Herman Mawengkang
Department of Mathematics, Universitas Sumatera Utara, Indonesia

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Neuron Model for Input Uncertainty Zulfian Azmi; Erna B N; Herman Mawengkang; M Zarlis
Journal of Telematics and Informatics Vol 6, No 2: June 2018
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jti.v6i2.

Abstract

The application of the Neuron Network model has not given optimal results on learning with input values ​​that are not binary, uncertain and varied. Variable inputs are not only 1 and 0 but allow between 0 and 1. and linguistic input and output and non-linear models. And the verification process for reviewing feasibility is reviewed from network, unit, behavior and procedural aspects. Further validation is done on the control module of the waterwheel rotation with dissolved oxygen input, water pH, salinity and water temperature varies. With such neuron models being the solution to varied and uncertain neuron models. The simulation is done withMatrix Laboratory software. Keywords: Neuron, Uncertainty, Waterwheel.
Mathematical Model for Vehicle Routing and Scheduling with Forward and Reverse Logistics Lady Ichwana Resti; Herman Mawengkang; Elly Rosmaini
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2023): Article Research Volume 8 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12599

Abstract

Companies usually use cross-docking to reduce logistics costs. The product delivery process from suppliers to retailers and vice versa is facilitated by crossdocking facilities. One of important problem in crossdocking is vehicle routes. In this work we discuss about cross-docking problem for vehicle routes which is brought into the form of an integration model. We also present the strategy to handle the forward and reverse logistics. From this strategy we have a NP-hard mathematical model as the result.
Simplifying Complexity: Scenario Reduction Techniques in Stochastic Programming Christian Sinaga; Tulus Tulus; Herman Mawengkang
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2023): Article Research Volume 8 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12753

Abstract

Stochastic programming problems arise as mathematical models for optimizing problems under stochastic uncertainty. Computational approaches for solving these models often involve approximating the underlying probability distribution with a probability measure that has finite support. To mitigate the computational complexity associated with increasing the number of scenarios, it may be necessary to reduce their quantity. The scenario is selected as the first element of supp , and the separable structure is used to determine the second element of supp while keeping the first element fixed. The process is repeated to establish the remaining indices, and each subsequent scenario is reduced accordingly. This iterative process continues until scenario is reduced