Operations Research & Optimization Projects With Julia

Discover practical optimization using Python, modeling problems to minimize costs and maximize performance. Learn linear programming, integer programming, and nonlinear optimization applied to logistics and resource allocation.

Explore operations research as a scientific approach using mathematical models, statistics, and algorithms to optimize decision making and resources. Learn core techniques like linear programming, simulation, and decision analysis.

Explore optimization tools and languages for operations research, including Python with Pyomo, Pulp, SciPy, and solvers like Cplex, Gurobi, MATLAB, and Fico Express, plus Julia.

Explore solvers as algorithmic engines that turn models of variables, constraints, and objectives to maximize or minimize into optimal or satisfactory solutions, using simplex, branch-and-cut, and gradient descent.

Explore the Nextmv decision support platform, integrating data, rules, and predictions into a unified framework, with CLI, SDKs, templates, and domain coverage like vehicle routing and scheduling.

Explore ten fold ai, an open source optimizer for operations and scheduling, supporting Java, Python, and Kotlin, with field service routing and last mile routing to boost efficiency.

Explore Hexal, a fast, scalable optimization tool with ready-made templates that deliver quick answers, enabling vehicle routing, production scheduling, and supply chain simulation with customization and training.

Explore Hexley's website tour, featuring the optimizer, studio, no-code modeler, modeling language, cloud services, APIs for programming languages, and templates for production scheduling, knapsack, and vehicle routing.

Explore Coin-or, an open source computational infrastructure for operations research. Discover key tools like CBC, CLP, SIP, and Ipopt that advance linear, mixed-integer, and nonlinear programming.

Explore OMLT, a toolkit that integrates machine learning models with optimization via Pyomo, enabling neural networks and gradient boosted trees to serve as surrogates in decision models.

See how Seeker, the optimization solver, enhances planning, scheduling, inventory control, and pricing strategies by enabling faster model creation, adaptive performance, and resilient, data-driven decisions.

Discover how SAP and the APO module optimize production planning by integrating capacity, demand forecasts, and materials data to generate efficient production schedules, reduce costs, and minimize waste.

Learn production planning and optimization in SAP, covering MRP logic, BOM levels, capacity planning, PDS scheduling, and how master data, orders, and safety stock drive feasible plant plans.

Explore how optimization underpins data science, guiding machine learning through objective functions, loss minimization, and gradient-based methods, including convex and non-convex approaches and proximal techniques.

Explore how operations research and machine learning intersect to solve optimization problems, using Julia to model, optimize, and implement practical decision-making solutions.

Explore the relationship between management science and operations research, using mathematical modeling, optimization, linear programming, simulation, and decision support systems to improve resource allocation.

Explore system simulation and operations research, modeling dynamic, stochastic systems with Python libraries for discrete event simulation, optimization, and robust validation to demonstrate integration for design and resource allocation.

Explore how operations research optimizes real world operations across supply chains, aviation, healthcare, telecommunications, traffic management, and urban planning, using models to improve scheduling, pricing, network design, and resource use.

Learn how simplex fits into real-world optimization, where solvers like Gurobi and GLPK handle large-scale problems, while practitioners focus on problem formulation, data, and software configuration.

Learn linear programming, a method to maximize or minimize a linear objective under linear constraints, with continuous non-negative decision variables and the optimal corner-point solution in the feasible region.

Translate a real world situation into a linear programming model by identifying the goal and decision variables, defining the objective, adding constraints and non-negativity, and solving via graphical or simplex methods.

Learn how to transform linear programs into standard form for the simplex method by converting minimizations to maximizations, turning inequalities into equalities with select variables, and enforcing non-negativity.

Set up a linear programming model to maximize profit from two products, A and B, with 120 labor hours; A 3 hours, B 2 hours, profits 40 and 30.

Understand canonical form of linear programming: minimize c^T x, with A x = b and x >= 0, and learn conversions for maximization, inequalities via slack variables, and unrestricted variables.

Master the simplex method fundamentals by exploring basic feasible solutions at corner points, convex feasible regions, and pivot steps that move between vertices to reach the optimal linear programming solution.

Learn how to prepare linear programs for the simplex method by converting to standard form with slack variables, then build and pivot the simplex tableau to reach the optimal solution.

Describe solving a linear program by the simplex method, introducing slack variables, building the simplex tableau, performing pivots via the minimum ratio test, and reaching the optimal solution.