AI in Supply Chain

To build a simulation-optimization scheme it is necessary to specify the following classes and functions:

• Classes:

• Container Ship: represents how the storage space of a container ship;

• Ship Travel simulator: represents the simulation of container ship travel through ports;

• Genetic Algorithm: represents how the genetic algorithm could work using individuals and evolutionary concepts.

• Functions:

• Loading rules: describe how the containers could be loaded in a container ship;

• Unloading rules: describe how the containers could be unloaded in a container ship.

Container Ship: represents how the storage space of a container ship.

• Functions:

• Loading rules: describe how the containers could be loaded in a container ship;

• Unloading rules: describe how the containers could be unloaded in a container ship.

Describes how to construct a container ship simulator class that enables the computation of the total number of container movements through a route and using certain unloading and loading rules.

Explains how to find a better combination of rules that will be applied through ship travel.

Explains how to build rules to coordinate the operation of quay cranes.

Explains how the quay cranes rules could be used to produce a simulator of quay cranes joint operation.

Explains how to build the modified genetic algorithm that considers the container ship and quay crane simulator.

Explains how the representation by rules using an automatic generator of rules could be employed to automatically improve the operations of all agents in a complex system. It also enables the concept of Automata Startup.

Describes the book, site, and Python codes in Google Colab which are a great support for the project.