An Extended Ant Colony Optimisation Approach for the Mass Customisation Paradigm

Traditional manufacturing systems are based on large production volumes and repeatable high quality. Recently, with the emergence of Industry 4.0, increasing range of products variations introduced by Mass Customisation can make assembly processes more complex. By understanding complexity and the direction in which it flows in assembly plants, there is an opportunity to minimise its impact on assembly operations. Additionally, planning and scheduling can become increasingly complex with frequently changing customer orders as expected with the emergence of the mass customisation paradigm. These changes can prove disruptive and costly for manufacturers. This research introduces two new complexity measures: Material Handling Complexity and Model Change Complexity and uses them to quantify variation induced complexity for a mass customisation paradigm. Ant Colony Optimisation (ACO) Algorithms have been used successfully in the past to tackle combinatorial optimisation problems in dynamic environments as its inbuilt mechanisms allow it to adapt to new environments. In this work we use a novel architecture that integrates an Extended Ant Colony Optimisation (EACO) Algorithm, an agent-based simulation model and a multi-agent system frame works to find the optimum build sequence for customer orders in a mixed model assembly for different optimisation objectives. Results show that the Extended Ant Colony Algorithm can be applied to solve multi-objective optimisation problems in a dynamic environment with unforeseen disturbances like mass customisation. In comparison to the First Come First Serve (FCFS) and standard ACO, the EACO showed the robustness to support decision making in a disruptive environment and performs significantly better in both dynamic and multi objective manufacturing environments. In comparison to the Machine Line Balancing Strategy (MLBS) and Standard Ant Colony Optimisation algorithm, the EACO showed significant improvements in the cumulative machine utilisations at the workstation nodes for a time optimisation objective.