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A multi-depot vehicle routing problem with stochastic road capacity and reduced two-stage stochastic integer linear programming models for rollout algorithm by Anuar, Wadi Khalid, Lee, Lai Soon, Seow, Hsin Vonn, Pickl, Stefan

“…A matheuristic approach based on a reduced two-stage Stochastic Integer Linear Programming (SILP) model is presented. The proposed approach is suitable for obtaining a policy constructed dynamically on the go during the rollout algorithm. …”
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Surveillance camera placement optimization using Particle Swarm Optimization (PSO) algorithm and Mixed-Integer Linear Programming (MILP) model / `Ain Safia Roslan by Roslan, `Ain Safia

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Quality of service in mobile IP networks with parametric multi-channel routing algorithms based on linear programming approach by Gholizadeh, Somayyeh

“…This approach tunes the parameters of the linear programming models that are used in the other algorithms by using a dynamic element. …”
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Model structure selection for a discrete-time non-linear system using genetic algorithm by Ahmad, Robiah, Jamaluddin , Hishamuddin, Hussain, Mohd. Azlan

“…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

“…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

“…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
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An Integer Linear Programming model and Adaptive Genetic Algorithm approach to minimize energy consumption of Cloud computing data centers by Ibrahim, Huda, Aburukba, Raafat O., El-Fakih, Khaled

“…This paper focuses on the development of a dynamic task scheduling algorithm by proposing an Integer Linear Programming (ILP) model that minimizes the energy consumption in a Cloud data center. …”
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Solving Sudoku puzzles in Binary Integer Linear Programming using Branch and Bound algorithm / Ahida Waliyyah Ahmad Fuad by Ahmad Fuad, Ahida Waliyyah

“…This study explores the application of a Binary Integer Linear Programming (BILP) model combined with the Branch and Bound (B&B) algorithm to solve Sudoku puzzles. …”
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Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed... by Ayodele B.V., Mustapa S.I., Kanthasamy R., Mohammad N., AlTurki A., Babu T.S.

“…Biomass; Catalysis; Digital storage; Gasification; Gaussian distribution; Hydrogen production; Learning algorithms; Lime; Palm oil; Quadratic programming; Regression analysis; Sensitivity analysis; Synthesis gas; Co-gasification; Gaussian process regression; Hydrogen-rich syngas; Machine learning algorithms; Non-linear response; Performance; Quadratic modeling; Renewable energies; Support vectors machine; Syn gas; Support vector machines…”

Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed... by Ayodele, B.V., Mustapa, S.I., Kanthasamy, R., Mohammad, N., AlTurki, A., Babu, T.S.

“…Taking advantage of the data generated from the process, this study explores the performance of twelve machine learning algorithms built on the support vector machine (SVM), the Gaussian process regression (GPR), and the non-linear response quadratic model (NLRQM) using Sequential quadratic programming, and the Levenberg-Marquardt algorithms. …”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, Mohd Azlan

“…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
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Performance analysis of support vector machine, Gaussian Process Regression, sequential quadratic programming algorithms in modeling hydrogen-rich syngas production from catalyzed... by Ayodele, B.V., Mustapa, S.I., Kanthasamy, R., Mohammad, N., AlTurki, A., Babu, T.S.

“…Taking advantage of the data generated from the process, this study explores the performance of twelve machine learning algorithms built on the support vector machine (SVM), the Gaussian process regression (GPR), and the non-linear response quadratic model (NLRQM) using Sequential quadratic programming, and the Levenberg-Marquardt algorithms. …”
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Comparison between fuzzy bootstrap weighted multiple linear regression and multiple linear regression: a case study for oral cancer modelling by Mohd Ibrahim, Mohamad Shafiq, Wan Ahmad, Wan Muhamad Amir, Hasan, Ruhaya, Harun, Masitah Hayati

“…Objectives: In this study, multiple linear regression model was calculated by using SAS programming language based on computational statistics which considered combination of robust regression, bootstrap, weighted data, Bayesian, and fuzzy regression method. …”
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A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms by Lai V., Huang Y.F., Koo C.H., Ahmed A.N., El-Shafie A.

“…Decision making; Dynamic programming; Linear programming; Nonlinear programming; Operating costs; 'current; Energy productions; Floodings; Meta-heuristics algorithms; Optimal reservoir operations; Optimizing energy; Reservoir operation; Reservoir operation optimizations; Traditional models; Water scarcity; Reservoirs (water)…”

A mixed integer linear programming model for real-time task scheduling in multiprocessor computer system by Oluwadare, Samuel Adeboyo, Akinnuli, Basil Oluwafemi

“…The advent of multi-processor systems offers a more efficient way of processing multimedia data in real-time.With the development of appropriate scheduling algorithm, another challenge is the mode of assigning tasks in multi-processor systems.This calls for the use of an appropriate mathematical model that will take cognizance of the nature of variables involved.In this research work, a Mixed Integer Linear Programming Model (MILP) was developed to assign tasks in a multiprocessor system.The MILP model was used to assign tasks to multi-processor systems ranging between 5 and 10 homogeneous processors.The result of the simulation runs shows that with the appropriate scheduling algorithm, a high success rate ratio and guaranteed number of deadlines met could be achieved.…”
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Augmentation of basic-line-search and quick-simplex-method algorithms to enhance linear programming computational performance by Nor Azlan, Nor Asmaa Alyaa

“…Linear programming (LP) is a mathematical modelling that formulate a problem into three components which are decision variables, objective function and constraints. …”
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Programming Approach in Teaching Operations Research Techniques for Computer Science Students by Maarop, Nurazean, Masrom, Maslin

“…OR techniques include among others, linear programming, non-linear programming, integer programming, data analysis including statistics, simulation, and goal programming. …”
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A modified linear programming model for cost minimisation of Pangasius hypophthalmus feed formulation / Zuraida Alwadood, Anis Syahirah Rosli and Norlenda Mohd Noor by Alwadood, Zuraida, Rosli, Anis Syahirah, Mohd Noor, Norlenda

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Dynamic linear programming model for quarterly budget allocation and execution / Rossidah Wan Abdul Aziz by Wan Abdul Aziz, Rossidah

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Development of nonlinear model for continuous stirred tank reactor (CSTR) by Mohamad Ikwan, Zakaria

“…Continuous stirred tank reactors (CSTR) finds wide application in the chemical industry from pilot plant to full-scale production operation.CSTRs generally present operational problems due to complex open-loop nonlinear behavior in the form of input/output multiplicities and ignition/extinction phenomena.A major limitation of linear model is that plant behavior is described by linear dynamic model.As a result,linear model is inadequate for highly nonlinear process and moderately nonlinear process which have large operating regimes.This shortcoming coupled with increasingly stringent demands on throughput and product quality has spurred the development of nonlinear model.The objective of this research are development mathematical model for CSTR process based on first principal,validation of mathematical model through experimentation and nonlinear model identification of a CSTR process.The mathematical model based on first principles is developed from sodium hydroxide,ethyl acetate,and sodium acetate and ethanol mass balance.Then,the model equation is solving in MATLAB environment by doing algorithm for this process.The program for CSTR system is created and this program known as nonlinear fundamental model.The result from the MATLAB simulation program is compared with experimental result to validate the fundamental model. …”
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