China
Africa
Explore chapters and articles related to this topic
Combined CODEC and Network Parameters for an Enhanced Quality of Experience in Video Streaming
Published in Ce Zhu, Yuenan Li, Advanced Video Communications over Wireless Networks, 2017
In this work, the OMNeT++ network simulator (OMNeT++, 2010) was used to validate the optimization model. OMNeT++ is a discrete-event simulator, which provides an object-oriented with an open-architecture network simulation environment. The optimization algorithm was implemented using CPLEX™ (CPLEX, 2010), which is an optimization library originally developed by ILOG and now is part of the product suite of IBM™. The C++ version of CPLEX was used within the network simulator.
Nonlinear Optimization
Published in Michael W. Carter, Camille C. Price, Ghaith Rabadi, Operations Research, 2018
Michael W. Carter, Camille C. Price, Ghaith Rabadi
IBM CPLEX Optimizer can solve both convex and non-convex quadratic to global optimality. It can find the unique solution to a concave maximization problem and a first-order solution to a non-concave problem. CPLEX has both barrier and simplex algorithms for solving convex quadratic programs and a barrier algorithm for solving non-convex problems. It can also solve problems with convex quadratic constraints.
Integer linear programming for mining systems
Published in Amit Kumar Gorai, Snehamoy Chatterjee, Optimization Techniques and their Applications to Mine Systems, 2023
Amit Kumar Gorai, Snehamoy Chatterjee
After CPLEX reads the problem, the command “optimize” needs to use for the optimization. Based on the lp formulation and decision variable types, CPLEX will automatically select the optimization algorithm. By writing ‘optimize’ on the CPLEX prompt and hitting enter KEY, the user can run the optimization in CPLEX (Figure 5A6).
Approach for integrated product variant allocation and configuration adaption of global production networks featuring post-optimality analysis
Published in International Journal of Production Research, 2022
Jan Hochdörffer, Felix Klenk, Thomas Fusen, Benjamin Häfner, Gisela Lanza
Inman and Gonsalvez (2001) developed an approach for the dynamic product allocation to existing production lines by combining several objectives. First, minimising penalty lost sales due to unsatisfied customer demand, an even factory utilisation is aimed. Second, the company’s internal production network is made more flexible by the possibility of simultaneously allocating individual products to several plants according to the chaining principle. To achieve these objectives, the approach is separated into two sub-models, which are modelled as mixed-integer non-linear programme and solved via the Branch & Bound algorithm. Most of the defined criteria are not fulfilled completely, especially no post-optimality analysis is assessed in this approach. The approach by Wittek (2013) aims to maximise the contribution margins generated by the company’s own production entities. Here, the production programme takes into account both advanced production quantities and backlogged production quantities in a differentiated manner and their cost implications, including inventory cost, can be directly integrated into the objective function. To solve the optimisation model, the CPLEX solver is used, which uses both the Branch & Bound and heuristic solution methods. The approach does not consider post-optimality analysis either, as well as multilevel modelling and alternative technologies and resources.
A multi-objective model for fleet allocation schedule in open-pit mines considering the impact of prioritising objectives on transportation system performance
Published in International Journal of Mining, Reclamation and Environment, 2021
Mehrnaz Mohtasham, Hossein Mirzaei-Nasirabad, Hooman Askari-Nasab, Behrooz Alizadeh
To illustrate the application of the MILGP model for optimal planning of the truck-shovel allocation problem, the model in Section 2.1 solved with the CPLEX solver in the GAMS environment version 25.1.3, a kind of optimisation software, on an Intel i7 CPU with 12GB of RAM. The CPLEX solver is a powerful tool for solving linear programming, mixed-integer programming, quadratic programming, quadratically constrained programming, and multi-objective programming models with strictly linear objectives. It provides flexible, efficient mathematical programming solutions. Solution report of the results specifies by the following items: (1) solver status, (2) model status, (3) absolute and relative gap, (4) iteration number, (5) best value for objective functions and decision variables, and all other information required to assure accurate and optimal results. By examining these cases, conclusions can be drawn about the optimality of the results.
Optimizing the Implementation of Small Modular Reactors into Ontario’s Future Energy Mix
Published in Nuclear Technology, 2023
C. Colterjohn, S. Nagasaki, Y. Fujii
For the purpose of this investigation, IBM’s ILOG CPLEX Optimization Studio, henceforth referred to as CPLEX, was used. This system utilizes IBM’s “intelligent software” (ILOG) and the application of the aforementioned Simplex method via C programming language. CPLEX allows for the simple creation and solving of a linear optimization model, requiring only defined decision variables, linear constraints, and an objective function, in addition to the relevant input values (applied as coefficients). For this model, the barrier method and CPLEX’s Flow Control (see Sec. A.I of the Appendix) were applied to solve the model.