## 2 Literature Review
### Buffer Allocation Problems  
[PERSON] et al. [55] presented an original methodology to address the buffer allocation and throughput trade-off problem in finite queueing networks. A specialised multi-objective genetic algorithm approximates the Pareto optimal set of solutions. Computational results demonstrate the efficiency and efficacy of the proposed methodology, highlighting the impact of the coefficient of service time variation on buffer allocation decisions and the dependence of buffer allocation on target throughput. [PERSON] et al. [56] addressed optimising topological network design for multi-server queueing networks. Series, merge, and split topologies are examined using approximation and iterative search methods to estimate performance and determine optimal buffer allocation. The impact of the coefficient of variation on buffer allocation is highlighted, and computational results illustrate emerging buffer patterns within different topologies. [PERSON] et al. [57] compared three layout structures in automotive engine shops to maximise profit based on throughput and buffer investment costs. The analysis utilises queueing network models with finite buffers and unreliable machines, considering heterogeneous service times and exponentially distributed failure and repair times. The study employed approximation methods and a gradient search approach to determine the optimal buffer allocation. [PERSON] et al. [58] modelled mould manufacturing using QQNs with finite buffers. Based on queuing theory approximations, the proposed methods effectively evaluate system performance and demonstrate feasibility for large-scale practical problems. The findings provide valuable insights for system design, resource planning, buffer allocation, and capacity configuration. [PERSON] [59] presented an approach to determine the buffer allocation of a finite system using a queueing network decomposition methodology combined with a nonlinear sequential quadratic programming algorithm. The joint optimisation problem maximises throughput by treating the population as a constraint. [PERSON] et al. [60] developed analytical formulas to estimate the throughput of a reliable production line with exponential service times and finite buffers. The formulas apply to approximately balanced lines with similar processing times and provide upper bounds for throughput. [PERSON] et al. [61] presented a study of optimising performance in finite single-server acyclic queueing networks using a multi-objective methodology to minimise buffer areas and service rates while maximising throughput. The proposed approach utilises a simulated annealing algorithm to redistribute buffer spaces and improve throughput without compromising overall capacity. [PERSON] et al. [62] introduced the discrete event optimisation (DEO) methodology for the simultaneous simulation and optimisation of manufacturing systems for buffer allocation in a production line. DEO utilises mathematical programming to approximate mixed integer linear programming models, providing a formal approach for simulation optimisation in queueing systems. [PERSON] et al. [63] addressed the optimisation of buffer allocation in AMHS in intelligent manufacturing workshops, highlighting the need for more integration between production and MHS in existing research. The study proposes a flow shop model with capacitated batch transports, utilising an OQN with blocking. An approximation method is presented to compute performance measures, and an iterative optimisation algorithm is developed for determining optimal buffer allocation. [PERSON] et al. [64] presented a study identifying the optimal task assignment policy for maximising long-term average throughput. It provides insights into the preferred server coordination policies based on buffer allocation and numerical comparisons and generalisations for longer queueing lines. A co-occurrence network (Figure 2) in the relevant literature provides insights into their interconnectedness and identifies important research trends and relationships.