mip#

AMPL Development Tutorial 1/6 – Capacitated Facility Location Problem#

AMPL Development Tutorial 2/6 – Stochastic Capacitated Facility Location Problem#

AMPL Development Tutorial 3/6 – Benders Decomposition via AMPL scripting#

3_benders_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: In this third installment of our six-part series, we continue our exploration by addressing the complexities introduced by the stochastic programming formulation presented in part two.

AMPL Development Tutorial 4/6 – Benders Decomposition via PYTHON scripting#

4_benders_in_python_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: In this fourth installment of our six-part series, we advance our exploration by demonstrating how to adapt our AMPL script for use with AMPL’s Python API.

AMPL Development Tutorial 5/6 – Parallelizing Subproblem Solves in Benders Decomposition#

5_benders_parallel_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: In the fifth installment of our six-part series, we delve deeper by showing how to evolve our Benders decomposition Python script from a serial execution to one that solves subproblems in parallel.

AMPL Development Tutorial 6/6 – Implementing Benders Decomposition with ampls#

6_benders_ampls_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: This concluding notebook in our six-part series delves into enhancing the efficiency of our decomposition algorithm by utilizing AMPL Solver Libraries (ampls).

Capacity expansion of power generation#

capacity_expansion.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Models the extensive form of a deterministic multi-stage capacity expansion problem. In this model we can have multiple resources of the same type which have identical properties. The model can be further developed into a stochastic one.

Containers scheduling#

containers_scheduling.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Scheduling model for harbor operations. It is a problem with dependences between containers, which should be dispatch the fastest possible. We are using the MP solver interfaces to model a complex system using techniques from Constraint Programming, such as indicator constraints, and logical or and forall operators. After the model is written, a couple instances are presented and Highs/Gurobi MIP solvers are used to tackle the problem.

Dual-Donor Organ Exchange problem#

Dual-Donor_Organ_Exchange.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Most transplants from living donors require only one donor for each procedure. There are, however, exceptions, including dual-graft liver transplantation, bilateral living-donor lobar lung transplantation, and simultaneous liver-kidney transplantation. For each of these procedures, grafts from two compatible living donors are transplanted. As such, these procedures are more involved from an organizational perspective than those with only one donor. Unfortunately, one or both of the donors can often be biologically incompatible with the intended recipient, precluding the transplantation.

Employee Scheduling Optimization#

Employee_Scheduling.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Employee scheduling model from the Analytical Decision Modeling course at the Arizona State University.

Hospitals-Residents MIP#

hospitals_residents.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: hospitals-residents problem with ties problem solved with ampl and highs

Labs scheduling#

labs_scheduling.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Model for laboratories scheduling. Some labs are needed to handle requests from researchers, and departments have to assign labs and locations to the requests.

Magic sequences#

magic_sequences.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Solving magic sequences through reinforced formulations and constrained programming. Some comparison between models and solvers is done, and we look into the “Another solution” problem for these sequences.

Oil refinery production optimization#

oil_refining.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: In this document, we present an enhanced approach to oil refining optimization for improved decision-making.

Oil refinery production optimization (+PowerBI)#

oil_refining_powerbi.ipynb Open In Colab
Description: In this document, we present an enhanced approach to oil refining optimization by integrating Power BI for improved decision-making and data visualization. For a full description of the model, you can read more about it [here](ampl/colab.ampl.com).

Oil refinery production optimization (ampl-only version)#

oil_refining_ampl_only.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: In this document, we present an enhanced approach to oil refining optimization for improved decision-making.

Optimization of Reinforced Concrete Production and Shipment: A Conveyor-Based Manufacturing and Curing Model#

Optimization of an advertising campaign for launching a new product on the market#

Optimizing the number of staff in a chain of stores#

P-Median problem#

p_median.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: this notebook states the p-median problem with a simple example, and a MIP formulation in amplpy. The problem is parametrized with a class, so it is easier to sample and replicate experiments. A graphical solution is plotted.

Pricing and target-market#

pricing_and_target_market.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Formulate a pricing optimization and target-market problem as a MILP.

Profit Maximization for Developers: Optimizing Pricing, Marketing, and Investment Strategies#

Project management: Minimizing the cost of implementing an investment project, taking into account the costs and risks of completing tasks and penalties for late fulfillment of obligations.#

Solving a nonogram puzzle#

nonogram.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Model for solving nonogram puzzles autogenerated using nonogram.mod, nonogram.dat and nonogram.run.
Tags: ampl-only, mip

Sudoku Generator#

sudoku_gen.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Generate Sudoku boards with unique solution via iterative method and mip formulation.

Warehouse location and transport#

warehouse_location.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Model for warehouse allocation. Farms (suppliers) send feedstock to warehouses, and later on, those warehouses send it to a production plant. The problem involves modeling a storage facility location problem with a transportation component to the final plant.