#gurobi — Public Fediverse posts

Article: Gerrymandering made easy

In this article, we take a simple approach to modifying a redistricting design. We add a requirement to our model that could be interpreted as either:

- The laudable goal of grouping together "communities of interest" – a common requirement when designing voting districts; or
- A nefarious attempt to manipulate the electoral outcome by gerrymandering.

Gerrymandering is the opposite of the model's purpose in our previous article. But, as model designers, we need to be aware that we don't always control the purposes to which decision makers apply our models and decision makers don't always understand the implications of small changes to a model.

https://www.solvermax.com/blog/gerrymandering-made-easy
#Python #orms #optimization #modelling #gurobi #highs
@AustinLBuchanan

Article: Academics, please publish your data and code

Academic research papers can be a valuable source of material for creating and improving real world optimization models. But we wish that academics would publish working code and data to accompany their papers.

In this article:
- Firstly, we briefly look at some reasons why academics might be reluctant to publish their data and code.
- Then we replicate, modify, and explore a published model that has been done well, with the data and program code publicly available.

https://www.solvermax.com/blog/academics-please-publish-your-data-and-code
#Python #orms #optimization #modelling #gurobi #highs
@AustinLBuchanan

Article: Well, that escalated quickly: Pyomo

We conclude our series of articles to decide the best order for positioning devices in a rack.

This article discusses Model 5, which formulates the situation in Pyomo as a Mixed Integer Linear Program (MILP). We solve the model using a single instance of Gurobi and parallel instances of the HiGHS solver.

Does this model perform better than the previous methods?

https://www.solvermax.com/blog/well-that-escalated-quickly-pyomo
#Python #orms #optimization #modelling #gurobi #highs

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

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

Along the way, we:
- Formulate a non-linear model of the situation.
- Compare several solvers, to see how they perform.
- Linearize our model to, hopefully, make it easier to solve.
- Disaggregate our model to make some variables exogenous, then iterate over an enumeration of the exogenous variables.
- Demonstrate use of Pyomo's last() and next() functions, which enable us to work with elements of ordered sets.
- Turn off a constraint using Pyomo's deactivate() function.

Importantly, we show that there's a surprising amount of extra storage space available for free, or minimal cost, just by redesigning the warehouse's racks and shelves.

The model is built in Python using Pyomo.

https://www.solvermax.com/blog/warehouse-space-for-free-non-linear-model

#Python #pyomo #orms #optimization #modelling #Gurobi

In this pair of articles, we formulate and solve Mixed Integer Linear Programming (MILP) models to compile crossword puzzles.
We're not the first to do this. An article from 1989 reported attempts, concluding that "the prospects of using integer programming for any type of puzzle of realistic size and with a substantial lexicon remain bleak".
But a lot has changed in the 35 years since 1989. Is the problem solvable now?
https://www.solvermax.com/blog/crossword-milp-model-1
#orms #optimization #milp #gurobi

In this article, we estimate the magnitude of speed improvement for optimization solvers and computer hardware in the 35 years from 1989 to 2024. The results may be surprising.
https://www.solvermax.com/blog/solver-performance-1989-vs-2024
#orms #optimization #cplex #gurobi