Load-Optimizer is the most flexible and easy to use tool for planning container and truck loads with mixed products or odd sized cargo. Utilizing self learning algorithms to power our optimization engine it will generate optimal loads while taking in to account:
Easy implementation of Load-Optimizer is guaranteed as it learns on the job. In manual mode a human planner can configure loads by shifting the 3D representations of the cargo into a 3D container or vehicle.
Additionally, the system learns from the way operators group packages, or places them in a certain way in the container or vehicle and adapts its optimization algorithm accordingly. After the initial training, Load-Optimizer can automatically generate better solutions than operators in seconds.
Load-Optimizer reads the loading list. The operator can select each object from the loading list in Load-Optimizer, see a 3D visualization of the package and place this in the container / vehicle.
If the operator hits the automated optimization button, Load-Optimizer will compute and optimize the loads itself. The operator validates the design and makes adaptations if needed
The truck is loaded according to the load design generated by Load-Optimizer. The driver, or unloader knows at any time during the trip where each package is placed in the truck, even after partial unloading.
Load-Optimizer includes both handling efforts during and stacking stability after partial unloading in it's optimization algorithm. Therefore the handling efforts are minimized and the stability of cargo and truck is guarenteed.
Our computational methods are based on Evolutionary Algorithms (GA). These are able to find the optimal solutions in a reasonable time. This allows for rapid computations for load designs with the highest match on criteria important to the client.
Load designs in automatic mode are created by developing multiple load designs in several generations. These load designs are not created randomly, but are based on historical correlation between stacked packages and locations in containers or trucks (the building engine). The decision making algorithm calculates the quality of each load design. Based on this outcome a new generation of load designs are created and evaluated. Therefore load-Optimizer can quickly find optimal solutions.
Rating generated load design based on:
These load designs are not created randomly, but are based on historical data. Also, the decision-making algorithm learns from the choices made by clients in case of tradeoffs.
Load-Optimizer searches data from load designs made for correlation between:
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