An Optimization Framework for Efficient and Sustainable Logistics Operations via Transportation Mode Optimization and Shipment Consolidation: A Case Study for GE Gas Power

TL;DR

This paper presents a novel optimization framework for GE Gas Power to enhance logistics efficiency and sustainability by optimizing transportation modes and shipment consolidation through a dynamic network flow model and heuristic approach.

Contribution

It introduces a customized multi-period, multi-commodity network flow model with a heuristic method for shipment consolidation and storage optimization in complex freight networks.

Findings

Improved cost-effectiveness in transportation planning.

Enhanced shipment consolidation leading to reduced logistics costs.

Demonstrated applicability through a real-world case study.

Abstract

General Electric (GE) Gas Power, a leading manufacturer of gas and steam turbines, manufactures and installs these turbines in power generation plants worldwide. They source components for these turbines from suppliers globally and transport these components to manufacturing and assembly locations in the United States using various modes of transportation, including ocean, air, and ground. These transportation options have different lead times and costs. The challenge lies in identifying the most cost-effective solution that meets the assembly requirements, given the high volume of shipments and the complexity of the freight network. To address this challenge, we develop a customized, multi-period (dynamic), multi-commodity network flow model and a novel heuristic approach with a rolling time horizon. This model incorporates consolidation and storage options at intermediate nodes, allowing the business to optimize its shipments.