Optimization of order and inventory sourcing decisions in supply chains with multiple nodes, carriers, shipment options, and products

Project description

The goal of the project has been to generalize some experience accumulated in the company in optimization of complex supply chains. The major artifact of the project is the following blog article:

Optimization of order and inventory sourcing decisions in supply chains with multiple nodes, carriers, shipment options, and products

The major results of the article are summarized by the following quote:

In this article, we analyze several common sourcing optimization scenarios, develop a relatively general framework for representing sourcing problems, and then evaluate and compare two optimization strategies (MIP solvers and metaheuristic (stochastic) optimization) on the problems of different sizes.

This repository contains mainly bare bones code used in preparation of the article. The only intention of this code publication is to make it possible to reproduce the results reported in the article.

Repository structure

• ./optim contains the main C-langueage code with the launching bash-script

• ./libcvk2 contains auxiliary code necessary to compile the project

Requirements

The project has been implemented in Linux framework. The requirements are:

• installation of the GCC compiler (installed on default in most Linux distributions);

• installation of the Bash UNIX-shell (installed on default in most Linux distributions);

• installation of the Python 3 (installed on default in most Linux distributions);

• installation of the GSL (GNU Scientific Library);

• installation of OR-Tools from Google using, for instance, the following instructinons

The installation of OR-Tools is optinonal. It is necessary only to run the Python code generated for OR-Tools by the main code of the project. If only the results of metaheuristic optimization are of intererest, then there is no the necessity to install OR-Tools.

How to setup and run project

• Ensure to meet the requirements.

• Change the working directory to ./optim and run the command ./compile. This should produce 3 files in the same directory:

  • ortools.prg.py is a Python code for MIP solution via OR-Tools;
  • res.csv contains the values of the objective function (column 3) and the penalty (column 4) by iterations;
  • log.txt contains full output of the main program into the stdout stream,which describes the whole metaheuristic optimization in details by iterations (could by cryptic for an occasional user).

License

Copyright 2022 Grid Dynamics International, Inc. All Rights Reserved

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Authors

• Volodymyr Koliadin, [email protected]
• Ilya Katsov, [email protected]