
Key Features
Container Load Optimisation
Project Argus Container Load Optimisation solution incorporates both cylindrical and cubical (or cubic) shape load measurements, along with consideration for CO2 emissions, offers several competitive advantages over solutions that don't have this dual functionality:
01
By accounting for both cylindrical and cubical cargo shapes, the solution provides a more comprehensive and accurate assessment of cargo volume.
02
It is versatile and adaptable to various industries and cargo types, making it an attractive solution for Shippers, Freight Forwarders & Airlines.
03
Optimisation of both cylindrical and cubical cargo can result in cost savings. This includes reducing the number of containers required, minimising fuel consumption, and avoiding fines or fees associated with excessive emissions, all contributing to lower operational costs.
Key Features of Argus Container Load Optimisation Solution
Platform
• Web-based solution• Multi-user access• Security – Authentication• User and Data Level Authorisation• Audit Logs• Integration for external data sources• Automation of workflows
Output Reports
• Container Summary Report• Detailed Load Plan report• Layout Diagram by container• Customised reports and dashboard
UI / UX
• Dashboards & Analytics reports• Exception Reports• Multi-Dimensional Visualisations• Planner oriented UI design for Intuitive UX• Export to PDF and MS Office• Custom measures and KPIs
Algorithm
• Customised Meta-Heuristics are used to solve this combinatorial non-linear loading problem• Algorithm tuned for balancing optimality and speed• The algorithm also addresses cylindrical shapes; this is the only known commercial algorithm in the industry for loading cylindrical loads.• Algorithm is parameterised at each level• Addresses loading of loads along any of the 3 axes.• Considers load-specific & customer-specific constraints.

Are you looking to calculate the packing utilisation of cubical and cylindrical load under certain constraints or conditions?
Process Flow of Container Load Optimisation
01
Input
• Dimensions of the container(s)• Dimensions of the load• Shipment quantities• Customer details• Shipment rule and restrictions
02
Optimiser
• Customised meta-heuristic that balances optimality and speed• Founded on past research and experience of the Spashta team in building Optimisation algorithms
03
Output
Load Plan for each container• Which load goes into which container• Visualisation of placement of the load in the container
04
What-ifs
• Scenario Planning (What-if planning based on possibilities)• Relevant Parameters and rules exposed to users for performing what-if plans or re-plans.
05
Release
• Release finalised container layout plan for loading
