Robust Order Scheduling in the Fashion Industry: A Multi-Objective Optimization Approach
This work addresses scheduling challenges for fashion industry production, but it is incremental as it applies an existing method to a specific domain with added considerations.
The paper tackles robust order scheduling in the fashion industry by considering pre-production events and uncertainties in daily production quantities, using a multi-objective evolutionary algorithm (NSJADE) to show that these factors significantly impact scheduling outcomes.
In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as pre-production events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this research, by considering the pre-production events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multi-objective evolutionary algorithm (MOEA) called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider pre-production events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling.