با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Fault-Tolerant Coherent H∞ Control for Linear Quantum Systems
کنترل منسجم H∞ مقاوم در برابر خطا برای سیستم های کوانتومی خطی-2022 Robustness and reliability are two key requirements for developing practical quantum control systems.
The purpose of this article is to design a coherent feedback
controller for a class of linear quantum systems suffering from Markovian jumping faults so that the closed-loop
quantum system has both fault tolerance and H∞ disturbance attenuation performance. This article first extends
the physical realization conditions from the time-invariant
case to the time-varying case for linear stochastic quantum
systems. By relating the fault-tolerant H∞ control problem
to the dissipation properties and the solutions of Riccati
differential equations, an H∞ controller for the quantum
system is then designed by solving a set of linear matrix inequalities. In particular, an algorithm is employed to introduce additional quantum inputs and to construct the corresponding input matrices to ensure the physical realizability
of the quantum controller. Also, we propose a real application of the developed fault-tolerant control strategy to
quantum optical systems. A linear quantum system example from quantum optics, where the amplitude of the pumping field randomly jumps among different values due to
the fault processes, can be modeled as a linear Markovian
jumping system. It is demonstrated that a quantum H∞
controller can be designed and implemented using some
basic optical components to achieve the desired control
goal for this class of systems.
Index Terms: Coherent feedback control | fault-tolerant quantum control | H∞ control | linear quantum systems | quantum controller. |
مقاله انگلیسی |
2 |
Learning to Learn Variational Quantum Algorithm
آموزش یادگیری الگوریتم کوانتومی متغیر-2022 Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and
update the circuit parameters to obtain an approximate ground
state. In this article, we present a meta-learning variational
quantum algorithm (meta-VQA) by recurrent unit, which uses
a technique called “meta-learner.” Motivated by the hybrid
quantum-classical algorithms, we train classical recurrent units
to assist quantum computing, learning to find approximate
optima in the parameter landscape. Here, aiming to reduce the
sampling number more efficiently, we use the quantum stochastic
gradient descent method and introduce the adaptive learning
rate. Finally, we deploy on the TensorFlow Quantum processor
within approximate quantum optimization for the Ising model
and variational quantum eigensolver for molecular hydrogen
(H2), lithium hydride (LiH), and helium hydride cation (HeH+).
Our algorithm can be expanded to larger system sizes and
problem instances, which have higher performance on near-term
processors.
Index Terms: Meta-learning | quantum algorithm | quantum computing | quantum information | quantum machine learning(QML). |
مقاله انگلیسی |
3 |
Quantum computing in power systems
محاسبات کوانتومی در سیستم های قدرت-2022 Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to
successfully operating large transmission and distribution systems. However, today’ s power systems are suffering from everincreasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources,
as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support
the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes
of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally
intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the
newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing
quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic
analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations,
hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure
communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will
hopefully stimulate increasing attention to the development of quantum-engineered smart grids.
keywords: Quantum computing | power system | variational quantum algorithms | quantum optimization | quantum machine learning | quantum security. |
مقاله انگلیسی |
4 |
Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
آموزش طبقهبندیکنندههای ترکیبی کلاسیک-کوانتومی از طریق بهینهسازی تغییرات تصادفی-2022 Quantum machine learning has emerged as a potential practical application of near-term quantum devices. In this
work, we study a two-layer hybrid classical-quantum classifier
in which a first layer of quantum stochastic neurons implementing generalized linear models (QGLMs) is followed by a second
classical combining layer. The input to the first, hidden, layer is
obtained via amplitude encoding in order to leverage the exponential size of the fan-in of the quantum neurons in the number of
qubits per neuron. To facilitate implementation of the QGLMs, all
weights and activations are binary. While the state of the art on
training strategies for this class of models is limited to exhaustive
search and single-neuron perceptron-like bit-flip strategies, this
letter introduces a stochastic variational optimization approach
that enables the joint training of quantum and classical layers via
stochastic gradient descent. Experiments show the advantages of
the approach for a variety of activation functions implemented by
QGLM neurons.
Index Terms: Probabilistic machine learning | quantum computing | quantum machine learning. |
مقاله انگلیسی |
5 |
Efficient and sustainable closed-loop supply chain network design: A two-stage stochastic formulation with a hybrid solution methodology
طراحی شبکه زنجیره تامین حلقه بسته کارآمد و پایدار: یک فرمول تصادفی دو مرحله ای با روش راه حل ترکیبی-2021 In recent years, consumers and legislators have pushed companies to design their supply chain networks to consider environmental and social impacts as an important performance outcome. Due to the role of resource utilization as a key component of logistics network design, another primary goal of design is ensuring available scarce resources are used as efficiently as possible across all facilities. To address efficiency issues in a sustainable closed-loop supply chain network, a stochastic integrated multi-objective mixed integer nonlinear programming model is developed in this paper, in which sustainability outcomes as well as efficiency of facility resource utilization are considered in the design of a sustainable supply chain network. In doing so, efficiency is assessed using a bi-objective output-oriented data envelopment analysis model. A hybrid three-step solution methodology is presented that creates a linear form of the original mixed integer nonlinear programming problem using piecewise McCormick envelopes approach. In the second step, an aggregated single objective programming model is derived by exploiting the multi-choice goal programming. Finally, a Lagrangian relaxation algorithm is developed to effectively solve the latter stochastic single objective mixed integer linear programming problem. The application of the proposed approach is investigated with data drawn from a case study in the electronics industry. This case study illustrates how firms may balance sustainability and efficiency in the supply chain network design problem. Further, it demonstrates the integration of efficiency results in improving economic aspects of sustainability as well as social responsibility outcomes, but also highlights the trade-offs that exist between efficiency and environmental impacts. Keywords: Closed-loop supply chain network | Sustainability | Data envelopment analysis | Stochastic programming | Multi-choice goal programming | Lagrangian relaxation |
مقاله انگلیسی |
6 |
A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network
یک مدل تصادفی محکم فازی چند هدفه برای طراحی یک شبکه زنجیره تأمین پایدار ، قابل انعطاف و پاسخگو-2021 This study proposes a multi-objective mixed-integer programming model to configure a sustainable supply chain network while considering resilience and responsiveness measures. The model aims at minimizing the total costs and environmental damages while maximizing the social impacts, as well as the responsiveness and resilience levels of the supply chain network. An improved version of the fuzzy robust stochastic optimization approach is proposed to tackle the uncertain data arising in the dynamic business environment. Furthermore, a new version of meta-goal programming named the multi-choice meta-goal programming associated with a utility function is developed to solve the resulting multi-objective model. A case study in the water heater industry is investigated to illustrate the application of the proposed model and its solution approach. The numerical results validate the proposed model and the developed solution method. Finally, interactions between the sustainability, responsiveness, and resilience dimensions are investigated and several sensitivity analyses are performed on critical parameters by which useful managerial insights are provided. Keywords: Supply chain network design | Sustainability | Resilience | Responsiveness | Fuzzy robust stochastic optimization | Multi-choice meta-goal programming |
مقاله انگلیسی |
7 |
A multi-objective robust optimization model for upstream hydrocarbon supply chain
یک مدل بهینه سازی قوی چند هدفه برای زنجیره تأمین هیدروکربن بالادست-2021 The hydrocarbon supply chain (HCSC) is a significant part of the world’s energy sector.
The energy market has experienced erratic behavior over the last few years results in financial risks
such as exceeding certain limits of the budget or not achieving the desired levels of cash in-flow, i.e.,
revenue. In this work, robust optimization and multi-objective mathematical programming are used
to develop a model that eliminates or at least mitigates the impact of uncertain market behavior.
Robust optimization provides tactical plans that are feasible and robust over market scenarios.
The model assesses the trade-offs between alternatives and guides the decision-maker towards
the effective management of the HCSC. The economic objectives are to minimize total cost and
maximize revenue, while the non-economic objective is to minimize the depletion rate. The model
considers the environmental aspect by limiting the emission of CO2 and the sustainability aspect by
reducing the depletion rate of natural resources. Uncertain behavior of the oil market is modeled on
scenario representation. A case study based on real data from Saudi Arabia HCSC is provided to
demonstrate the model’s practicality, and a sensitivity analysis is conducted to provide some managerial insights. The results indicate that Saudi Arabia can cover its entire expenditure, break-evenpoint, by producing oil at 7.18 MMbbld and gas at 3,543.48 MMcftd. Besides, the robust approach
provides a preferred plan with the highest cash inflow and the lowest sustainability over other
approaches, e.g., deterministic, stochastic, and risk-based. The differences show that the robust
model increases oil production to compensate for the variability of the scenario.
KEYWORDS: Hydrocarbon supply chain | Multi-objective optimization | Robust optimization | Scenario-Based Optimization | Tactical planning |
مقاله انگلیسی |
8 |
The optimal recovery-fund based strategy for uncertain supply chain disruptions: A risk-averse two-stage stochastic programming approach
استراتژی مبتنی بر صندوق بازیابی بهینه برای اختلالات نامشخص زنجیره تأمین: رویکرد برنامه ریزی تصادفی دو مرحله ای ریسک پذیر-2021 For a supply chain subject to uncertain production disruptions, the joint optimization of invest- ment intervention on recovery speed and duration of disrupted production capacity and location and inventory management has not been well studied. In this paper, a novel recovery strategy is introduced and studied, which uses investment to adjust the recovery speed and duration of production capacity, and two recovery behaviors responding to different types of disruptions are modeled. Considering uncertain disruption scenarios and their ripple effects over the supply chain, a risk-averse two-stage stochastic programming model (RTSPM) is established to study the integrated supply chain management of selection of distribution centers, multi-period inventory, transportation flows, and recovery-fund based mitigation policy. The RTSPM incorporates the risk preference of managers in decision making. We propose a trust-region-based decomposition method to solve the RTSPM and demonstrate its efficiency by benchmarking on state-of-the-art commercial solvers. Through numerical examples, we deeply analyze the effectiveness of RTSPM and the relations of optimal recovery investment decisions with the uncertain disruption factors. Finally, we provide implications and suggestions induced from the models and findings to aid the decisions on renting of distribution centers and the emergency investment and operational decisions when suffering the disruptions. Keywords: Supply chain disruption management | Recovery-fund based mitigation strategy | Location-inventory-transportation model | Risk-averse two-stage stochastic programming | Trust-region-based decomposition method |
مقاله انگلیسی |
9 |
Data, data flows, and model specifications for linking multi-level contribution margin accounting with multi-level fixed-charge problems
دادهها، جریانهای داده، و مشخصات مدل برای پیوند حسابداری حاشیه سهم چندسطحی با مشکلات شارژ ثابت چندسطحی-2021 This article describes the data, data flows, and spreadsheet
implementations for linking multi-level contribution margin
accounting as a subsystem in cost accounting with several
versions of a multi-level fixed-charge problem (MLFCP), the
latter based on the optimization approach in operations research. This linkage can reveal previously hidden optimization potentials within the framework of multi-level contribution margin accounting, thus providing better information for decision making in companies and other organizations. For the data, plausible fictitious values have been assumed taking into consideration the calculation principles
in cost accounting where applicable. They include resourcerelated data, market-related data, and data from cost accounting needed to analyze the profitability of a companys´
products and organizational entities in the presence of hierarchically structured fixed costs. The data are processed and
analyzed by means of mathematical optimization techniques
and sensitivity analysis. The linkage between multi-level contribution margin accounting and MLFCP is implemented in
three spreadsheet files, including versions for deterministic
optimization, stochastic optimization, and robust optimization. This paper provides specifications for compatible solver
add-ins and for executing sensitivity analysis. The data and spreadsheet implementations described in this article were
used in a research article entitled “Making better decisions
by applying mathematical optimization to cost accounting:
An advanced approach to multi-level contribution margin accounting” [1]. The data sets and the spreadsheet implementations may be reused a) by researchers in management and
cost accounting as well as in operations research and quantitative methods for verification and for further development
of the linkage concept and of the underlying optimization
models; b) by practitioners for gaining insight into the data
requirements, methods, and benefits of the proposed linkage,
thus supporting continuing education; and c) by instructors
in academia who may find the data and spreadsheets valuable for classroom use in advanced courses. The complete
spreadsheet implementations in the form of three ready-touse Excel files (deterministic, stochastic, and robust version)
are available for download at Mendeley Data. They may serve
as customizable templates for various use cases in research,
practice, and education.
keywords: حسابداری هزینه | تحقیق در عملیات | مشکل ثابت شارژ | بهینه سازی | برنامه نویسی صحیح | تجزیه و تحلیل میزان حساسیت | بهینه سازی تصادفی | صفحه گسترده | Cost accounting | Operations research | Fixed-charge problem | Optimization | Integer programming | Sensitivity analysis | Stochastic optimization | Spreadsheet |
مقاله انگلیسی |
10 |
Making better decisions by applying mathematical optimization to cost accounting: An advanced approach to multi-level contribution margin accounting
تصمیم گیری های بهتر را با استفاده از بهینه سازی ریاضی به هزینه حسابداری: یک رویکرد پیشرفته به حسابداری حاشیه کمک چند سطح-2021 The purpose of multi-level contribution margin accounting in cost accounting is to analyze the profitability of
products and organizational entities with appropriate allocation of fixed costs and to provide relevant information
for short-term, medium- and longer-term decisions. However, the conventional framework of multi-level
contribution margin accounting does not usually incorporate a mathematical optimization method that simultaneously integrates variable and fixed costs to determine the best possible product mix within hierarchically
structured organizations. This may be surprising in that operations research provides an optimization model in the
form of the fixed-charge problem (FCP) that takes into account not only variable costs but also fixed costs of the
activities to be planned. This paper links the two approaches by expanding the FCP to a multi-level fixed-charge
problem (MLFCP), which maps the hierarchical decomposition of fixed costs in accordance with multi-level
contribution margin accounting. In this way, previously hidden optimization potentials can be made visible
within the framework of multi-level contribution margin accounting. Applying the linkage to a case study illustrates that the original assessment of profitability gained on the sole basis of a multi-level contribution margin
calculation might turn out to be inappropriate or even inverted as soon as mathematical optimization is utilized:
products, divisions, and other reference objects for fixed cost allocation, which at first glance seem to be profitable
(or unprofitable) might be revealed as actually unprofitable (or profitable), when the multi-level contribution
margin calculation is linked to the MLFCP. Furthermore, the proposed concept facilitates assessment of the costs
of an increasing variant diversity, which also demonstrates that common rules on how to interpret a multi-level
contribution margin calculation may have to be revised in some cases from the viewpoint of optimization. Finally,
the impact of changes in the fixed cost structure and other parameters is tested via sensitivity analyses and
stochastic optimization.
keywords: حسابداری هزینه | حد مشارکت، محدوده مشارکت | هزینه های ثابت | نرم افزار | مخلوط محصول | تصمیم گیری | تحقیق در عملیات | مشکل ثابت شارژ | مشکل چند سطح قابل شارژ | بهینه سازی | برنامه نویسی صحیح | تجزیه و تحلیل میزان حساسیت | بهینه سازی تصادفی | صفحه گسترده | مطالعه موردی | Cost accounting | Contribution margin | Fixed costs | Profitability | Product mix | Decision making | Operations research | Fixed-charge problem | Multi-level fixed-charge problem | Optimization | Integer programming | Sensitivity analysis | Stochastic optimization | Spreadsheet | Case study |
مقاله انگلیسی |