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

Abstract

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.

Lz-Transform Method and Markov Reward Approach for Flexible Manufacturing System Performance Evaluation

“…The availability of an FMS can be defined as the ability of the manufacturing system to meet production demands in accordance with specified conditions and time interval [16]. Therefore, the availability of FMS depends on the relation between the output performance and the desired level of this performance-demand, which is determined outside the system.…”

“…Jain and Raj [15] performed three different approaches to analyze the intensity of performance variables in an organization and proposed the FMS performance index to intensify the factors, which affect FMS. In a multistage production system, Gyulai et al [16] proposed a simulation-based optimization method that utilizes lower-level shop floor data to calculate robust production plans for final assembly lines of a flexible, multistage production system. Rybicka, J. et al [17] demonstrated how discrete event simulation (DES) can address complexity in an FMS to optimize the production line performance.…”

Response surface approach to robust design of assembly cells through simulation

“…Several studies focus on robust balancing of classic ALs (Moreira et al , 2015; Chica et al , 2016), while others focus on robust U-shaped AL balancing (Bagher et al , 2011) considering uncertainty in factors such as task times and demand. Gyulai et al (2017) proposed a robust SO approach for production planning of final ALs in a multi-stage production system considering stochasticity in processing times and machine availability. In a similar context, Gyulai et al (2015) combined regression modelling, mathematical programming and simulation analysis to obtain robust production planning and capacity control in flexible ALs with the objective of minimization of a cost function.…”

Correlational investigation of manufacturing technology and environmental impact in an agricultural machinery industry

“…In the industry of this study, the Production Planning and Control department (PPC), along with the Manufacturing Engineering act in the improving and management of the production. Studies show the lead-time for the manufacturing of the products is one of the most important parameters used by the PPC (Gyulai et al, 2017). There is also manufacturing capacity operation, inventory reduction and delivery time (Reschke and Schuh, 2017).The authors do not know the environmental impact parameters generated by the manufacturing technology, material or processes.…”