Value-driven Manufacturing Planning using Cloud-based Evolutionary Optimisation

TL;DR

This paper introduces cloud-based evolutionary algorithms for manufacturing planning where order values decrease over time, optimizing for makespan and total value, with a focus on selecting profitable orders.

Contribution

It presents two novel genetic-algorithm-based methods for multi-objective manufacturing scheduling that incorporate value decay and are deployed on cloud infrastructure.

Findings

Both methods achieve similar total value.

The order selection method results in a shorter makespan.

Algorithms are tested on real-world-inspired data.

Abstract

This paper considers manufacturing planning and scheduling of manufacturing orders whose value decreases over time. The value decrease is modelled with a so-called value curve. Two genetic-algorithm-based methods for multi-objective optimisation have been proposed, implemented and deployed to a cloud. The first proposed method allocates and schedules manufacturing of all the ordered elements optimising both the makespan and the total value, whereas the second method selects only the profitable orders for manufacturing. The proposed evolutionary optimisation has been performed for a set of real-world-inspired manufacturing orders. Both the methods yield a similar total value, but the latter method leads to a shorter makespan.