Optimization Archives - Supply Chain Data Analytics

Creating a support vector machine using 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 […]

Quadratic assignment problem with Pyomo in Python

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 in Python

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 for manufacturing implemented 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 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 […]

Augmented epsilon constraint method: Multi-goal optimization with Pulp 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 optimization problems: Method comparison

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 […]

Optimization for Machine Learning: Shallow and Deep Supervised Learning Models

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 […]

Handling deep data uncertainty using robust optimization

In this post, I describe how deeply uncertain parametric data can be controlled in an optimization model to gain conservative solutions and decisions. The investigated robust optimization approach can lead to least, partially, or fully conservative solutions by paying the […]

Constraint programming for work scheduling with Google OR-Tools

In previous posts I have already introduced Google OR tools for linear programming. In this post I want to demonstrate the capabilities of Google OR tools for constraint programming. More specifically, I will solve a job scheduling problem using constraint […]

Linear integer programming with Google ortools in Python

In a several other posts on Google’s ortools module in Python I have already solved the linear optimization problem stated below. The problem is a continuous problem as all optimization variables are from a continuous solution space. I could think […]

Optimization

Creating a support vector machine using Gekko in Python

Quadratic assignment problem with Pyomo in Python

Single machine scheduling with PuLP in Python

Integer programming for manufacturing implemented with CPLEX & DOCPLEX

Optimization with JuMP in Julia

Augmented epsilon constraint method: Multi-goal optimization with Pulp in Python

Scalarizing multi-objective optimization problems: Method comparison

Optimization for Machine Learning: Shallow and Deep Supervised Learning Models

Handling deep data uncertainty using robust optimization

Constraint programming for work scheduling with Google OR-Tools

Linear integer programming with Google ortools in Python

Lean coding of simple linear optimization ortools models in Python

Simple linear programming with Google ortools in Python

Categorization of optimization problems

Multi-objective linear optimization with weighted sub-problems, using PuLP in Python

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