I want to show how you can optimize a conveyor system with a combination of mathematical modeling and discrete-event simulation (DES). This is an addition to the rich variety of conveyor optimization examples available in industry. However, as I will explain in this article the workflow and procedure proposed by me leads to significantly better […]
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 […]
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 […]
There are many use cases of operations research (OR) where the decision problem is finding an optimal sequence over time. For instance, we may be interested in allocating a resource (e.g., a machine, a human, a facility, a plane, a cloud computer) overtime to conduct a set of tasks (e.g., manufacturing, project, order picking, flight, […]
Following the previous article on modeling and solving an optimization problem in Python using several “interfaces” (+), in this article, I try to provide a comprehensive review of open-source (OS), free, free & open-source (FOSS), and commercial “solvers,” which are usually used for specific types of problems and coded with low-level programming languages (such as […]