Air Cargo Load Optimization Leveraged by Quantum Computing
PRESS RELEASE — Montevideo, Uruguay. Quantum-South releases quantum computing-based air cargo optimization application ready to execute proofs of concept. The software aims to bring efficiency in logistics, helping companies to improve revenues and reduce costs. The software has been tested on platforms of different quantum computing providers.
The Quantum-South team has been working in container load optimization for air cargo since the Airbus Quantum Computing Challenge in 2019 where it became one of the global finalists. Airbus has identified this problem as one of the most challenging in the aerospace industry. Basically, the problem can be stated as determining the optimal loading strategy for packing merchandise in aircrafts, aiming to find the optimal configuration of maximizing the loading subject to appropriate constraints. Quantum-South developments have been focused on using quantum algorithms to address the problem.
The problem addresses a number of packages to be transported with the objective to identify the optimal selection of the ones that maximize the profit associated to the ULDs (Unit Loading Devices) while respecting restrictions, such as maximum weight and length, center of gravity, shear, and the shape of the fuselage. Given the exponential increase in the number of possible combinations as the number of packages increases, the problem poses a serious challenge to be solved by classical means.
The air cargo optimization software based in quantum computing: Quantum-South is ready to work together with air cargo companies in proofs of concept (PoC) for a real business scenario, involving air cargo operations, using determined source-destination locations with an interesting complexity and a particular aircraft freighter model.
The source-destination locations can have multiple weekly flights for the same freighter where containers of different nature and priority could be arranged.
The software is flexible to air cargo scenarios as it can be adapted to business
requirements/restrictions and incorporated in the optimization criteria. It can be applied to any model of cargo aircraft and can be extended to the loading of passenger aircraft.
The idea of the algorithm is to load the aircraft maximizing the profit associated to the ULDs while respecting restrictions, such as maximum weight and length, center of gravity, shear and the shape of the fuselage. Optimizing the location of the center of mass has a significant impact on fuel consumption and environmental protection.
The software supports two decks to fill with ULDs. All the restrictions such as maximum weight and volume, center of gravity and shear are implemented taking into consideration the main and lower decks. The ULD picking list is composed of ULD of industry-standard sizes, both containers and pallets. Each aircraft type, depending on the size of its fuselage, may carry a different subset of ULDs. In general, the subset of ULDs of its two decks (main deck and lower deck is disjoint given the differences in capacity). Finally, there are ULDs that can go in only one position on each deck, while others accept multiple configurations.
This is the first product release of Quantum-South. The company is conducting R&D efforts to develop other quantum software products aiming to optimize operations in Air and Maritime Cargo.
Quantum-South
Av. Ing. Luis P. Ponce 1307
11300 Montevideo — Uruguay
Contact:
Martin Machin
[email protected]
About Quantum-South
Founded in 2019, Quantum-South works with complex optimization problems for cargo in airlines and ships leveraging quantum computing software. Quantum South was born in Universidad de Montevideo and is the first quantum start-up in Latin America. Quantum South aims to achieve better results than conventional solutions available today and help companies to improve revenues and reduce costs. Quantum South also works in solving financial sector problems where the quantum utility provides better solutions.
https://www.quantum-south.com
Contact information: [email protected]
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