Dávid Gyulai

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.

Vehicle Routing Approach for Lean Shop-Floor Logistics

In order to satisfy the material supply needs of large scale shop-floors and production systems, various logistics solutions are applied. In lean manufacturing enterprises, the material supply is pulled by the demands of manufacturing/assembly processes; therefore, a milkrun service is often applied to support the production without glitches. The milkrun logistics planning is a special case for vehicle routing problem (VRP), and requires effective approach to solution in order to satisfy various constraints, and minimize the cost of service. This study gives an overview about lean logistics as well as the most efficient VRP solver algorithms. Furthermore, a novel initial solution with generation heuristics is proposed, which is specially focused on flexible milkrun planning. In order to demonstrate the capabilities of the solution, a software environment is developed as a demonstration that focuses on the main industrial requirements of logistics planning like effective layout definition, quick response of the delivery service and effective order handling.