On the Cost-Optimality Trade-off for Service Function Chain Reconfiguration

Shift to The Front II Komplek Sukamukti Banjaran Project is one of the projects implemented by one of the companies engaged in telecommunications. In its implementation, each project including Shift to The Front II Komplek Sukamukti Banjaran has a time limit specified in the contract. Project scheduling is an important role in predicting both the cost and time in a project. Every project should be able to complete the project before or just in the time specified in the contract. Delay in a project can be anticipated by accelerating the duration of completion by using the crashing method with the application of linear programming. Linear programming will help iteration in the calculation of crashing because if linear programming not used, iteration will be repeated. The objective function in this scheduling is to minimize the cost. This study aims to find a trade-off between the costs and the minimum time expected to complete this project. The acceleration of the duration of this study was carried out using the addition of 4 hours of overtime work, 3 hours of overtime work, 2 hours of overtime work, and 1 hour of overtime work. The normal time for this project is 35 days with a service fee of Rp. 52,335,690. From the results of the crashing analysis, the alternative chosen is to add 1 hour of overtime to 34 days with a total service cost of Rp. 52,375,492. This acceleration will affect the entire project because there are 33 different locations worked on Shift to The Front II and if all these locations can be accelerated then the duration of completion of the entire project will be effective

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Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls

Sustainability ◽

10.3390/su12072767 ◽

2020 ◽

Vol 12 (7) ◽

pp. 2767 ◽

Cited By ~ 13

Author(s):

Víctor Yepes ◽

José V. Martí ◽

José García

Keyword(s):

Black Hole ◽

Sustainable Design ◽

Retaining Walls ◽

The Other ◽

Trade Off ◽

Construction Company ◽

Geometric Variables ◽

The Stability ◽

The Cost ◽

Black Hole Algorithm

The optimization of the cost and CO 2 emissions in earth-retaining walls is of relevance, since these structures are often used in civil engineering. The optimization of costs is essential for the competitiveness of the construction company, and the optimization of emissions is relevant in the environmental impact of construction. To address the optimization, black hole metaheuristics were used, along with a discretization mechanism based on min–max normalization. The stability of the algorithm was evaluated with respect to the solutions obtained; the steel and concrete values obtained in both optimizations were analyzed. Additionally, the geometric variables of the structure were compared. Finally, the results obtained were compared with another algorithm that solved the problem. The results show that there is a trade-off between the use of steel and concrete. The solutions that minimize CO 2 emissions prefer the use of concrete instead of those that optimize the cost. On the other hand, when comparing the geometric variables, it is seen that most remain similar in both optimizations except for the distance between buttresses. When comparing with another algorithm, the results show a good performance in optimization using the black hole algorithm.

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Hybrid last mile delivery fleets with crowdsourcing: A systems view of managing the cost‐service trade‐off

Journal of Business Logistics ◽

10.1111/jbl.12288 ◽

2021 ◽

Author(s):

Vincent E. Castillo ◽

John E. Bell ◽

Diane A. Mollenkopf ◽

Theodore P. Stank

Keyword(s):

Service Trade ◽

Trade Off ◽

Last Mile ◽

The Cost ◽

Last Mile Delivery ◽

Systems View

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GCN-TD: A Learning-based Approach for Service Function Chain Deployment on the Fly

GLOBECOM 2020 - 2020 IEEE Global Communications Conference ◽

10.1109/globecom42002.2020.9322359 ◽

2020 ◽

Author(s):

Pan Pan ◽

Qilin Fan ◽

Sen Wang ◽

Xiuhua Li ◽

Jian Li ◽

...

Keyword(s):

Service Function ◽

Service Function Chain

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Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Scientific Reports ◽

10.1038/s41598-021-82642-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jeonghyuk Park ◽

Yul Ri Chung ◽

Seo Taek Kong ◽

Yeong Won Kim ◽

Hyunho Park ◽

...

Keyword(s):

Cancer Detection ◽

Morphological Analysis ◽

Deep Neural Networks ◽

Large Datasets ◽

Histopathological Diagnosis ◽

Single Model ◽

Trade Off ◽

Detection Model ◽

Optimal Behavior ◽

The Cost

AbstractThere have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an aggregate of all cohorts when there are only a few images available for the target cohort. A trade-off between decreasing the number of models and their cancer detection performance was evident in our experiments with The Cancer Genomic Atlas dataset, with the former approach achieving higher performance at the cost of having to acquire large datasets from the cohort of interest. Constructing annotated datasets for individual cohorts is extremely time-consuming, with the acquisition cost of such datasets growing linearly with the number of cohorts. Another issue associated with developing cohort-specific models is the difficulty of maintenance: all cohort-specific models may need to be adjusted when a new DL algorithm is to be used, where training even a single model may require a non-negligible amount of computation, or when more data is added to some cohorts. In resolving the sub-optimal behavior of a universal cancer detection model trained on an aggregate of cohorts, we investigated how cohorts can be grouped to augment a dataset without increasing the number of models linearly with the number of cohorts. This study introduces several metrics which measure the morphological similarities between cohort pairs and demonstrates how the metrics can be used to control the trade-off between performance and the number of models.

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A trade-off between classical and quantum circuit size for an attack against CSIDH

Journal of Mathematical Cryptology ◽

10.1515/jmc-2020-0070 ◽

2020 ◽

Vol 15 (1) ◽

pp. 4-17

Author(s):

Jean-François Biasse ◽

Xavier Bonnetain ◽

Benjamin Pring ◽

André Schrottenloher ◽

William Youmans

Keyword(s):

Endomorphism Ring ◽

State Of The Art ◽

Quantum Circuit ◽

List Type ◽

Hard Problem ◽

Trade Off ◽

Classical State ◽

Security Parameter ◽

The Cost ◽

Quadratic Order

AbstractWe propose a heuristic algorithm to solve the underlying hard problem of the CSIDH cryptosystem (and other isogeny-based cryptosystems using elliptic curves with endomorphism ring isomorphic to an imaginary quadratic order 𝒪). Let Δ = Disc(𝒪) (in CSIDH, Δ = −4p for p the security parameter). Let 0 < α < 1/2, our algorithm requires:A classical circuit of size $2^{\tilde{O}\left(\log(|\Delta|)^{1-\alpha}\right)}.$A quantum circuit of size $2^{\tilde{O}\left(\log(|\Delta|)^{\alpha}\right)}.$Polynomial classical and quantum memory.Essentially, we propose to reduce the size of the quantum circuit below the state-of-the-art complexity $2^{\tilde{O}\left(\log(|\Delta|)^{1/2}\right)}$ at the cost of increasing the classical circuit-size required. The required classical circuit remains subexponential, which is a superpolynomial improvement over the classical state-of-the-art exponential solutions to these problems. Our method requires polynomial memory, both classical and quantum.

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