# Optimization

## Inventory simulation for optimized stock

Inventory optimization refers to reducing excess inventory, avoiding lost sales due to unavailability of items in stock, well-defined safety stocks and reorder points, and much more. In this article I demonstrate how simulation can be used to master the challenges […]

## Conveyor system optimization procedure

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 […]

## 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 […]

## 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, […]

## Single machine scheduling with PuLP

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 […]

## Integer programming with CPLEX & DOCPLEX

Thanks to the evolution of Python and its applications to solve linear programs and their variations supply chain and operations research analysts now have access to numerous packages and tools that support decision making. In this article I will use […]

## Optimization with JuMP and GLPK in Julia

As a result of the emergence and progress of open-source languages SCM and OR analysts have access to a growing number of constantly improving tools and solvers. Julia, for example, is a flexible dynamic open-source programming language that is appropriate […]

## Epsilon constraint optimization in Python

In previous posts, we have discussed using PuLP in python for the implementation of multi-objective linear optimization methods such as maximizing for one objective, then adding it as a constraint, and solving for the other objective (or applying a scalar […]

## Scalarizing multi-objective optimizations

I already introduced various coding examples implementing multi-objective optimization. In these examples I implemented different strategies for searching a multi-objective optimum. One of these strategies was based on scalarizing multiple objectives into a single objective function using weights for each […]

## Shallow and deep supervised learning model

In this post, I explain how supervised learning models in machine learning use single or multiple neurons in a single layer (shallow learning) or multiple neurons in multiple layers (deep learning) to generate a real, binary, or integer value beneficial […]