for that purpose.Ī good and popular programming language recommended by many in the OR and Data Science communities is Python. They need to utilize a programming language such as C++, Java, C#, Python, etc. This means that today’s OR practitioners need to design, model, and implement robust software engines that are based on LP/MILP models. Recent computational advances have provided the infrastructure for us to incorporate optimization models in analytic software solutions.
These days, however, many in industry want to plan and make optimal decisions regularly as a part of their hourly, daily, or weekly operations. Thus, optimization models were traditionally designed for use in strategic/tactical decisions rather than operational ones. In the past, we used to model a real-world optimization problem with LP/MILP packages in isolation such as GAMS, AMPL, OPL, or others, then solve it with an optimization solver (such as CPLEX, Gurobi, Mosek, Xpress, etc.) and give the optimal result to managers and decision makers. In the past four years, I have realized the importance of OR solutions (i.e., software solutions that are based on optimization models) for solving these kinds of programs. I have been involved in the design, development, and implementation of operations research (OR) and optimization models such as Linear Programs (LP), Mixed Integer Linear Programs (MILP), and Quadratic Programs (QP) for more than a decade.
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