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LogoutIt is critical to learn how to create mathematical programming models with linear and integer variables in operations research. You should learn how to solve models using commercial solvers such as GUROBI, CPLEX, XPRESS, or MOSEK. COIN-OR Project’s CLP (linear solver) and CBC (mixed integer solver) are free alternatives.
The most common language for creating models is AMPL, which can be readily interfaced with most standard solvers (such as cplex, gurobi, clp/cbc, xpress, mosek, and so on). Like MATLAB and IBM OPL, other choices are less common in OR than AMPL (in my humble opinion).
Suppose you wish to pursue an OR profession. In that case, you will be confronted with extremely difficult issues that will necessitate the development of bespoke algorithms (e.g. branch and cut) that will necessitate a deeper contact with the solver. You’ll also need to create heuristics or metaheuristics algorithms that are really fast (e.g. to solve VRP or scheduling problems). You’ll need to study C/C++ and comprehend the callback system in order to interface with the solvers in this situation.
Python is a simple and lightweight programming language. Whatever you model is quite simple to implement.
R/Matlab has a lot of statistical and modelling features. Statistics, machine learning, signal processing, image processing and computer vision, computer vision, optimization, symbolic computing, control systems, test and measurement, computational finance, and computational biology are all supported by its libraries.
AMPL (A Mathematical Programming Language) is a simple and straightforward programming language for large-scale computing. It supports a large number of solvers, some of which are open source and others which are commercial. One of the most important languages in optimization, if not the most essential.
JULIA: Julia is a relatively new language with a strong focus on scientific applications. To model and develop OR applications, you can use juliaopt and JuMP. In contrast to Python, JULIA, in my perspective, is still too new and underdocumented.