Robust Optimization for Technician and Resource Management in Reconfigurable Assembly Lines

Abstract

Research studies have rarely taken into consideration the challenge of resource selection and technician scheduling problems in the context of product evolution. Indeed, incorporating new product variants into the line’s future production generation has a significant impact on resource selection and technician scheduling. Additional resources may be necessary to produce new products, and each of these resources demands specialized skill for operation. Hence, it may be necessary to provide training for the technicians to enable them to effectively work with the new resources. This study aims to plan resource selection and technician scheduling for a reconfigurable assembly line. This line produces a given product family with a set of product variants. Moreover, a new product variant will be added after a certain period of time, named production generation. At each generation, the product family can be changed by adding or removing a product variant. We developed a Mixed-Integer Linear Programming (MILP) that minimizes the total resource selection and technician training costs for the worst-case scenario. The MILP is validated through solving a simple example and the results are given.