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Load Optimization using Genetic Algorithm and Decision Support System

The global trade supply chain consists of container shipment of hundreds of millions of metric tons of deadweight. With the ever-increasing demand for a freight shipment, the need of the hour is optimized freight loading pattern and plan. This scholarly paper aims to optimize the loading pattern within a container to make space for more cargo making underutilization of space inside a container almost near to obsolete. In this paper, we have surveyed research papers that the existing system uses. 
 
The proposed way out to overcome the flaws is a model which is an amalgamation of Decision Support System (DSS) and Genetic Algorithm (GA). This hybrid algorithm is first fed the container size and the number of different sizes of cargo individually. The input data will then run GA against a certain set of predefined target parameters. Upon multiple iterations, GA will generate multiple near-optimal solutions. This pool of solutions will be processed with the help of DSS to decide the optimal loading pattern. The optimal solution is fed in the form of coordinates to the Unity engine. The Unity engine will use the given coordinates to simulate a 3D optimized loading pattern inside a container. The user can see a step-by-step simulation of loading the cargo into the container where the cargo will be color-coded. This result can be stored to refer to and transport a similar cargo.