب خریدار و قیمت گذاری خدمات در زنجیره تأمین ناوگان الکتریکی-2021
Much attention has been focused on supplier selection in operations. There has been less research on the supplier selecting buyers in a two-echelon supplier-buyer chain, which we study for downstream taxicab vehicle fleets. We consider the problem of pricing infrastructure services by an electric vehicles (EVs) service provider (SP), which determines the group of taxicab companies (TCs) that will adopt EVs. We study SP’s pricing decisions in a decentralized supply chain under a general infrastructure cost function, multiple TCs, and symmetric information. We extend the modeling to the case with (i) endogenous demand and EV-taxicab end-consumer pricing and (ii) asymmetric information between SP and TC. We analyze the factors that influence SP’s profits and the set of participating TCs who adopt EVs. We find that when the fleet size of TCs increases, SP prefers to serve more low-mile TCs than the high-mile TCs and even removes some high-mile TCs in exchange for low-mile TCs, where low and high-miles correspond to average miles driven in a time period (shift). When the coefficient of variation of miles driven increases, SP prefers to serve more high-mile TCs than the low-mile TCs. In general, the set of TCs that adopt EVs cannot be simply characterized using inputs such as average miles driven by different TCs. This study provides a modeling framework and managerial implications for TC selection and pricing contracts by an EV infrastructure service provider.
Keywords: Supply chain management | Buyer selection | Electric Vehicles | Service pricing | Submodular infrastructure cost
مقاله انگلیسی
4 Optimal selection and release problem in software testing process: A continuous time stochastic control approach
انتخاب بهینه و مشکل انتشار در فرآیند تست نرم افزار : یک روش کنترل تصادفی با زمان مداوم-2020
This paper studies a joint selection of test cases and release problem for a software under test with predetermined classes of test cases and release time deadline. The software test manager can make three alternative choices dynamically during software testing progress before the deadline: continue testing and select a class of test cases, release the software, or scrap the software, with the objective of minimizing the cumulative testing cost plus penalty cost after releasing or scrapping the software. We formulate the problem as a continuous time stochastic control model and provide a mathematically rigorous method to establish the concavity of the optimal cost function. Based on this property, we are able to characterize that the optimal release policy has a threshold structure. Moreover, the thresholds are founded to be monotone in the residual time length in the case of homogeneous release cost. Besides, we put forward a method based on low convex envelope and discover that the optimal selection policy also has a threshold or other simple structure, if the running cost or the removal cost is the same for all classes. Finally, we present an approximation algorithm of computing the optimal cost function, by which some numerical examples are studied to justify our theoretical results and the robustness of our policy. We also conduct a case study to compare our dynamic selection and release testing policy with two other commonly used testing policies and find that our policy is the best in most instances.
Keywords: Project management | Software testing process | Dynamic programming | Continuous time stochastic optimal control | Optimal software testing and release
مقاله انگلیسی
5 An optimal Gauss–Markov approximation for a process with stochastic drift and applications
تخمین بهینه گاوس-مارکوف برای یک فرآیند با رانش تصادفی و برنامه های کاربردی-2020
We consider a linear stochastic differential equation with stochastic drift. We study the problem of approximating the solution of such equation through an Ornstein–Uhlenbeck type process, by using direct methods of calculus of variations. We show that general power cost functionals satisfy the conditions for existence and uniqueness of the approximation. We provide some examples of general interest and we give bounds on the goodness of the corresponding approximations. Finally, we focus on a model of a neuron embedded in a simple network and we study the approximation of its activity, by exploiting the aforementioned results.
Keywords: Stochastic differential equations | Optimality conditions | Shot noise | Neuronal models
مقاله انگلیسی
6 Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation
سیستم مدیریت انرژی برای ریز شبکه هیبریدی PV-باد باتری با استفاده از برنامه نویسی محدب ، مدل پیش بینی و کنترل پیش بینی افق نورد با اعتبارسنجی آزمایشی-2020
The integration of energy storage technologies with renewable energy systems can significantly reduce the operating costs for microgrids (MG) in future electricity networks. This paper presents a novel energy management system (EMS) which can minimize the daily operating cost of a MG and maximize the self-consumption of the RES by determining the best setting for a central battery energy storage system (BESS) based on a defined cost function. This EMS has a two-layer structure. In the upper layer, a Convex Optimization Technique is used to solve the optimization problem and to determine the reference values for the power that should be drawn by the MG from the main grid using a 15 min sample time. The reference values are then fed to a lower control layer, which uses a 1 min sample time, to determine the settings for the BESS which then ensures that the MG accurately follows these references. This lower control layer uses a Rolling Horizon Predictive Controller and Model Predictive Controllers to achieve its target. Experimental studies using a laboratory-based MG are implemented to demonstrate the capability of the proposed EMS.
Keywords: Microgrid Energy Management | Battery Energy Storage System | Real-Time Battery Control | Convex Optimization | Model Predictive Control | Rolling Horizon Predictive Controller | Adaptive Autoregression Algorithm
مقاله انگلیسی
7 Standardised modelling and optimisation of a system of interconnected energy hubs considering multiple energies—Electricity, gas, heating, and cooling
مدل سازی استاندارد و بهینه سازی سیستم هاب های انرژی بهم پیوسته با توجه به انرژی های متعدد برق ، گاز ، گرمایش و سرمایش-2020
The system of interconnected energy hubs (EHs) is a key multi-carrier energy system (MES) model. It is difficult, however, to directly calculate the operational state of the model because of its highly dimensional nonlinear characteristics. To resolve the foregoing problem, a standardised modelling and optimisation method for the system of interconnected EH model is introduced in this paper. In this proposed method, one-dimensional and multi-dimensional piecewise linear approximation methods are adopted to simplify non-convex natural gas transmission functions, generator cost functions, and compressor function. Moreover, a multi-step linearisation method is applied to EHs. The whole system can accordingly be reformulated as a mixed-integer linear programming (MILP) problem. In contrast to the traditional model, the formulated MILP model is effortlessly implemented in the optimisation of the MES with existing advanced optimisation techniques. Finally, the method is verified using a modified three-hub interconnected system. The test results show that the method can save more than 90% computational time with sufficient accuracy. The results also demonstrate that the unified dispatch exploits different energy resources, and the applied energy storage devices can reduce the operational cost from $319,840.267 to $316,382.685.
Keywords: Multi-carrier energy systems | System of interconnected energy hubs | Energy storage | Energy management | Piecewise linear approximation method | Mixed-integer linear programming
مقاله انگلیسی
8 Cooperative online Guide-Launch-Guide policy in a target-missile-defender engagement using deep reinforcement learning
مشارکت آنلاین راهنمای راه اندازی-راهنمای مشارکت در دفاع از موشک-هدف با استفاده از یادگیری تقویتی عمیق-2020
A target-missile-defender engagement is considered, in which the missile attempts to intercept the target and the defender tries to prevent this interception via missile’s interception. In this engagement, finding an optimal launch time of the defender and an optimal target guidance law before and after launch, which can be formulated as a switched system optimization problem, is crucial for improving performance of the target-defender team. The objective of this paper is to examine the potential of using deep reinforcement learning in switched system optimization. To that end, we propose estimating the optimal launch time of the defender and the optimal guidance law of the target online, using a reinforcement learning based method. A policy suggesting at each decision time the bang-bang target maneuver and whether or not to launch the defender was obtained and analyzed via simulations. Simulations showed the ability of the reinforcement learning based method to obtain a close to optimal level of performance in terms of the suggested cost function.
مقاله انگلیسی
9 A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity
یک استراتژی مدیریت انرژی سلسله مراتبی برای ذخیره انرژی هیبریدی از طریق اتصال وسیله نقلیه به ابر-2020
In order to enhance energy efficiency and improve system performance, the road mobility system requires more preview information and advanced methods. This paper proposes a novel hierarchical optimal energy management strategy for electric buses with a battery/ultracapacitor hybrid energy storage system, to optimal split the power and reduce the battery life degradation. This method is based on vehicle-to-cloud connectivity. In the cloud platform, an optimal energy management strategy is developed using dynamic programming, where the battery degradation cost and the electric cost are taken into consideration. In the vehicle level, a model predictive control is developed to deal with the uncertainties, reduce the energy losses, and handle the system constraints. The cost function of the model predictive control includes the ultracapacitor state of charge planning and energy losses. In order to evaluate the effectiveness of the proposed method, a rule-based energy management strategy is developed as the baseline approach. The China bus driving cycle and other six real bus driving cycles recorded in China are used to validate the robustness of the proposed method. To be more realistic, the random uncertainties up to 20% are included in all driving cycles. Furthermore, the time delay and packet losses in communication are also considered. Simulation results show that the proposed method significantly outperforms the rule-based method, and the average improvement could be over 40% in the studied driving cycles.
Keywords: Vehicle-to-cloud connectivity | Energy management | Model predictive control | Real-time optimization | Hybrid energy storage
مقاله انگلیسی
10 High strain rate micro-compression for crystal plasticity constitutive law parameters identification
میزان فشار بالا فشرده سازی میکرو برای شناسایی پارامترهای قانون سازنده انعطاف پذیری کریستال-2020
Microcompression tests were performed on single crystal copper micropillars at 10−2 s−1 and 102 s−1 in the [100], [110] and [111] orientations, to provide inputs for crystal plasticity strain rate sensitive parameters inverse identification. The identification procedure used full pillar geometry finite element simulations. An identifiability indicator based on the cost functions hessian matrix approximate close to the minimum was used to assess the uniqueness and stability of the identified coefficients. Experimental microcompression tests displayed a strain rate sensitive behaviour in the [100] crystal orientation. The [110] and [111] orientations were less sensitive and were not used for identification. Stress-strain curve sensitivity plots revealed that the higher the strain rate, the better the sensitivity. This was attributed to high strain rates concentration in the shear bands as the strain rate increases. Identification on experimental data well represented the single crystal strain rate sensitivity in the [100] orientations. A unique solution was found using only a single orientation.
Keywords: Micropillar compression | Crystal plasticity finite element | Inverse identification | Identifiability analysis
مقاله انگلیسی
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