The production line of modern multi-product manufacturing with erratic demand profiles shows that the selection and implementation of appropriate production control strategy are an important challenge. Organisations that adopt pull-type production control strategies, such as Kanban control strategy, for multi-product production lines find that is necessary to plan high Kanban card allocations in order to maintain volume flexibility to manage demand variability. This can result in line congestion, long lead times and low throughput rate. A recently proposed shared Kanban allocation policy has the benefit of minimising inventories in the line by allocating Kanbans accordingly and therefore maintains volume flexibility. However, many pull production control strategies that have been shown to be successful in single product manufacturing environments, for instance Kanban, CONWIP and Basestock cannot operate the shared Kanban allocation policy naturally. This Thesis presents a practically applicable modification approach to enable pull production control strategies that are naturally unable to operate in a shared Kanban allocation policy mode to operate it. Furthermore, the approach enables the development of a new pull production control strategy referred to as Basestock Kanban CONWIP control strategy that has the capability to operate the shared Kanban allocation policy, minimising inventory and backlog while maintaining volume flexibility. To investigate the performance of the pull production control strategies and policies, discrete event simulation and evolutionary multi-objective optimisation approach were adopted to develop sets of non-dominated optimal solutions for the experiments. Nelson’s screening and selection procedure were used to select the best pull control strategy and Kanban allocation policy when robustness are not considered. Additionally, Latin hypercube sampling technique and stochastic dominance test were employed for selection of a superior policy and strategy under environmental and system variability. Under non-robust conditions (anticipated environmental and system variability), pull control strategies combined with the shared Kanban allocation policy outperforms pull control strategies combined with dedicated Kanban allocation policy. Conversely, pull control strategies combined with the dedicated Kanban allocation policy outperforms pull control strategies combined with shared Kanban allocation policy when the system is prone to environmental and system variabilities. Furthermore Basestock Kanban CONWIP control strategy outperforms the alternatives in both robust and non-robust conditions.