Heuristic optimization in Python

Following the previous articles on interfaces (+) and (exact) solvers (+) for optimization in Python, in this article, I introduce some packages that provide an easy-to-use “interface” for artificially intelligent algorithms (AIAs) (e.g., heuristics, meta-heuristics, math-heuristics, learn-heuristics, hyper-heuristics, or sim-heuristics). AIAs may not reach globally optimal solutions, which means that the user should always be […]

Support vector machine with Gekko in Python

In this blog post I model a support vector machine in Python. Previously, I modeled and solved the quadratic assignment problem (QAP) in Python using Pyomo (+). I described that the “similarity” of two facilities could be a reason for putting them closer to each other when assigning them to a set of pre-determined positions. […]

Pyomo for quadratic assignment problem

No matter if it is the assignment of departments to empty rooms in a building, machines to manufacturing cells, factories to geographical regions, products to racks in a warehouse, sensors to devices, edge computers inside the internet of things network, public transport stations to different zones in a city, you may need to model and […]

Flow shop scheduling with PuLP in Python

In previous articles, I modeled and solved a single machine scheduling problem. What if we have two resources that operate in a pre-determined sequence (but not in parallel) to process a set of similar jobs? The so-called “flow shop” scheduling models focus on these situations. It can be production scheduling inside a factory. Or it […]

Gekko for linear demand pricing

How can we decide on a product or service price using observed data over time? For example, a retailer may have changed the price for a specific product in multiple time slots of a week, tested the demanded quantity (sales) based on different prices, and now wants to know the optimal price to set for […]