Ant Colony Optimisation for Backward Production Scheduling

Abstract EN

The main objective of a production scheduling system is to assign tasks (orders or jobs) to resources and sequence them as efficiently and economically (optimised) as possible.

Achieving this goal is a difficult task in complex environment where capacity is usually limited.

In these scenarios, finding an optimal solution—if possible—demands a large amount of computer time.

For this reason, in many cases, a good solution that is quickly found is preferred.

In such situations, the use of metaheuristics is an appropriate strategy.

In these last two decades, some out-of-the-shelf systems have been developed using such techniques.

This paper presents and analyses the development of a shop-floor scheduling system that uses ant colony optimisation (ACO) in a backward scheduling problem in a manufacturing scenario with single-stage processing, parallel resources, and flexible routings.

This scenario was found in a large food industry where the corresponding author worked as consultant for more than a year.

This work demonstrates the applicability of this artificial intelligence technique.

In fact, ACO proved to be as efficient as branch-and-bound, however, executing much faster.