By using ML surrogates to predict required system costs and performance indicators, we can approximate the nonlinearities in the GDP to generate an efficient mixed-integer linear programming (MILP) ...

This paper investigates the equivalence between a class of mixed-integer linear and linear programming prob-lems. By utilizing the addition of slack variables theorem, we demonstrate that certain ...

This project uses ordinal optimization for computationally efficient sizing of a hybrid energy system containing PV panels, batteries, diesel generators, and an intermittent grid. It also utilizes ...

Optimization of a city delivery network using Gurobi for Python. Achieve efficient perishable goods distribution with a hub-and-spoke model, balancing delivery volumes and distances.

This paper proposes a new algorithm with combining algorithms of unit commitment and economic dispatch, coal transshipment, coal blending and inventory problems which will be implemented using Pyomo ...

Linear programming is used to maximize or minimize a linear objective function subject to one or more constraints, while mixed integer programming (MIP) adds one additional condition: that at least ...

Mixed-integer linear programming (MILP) is often used for system analysis and optimization as it presents a flexible and powerful method for solving large, complex problems such as the case with ...