#pyomo — Public Fediverse posts

Locational marginal pricing of potatoes

We apply Locational Marginal Pricing (LMP) to the supply of potatoes. The article describes the model, calculation of LMPs, and scenarios for how the suppliers and contractors may respond to the price signals.
#orms #pyomo
https://www.solvermax.com/blog/locational-marginal-pricing-of-potatoes

Permission granted: A role mining model

We implement a recently published role mining model using both constraint programming and mixed integer linear programming, then compare their relative performance while solving several examples.
https://www.solvermax.com/blog/permission-granted-a-role-mining-model
#orms #pyomo #ortools #python

Как нам удалось в 100 раз ускорить решение оптимизационной задачи NBO в Альфа-Банке

В данной статье мы расскажем, как нам удалось найти решение задачи NBO на open source солвере CBC примерно в 100 раз и добиться повышения оптимального значения целевой функции на 0.5%.

https://habr.com/ru/companies/glowbyte/articles/838410/

#Математическая_оптимизация #исследование_операций #ускорение_солверов #cbc #pyomo #nbo #маркетинговая_оптимизация #линейное_программирование #машинное+обучение

Как нам удалось в 100 раз ускорить решение оптимизационной задачи NBO в Альфа-Банке

В данной статье мы расскажем, как нам удалось найти решение задачи NBO на open source солвере CBC примерно в 100 раз и добиться повышения оптимального значения целевой функции на 0.5%.

https://habr.com/ru/companies/glowbyte/articles/838410/

#Математическая_оптимизация #исследование_операций #ускорение_солверов #cbc #pyomo #nbo #маркетинговая_оптимизация #линейное_программирование #машинное+обучение

Как нам удалось в 100 раз ускорить решение оптимизационной задачи NBO в Альфа-Банке

В данной статье мы расскажем, как нам удалось найти решение задачи NBO на open source солвере CBC примерно в 100 раз и добиться повышения оптимального значения целевой функции на 0.5%.

https://habr.com/ru/companies/glowbyte/articles/838410/

#Математическая_оптимизация #исследование_операций #ускорение_солверов #cbc #pyomo #nbo #маркетинговая_оптимизация #линейное_программирование #машинное+обучение

Article: 10 times faster, running cases in parallel

In this article, we explore running optimization model cases in parallel. Specifically, we use the Python multiprocessing and mpi4py libraries to fully use the many CPU cores/threads in modern computers.

Our goals are to:
- Illustrate how to apply the multiprocessing and mpi4py libraries to running optimization model cases in parallel.
- Measure the performance of running cases in parallel compared with serially.
- Compare the performance of an old 4 core / 4 thread CPU with a new 20 core / 28 thread CPU, using the HiGHS solver.

https://www.solvermax.com/blog/10-times-faster-running-cases-in-parallel
#Python #pyomo #orms #optimization #modelling #HiGHS #multiprocessing #mpi4py

Warehouse space for free: Exogenous enumeration

In this article series, we look at improving the efficiency of a pallet warehouse, where all items are stored on standard-size pallets.

In part 3 of 3, we make some variables exogenous and enumerate all of their combinations. The goal is to make the model solvable at full scale in a reasonable time.

The result is a 200 times improvement in model performance, leading to a 40% improvement in warehouse storage efficiency.

The model is built in Python using Pyomo, and solved with either the Gurobi or HiGHS solvers.

https://www.solvermax.com/blog/warehouse-space-for-free-exogenous-enumeration
#Python #pyomo #orms #optimization #modelling #Gurobi #HiGHS

Warehouse space for free: Linearized model

In this article series, we look at improving the efficiency of a pallet warehouse, where all items are stored on standard-size pallets.

In part 2 we linearize our model to, hopefully, make it easier to solve.

The model is built in Python using Pyomo.

https://www.solvermax.com/blog/warehouse-space-for-free-linearized-model
#Python #pyomo #orms #optimization #modelling #Gurobi #HiGHS

Production mix - Conclusions
We built a linear program in six Python libraries: #Pyomo, #PuLP, #ORTools, #Gekko, #CVXPY, and #SciPy.
This blog article summarizes our conclusions from using each of the libraries. We also indicate which library we prefer for various types of optimization modelling.
https://www.solvermax.com/blog/production-mix-conclusions
#orms #Python #DataScience #optimization

Production mix - Model 9, Gekko
We build Model 9 of the Python "Production mix" series.
Our objective is to compare a model built using Gekko with the same model built using Pyomo.
#orms #Python #DataScience #optimization #Pyomo #Gekko
https://www.solvermax.com/blog/production-mix-model-9-gekko