The objective of scheduling is to efficiently allocate shared resources (machines, people etc) over time to competing activities (jobs, tasks, etc.) such that a number of goals can be achieved and the given constraints can be satisfied.
The collective behavior of decentralized, self-organized systems. SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents.
Each particle has the following features:
- A position
- A velocity (an operator which can be applied to a position in order to modify it)
- The ability to memorize its personal best position
- The ability to know the globally best position
- The ability to use information to make a decision
A population-based search algorithm which mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization.
Bees algorithm and its application: https://www.youtube.com/watch?v=O9BYK-7hY0s
The mapping between the particle and the scheduling solution is established through connecting the operation sequence of all the jobs with the particle position sequence