Optimal buffer allocations can significantly improve system throughput by managing variability and disruptions in manufacturing or service operations. Organisations can minimise waiting times and bottlenecks by strategically placing buffers along the flow path, leading to a smoother and more efficient production or service delivery process. Determining the optimal size of buffers poses a challenging dilemma, as it involves balancing the cost of buffer allocation, system throughput, and waiting times at each service station. This paper presents a framework that utilises finite queueing networks for performance analysis and optimisation of topologies, specifically focusing on buffer allocations. The proposed framework incorporates a finite closed queuing network to model the intra-logistics material transfer process and a finite open queueing network to model the outbound logistics process within a manufacturing setup. The generalised expansion method (GEM) is employed to calculate network performance measures of the system, considering the blocking phenomenon. Discrete event simulation (DES) models are constructed using simulation software, integrating optimisation configurations to determine optimal buffer allocations to maximise system throughput. The findings of this study have significant implications for decision-making processes and offer opportunities to enhance the efficiency of manufacturing systems. By leveraging the proposed framework, organisations can gain valuable insights into supply chain performance, identify potential bottlenecks, and optimise buffer allocations to achieve improved operational efficiency and overall system throughput.

The focus of manufacturing organisations on their core competencies has placed third-party logistics service providers (3PL) in an ideal position to deal with supportive activities such as material handling, transportation, and storage using their expertise and economies of scale. This study considers the problem of determining the optimal fleet size of heterogeneous trucks to be outsourced from a 3PL to fulfil the demand of various raw materials requirements by a production facility while achieving a minimum total cost of these daily operations. Trucks are deployed in an interfacility material transportation system with different raw materials from designated storage areas to be transported to specified buffer locations per the production requirements. In a typical material forward flow, a truck undergoes many sub-processes with stochastic service times that vary with the type of material it carries and the type of the truck. Each material encompasses different physical attributes and specific job routings. Within this context, the inter-facility transportation process is modelled as a closed queueing network (CQN), and a mathematical model is developed to determine the optimal number of heterogeneous trucks to be outsourced from a 3PL while fulfilling the production requirements. A Discrete event simulation model, using Anylogic simulation software, is employed for solving the model to determine the optimal fleet size of trucks and their specific heterogeneous composition. Moreover, the simulation model is used to determine the main performance measures of the system, such as sub-process response times, queue lengths, cycle times, resource utilisations and bottlenecks, to assist in the decision-making process.

Queueing is a common phenomenon in manufacturing and service environments. Practitioners often use queueing network models to analyse these systems for decision-making processes. However, finite queueing systems with blocking are used to a lesser extent due to the complexity of solving the systems. Generally, closed queueing networks (CQN) with finite capacities do not lend themselves to product-form solutions and must be solved using approximation algorithms. This study considers the problem of determining the optimal buffers allocation for an inter-facility material transportation system in a manufacturing facility considering the blocking after service (BAS) phenomenon. Under BAS, a job can only reach the next node until there is space available in the queue of the next node. If the queue capacity of the destination node is full, the job in the preceding node, even after being serviced, will block that server. In a typical material flow, trucks transfer materials between the facility and undergo several sub-processes such as gate entry, weighing, loading, and unloading. This material flow process is modelled using a CQN, and each sub-process is designed as a node with a finite queueing capacity. An analytical model is developed within this context to determine the optimal buffer sizes to maximise the system's throughput. More specifically, a simulation model is developed using the Anylogic software, and the optimisation problem is incorporated into the model to determine the optimal buffer allocations. The Anylogic simulation-optimisation experiment engine uses various metaheuristics through the OptQuest tool to determine the non-dominated solutions of the corresponding CQN.