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نتیجه جستجو - Heuristics

تعداد مقالات یافته شده: 75
ردیف عنوان نوع
1 Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
لایه VQE: یک رویکرد متغیر برای بهینه سازی ترکیبی در کامپیوترهای کوانتومی پر سر و صدا-2022
Combinatorial optimization on near-term quantum devices is a promising path to demonstrating quantum advantage. However, the capabilities of these devices are constrained by high noise or error rates. In this article, inspired by the variational quantum eigensolver (VQE), we propose an iterative layer VQE (L-VQE) approach. We present a large-scale numerical study, simulating circuits with up to 40 qubits and 352 parameters, that demonstrates the potential of the proposed approach. We evaluate quantum optimization heuristics on the problem of detecting multiple communities in networks, for which we introduce a novel qubit-frugal formulation. We numerically compare L-VQE with the quantum approximate optimization algorithm (QAOA) and demonstrate that QAOA achieves lower approximation ratios while requiring significantly deeper circuits. We show that L-VQE is more robust to finite sampling errors and has a higher chance of finding the solution as compared with standard VQE approaches. Our simulation results show that L-VQE performs well under realistic hardware noise.
INDEX TERMS: Combinatorial optimization | hybrid quantum-classical algorithm | quantum optimization.
مقاله انگلیسی
2 Attention-based model and deep reinforcement learning for distribution of event processing tasks
مدل مبتنی بر توجه و یادگیری تقویتی عمیق برای توزیع وظایف پردازش رویداد-2022
Event processing is the cornerstone of the dynamic and responsive Internet of Things (IoT). Recent approaches in this area are based on representational state transfer (REST) principles, which allow event processing tasks to be placed at any device that follows the same principles. However, the tasks should be properly distributed among edge devices to ensure fair resources utilization and guarantee seamless execution. This article investigates the use of deep learning to fairly distribute the tasks. An attention-based neural network model is proposed to generate efficient load balancing solutions under different scenarios. The proposed model is based on the Transformer and Pointer Network architectures, and is trained by an advantage actorcritic reinforcement learning algorithm. The model is designed to scale to the number of event processing tasks and the number of edge devices, with no need for hyperparameters re-tuning or even retraining. Extensive experimental results show that the proposed model outperforms conventional heuristics in many key performance indicators. The generic design and the obtained results show that the proposed model can potentially be applied to several other load balancing problem variations, which makes the proposal an attractive option to be used in real-world scenarios due to its scalability and efficiency.
keywords: Web of Things (WoT) | Representational state transfer (REST) | application programming interface (APIs) | Edge computing | Load balancing | Resource placement | Deep reinforcement leaning | Transformer model | Pointer networks | Actor critic
مقاله انگلیسی
3 Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic
استفاده از اینترنت اشیا برای طراحی شبکه زنجیره تأمین امداد برای همه گیری SARS-COV-2-2021
The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare system for both better understanding of the patient’s condition and appropriate monitoring in a remote fashion. However, there has always been a gap for utilizing this approach on the healthcare system especially in agitated condition of the pandemics. Therefore, in this study, we develop two innovative approaches to design a relief supply chain network is by using IoT to address multiple suspected cases during a pandemic like the SARS-COV-2 outbreak. The first approach (prioritizing approach) minimizes the maximum ambulances response time, while the second approach (allocating approach) minimizes the total critical response time. Each approach is validated and investigated utilizing several test problems and a real case in Iran as well. A set of efficient meta-heuristics and hybrid ones is developed to optimize the proposed models. The proposed approaches have shown their versatility in various harsh SARS-COV-2 pandemic situations being dealt with by managers. Finally, we compare the two proposed approaches in terms of response time and route optimization using a real case study in Iran. Implementing the proposed IoT-based methodology in three consecutive weeks, the results showed 35.54% decrease in the number of confirmed cases.© 2021 Elsevier B.V. All rights reserved.
Keywords: Supply chain design | Epidemic outbreaks | Industry 4.0 | COVID-19 | SARS-COV-2
مقاله انگلیسی
4 Optimization of extended business processes in digital supply chains using mathematical programming
بهینه سازی فرآیندهای تجاری گسترده در زنجیره های تأمین دیجیتال با استفاده از برنامه ریزی ریاضی-2021
We propose a mathematical programming approach to optimize the business process transactions in digital supply chains. Five scheduling models from the Process Systems Engineering (PSE) area are applied to schedule the processing of orders in a simplified Order-To-Cash (OTC) business process, which is modeled as a multistage network with parallel units (agents). Two case studies are presented to compare the performance of the scheduling models on various sizes of a flexible jobshop representation of the OTC process. The models are compared and scaled to select those that are more suitable to this application. The continuous-time general precedence model provides an accurate representation of the real system and performs well for small instances. The discrete-time State-Task Network (STN), however, proves most efficient in terms of tractability, despite the well-known limitations resulting from discretizing time. The tightness of the linear programming (LP) relaxations in the discrete-time STN framework, as well as the ability of commercial solvers to perform preprocessing and apply heuristics to the STN formulation, enables finding near optimal solutions quickly even for larger instances.
Keywords: Business process optimization | digital supply chain | order-to-cash | scheduling | mathematical programming
مقاله انگلیسی
5 Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization
زنجیره تامین حلقه بسته پایدار برای صنایع لبنی با بهینه سازی قوی و ابتکاری-2021
This paper supplements the augmented ε-constraint approach with linearization using robust optimization and heuristics with an improved algorithm to maximize the total profit and minimize the environmental effects of a sustainable closed-loop supply chain (CLSC) in the dairy industry. The resultant mixed-integer linear programming (MILP) model is applied to a case from the dairy industry and evaluated against several test problems. The pessimistic, optimistic, and worst-case scenarios are considered along with the sensitivity analysis on the profitability of the CLSC concerning the product lifetimes. Our results inform that applying the heuristic on large- scale problems yields a 25% improvement in runtime. Furthermore, products with a longer lifetime under the worst-case scenario yield greater profit than those products with a shorter lifetime under an optimistic scenario.
Keywords: Robust optimization | Closed-loop supply chain | Augmented ε-constraint | Diary
مقاله انگلیسی
6 A set of efficient heuristics and meta-heuristics to solve a multi-objective pharmaceutical supply chain network
مجموعه ای از روشهای اکتشافی و کارآیی کارآمد برای حل یک شبکه زنجیره تامین دارویی چند هدفه-2021
In this paper, we propose a new multi-objective optimization approach for the pharmaceutical supply chain network (PSCN) design problem to minimize the total cost and the delivery time of pharmaceutical products to the hospital and pharmacy, while maximizing the reliability of the transportation system. A new mixed-integer non-linear programming model was developed for the production-allocation-distribution-inventory-ordering- routing problem. Three new heuristics (H-1), (H-2), and (H-3) have been proposed and to validate the model, two new meta-heuristic algorithms, namely, an Improved Social Engineering Optimization (ISEO) and Hybrid Firefly and Simulated Annealing Algorithm (HFFA-SA) have been developed. The proposed mathematical model has been evaluated through extensive simulation experiments by analyzing different criteria. The results show that the proposed model along with the solution method provides a reliable and powerful instrument to solve the PSCN design problem.
Keywords: Pharmaceutical supply chain network | Heuristic algorithms | Improved social engineering optimization | Hybrid firefly and simulated annealing | algorithm | Multi-objective optimization
مقاله انگلیسی
7 Combining Production and Distribution in Supply Chains: the Hybrid Flow-Shop Vehicle Routing Problem
ترکیب تولید و توزیع در زنجیره های تأمین: مشکل مسیریابی وسایل نقلیه فروشگاه های گلخانه ای ترکیبی-2021
Many supply chains are composed of producers, suppliers, carriers, and customers. These agents must be coordinated to reduce waste and lead times. Production and distribution are two essential phases in most supply chains. Hence, improving the coordination of these phases is critical. This paper studies a combined hybrid flow-shop and vehicle routing problem. The production phase is modeled as a hybrid flow-shop configuration. In the second phase, the produced jobs have to be delivered to a set of customers. The delivery is carried out in batches of products, using vehicles with a limited capacity. With the objective of minimizing the service time of the last customer, we propose a biased-randomized variable neighborhood descent algorithm. Different test factors, such as the use of alternative initial solutions, solution representations, and loading strategies, are considered and analyzed.
Keywords: hybrid flow-shop problem | vehicle routing problem | biased randomization | metaheuristics
مقاله انگلیسی
8 DATA BREACH MANAGEMENT: AN INTEGRATED RISK MODEL
مدیریت نقض داده ها: یک مدل ریسک یکپارچه-2021
In response to organizations’ increasing vulnerability to data breaches, we present an integrated risk model for data breach management based on a systematic review of the literature. Theoretically, the study extends the body of knowledge on data breach management by identifying and updating conceptualizations of data breach risks (items) and resolutions (actions) and by providing a foundation for organizational responses to emerging data breach incidents (heuristics). Practically, the study provides key insights that practitioners can use to organize and orchestrate effective data breach management based on comprehensive profiles of risk items and resolution techniques.
keywords: نقض داده | مدیریت ریسک | مدل ریسک یکپارچه | مدیریت حادثه | تجزیه و تحلیل ادبیات | Data breach | risk management | integrated risk model | incident management | literature analysis
مقاله انگلیسی
9 Pharmaceutical R & D pipeline management under trial duration uncertainty
مدیریت خط لوله تحقیق و توسعه دارویی تحت عدم قطعیت آزمایش-2020
We consider a pharmaceutical Research & Development (R & D) pipeline management problem under two significant uncertainties: the outcomes of clinical trials and their durations. We present an Approximate Dynamic Programming (ADP) approach to solve the problem efficiently. Given an initial list of potential drug candidates, ADP derives a policy that suggests the trials to be performed at each decision point and state. For the classical R&D pipeline planning problem with deterministic trial durations, we compare our ADP approach with other methods from the literature, and find that it can find better solutions more quickly in particular for larger problem instances. For the case with stochastic trial durations, we compare the ADP algorithm with a myopic approach and show that the expected net profit obtained by the derived ADP policy is higher (almost 20% for a 10-drug portfolio).
Keywords: Dynamic programming | Pharmaceutical R&D pipeline management | Heuristics | Approximate dynamic programming | Project scheduling
مقاله انگلیسی
10 How Entrepreneurs make sense of Lean Startup Approaches: Business Models as cognitive lenses to generate fast and frugal Heuristics
چگونه کارآفرینان رویکردهای راه اندازی ناب را احساس می کنند: مدل های کسب و کار به عنوان لنزهای شناختی برای تولید اکتشافی سریع و مقرون به صرفه-2020
The role of the business model (BM) as heuristics to support entrepreneurial and strategic problem solving at a cognitive level has been hinted at by extant literature, but left largely unexplored as of yet. This study is po- sitioned in the emerging research on the cognitive individual microfoundations of Entrepreneurship and Strategy, and contributes to the discussion of how business models are used as heuristics, in the novel and relevant setting of Digital Entrepreneurship. We conducted a multiple case study on three digital startups that applied the emerging Lean Startup Approaches (LSAs) and embody technological development in their value proposition. We found that digital entrepreneurs applying LSAs as a systematic process to validate their business ideas rely on business models as cognitive lenses to make sense of LSAs and translate abstract guidelines into fast and frugal heuristics, in order to ‘make do’ with cognitive resource scarcity. These BM-generated heuristics in turn help entrepreneurs in the activities of: (i) making sense of entrepreneurial opportunities; (ii) formulating falsifiable hypotheses concerning their startups’ viability; (iii) filtering, selecting and organizing fuzzy and in- complete external and internal information; (iv) designing multidimensional customer experiments and tests revolving around the notion of value, through Minimum Viable Business Models (MVBMs); (v) prioritizing these experiments and tests to validate their early BM through analogical arguments; and (vi) processing the learning they obtain from experiments, and concretizing it in the form of BM pivots. We also provide empirically-driven insight on an integrative set of cognitive processes – namely (1) cognitive imprinting, (2) common language transfer; (3) attention intensity and (4) scientific and experimental cognition – that mold and blend together the BM-generated heuristics and explain how they are learnt, transferred, enacted, and how they persistently enablea cognitive transition to the application of a scientific method to Entrepreneurship based on more sophisticated experiments and metrics.«Non sunt multiplicanda entia sine necessitate» (Entities are not to be multiplied without necessity)William of Ockham (c. 1287–1347)
Keywords: Business Model | Heuristic | Lean Startup Approaches | Microfoundations | Entrepreneurship | Experimentation | Digital Entrepreneurship | Scientific method
مقاله انگلیسی
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