Dynamic reassembly control in flexible remanufacturing systems using Ant Colony Optimisation

Remanufacturing demands flexible operational management because fluctuations in return volumes, component conditions, and processing durations can spread through the process steps and quickly undermine fixed plans. While research has largely focused on disassembly, dynamic control of reassembly remains neglected despite its variant-dependent matching requirements. This paper proposes a new Ant Colony Optimisation (ACO) formulation that treats reassembly as a sequential decision-making process, rather than building complete schedules, employing pheromone adaptation to guide effective matching decisions under uncertainty. Embedded within a discrete-event simulation model, the proposed single-shift approach is evaluated across scenarios of increasing complexity. Results show substantially improved adherence to production programs compared with heuristics, with benefits growing as system complexity rises.