This paper focusses upon passenger flow issues within airport terminals and includes all activities occurring between curb-side and boarding. To improve passenger flow and associated planning activities, a simulation framework is developed using Discrete-Event Simulation (DES). The DES is built using ExtendSim V9.2 simulator software from Imagine That. The model can be used to evaluate the efficiency of the outbound operational processes including check-in, security screening, immigration & custom and boarding. It can also assist management to identify potential bottlenecks in the system. The main input of the model is the flight schedule. A case study of the Brisbane international airport was analysed

This research article aims to study a multi-objective linear fractional programming (FMOLFP) problem having fuzzy random coefficients as well as fuzzy pseudorandom decision variables. Initially, the FMOLFP model is converted to a single objective fuzzy linear programming (FLP) model. Secondly, we show that a fuzzy random optimal solution of an FLP problem is resolved into a class of random optimal solution of relative pseudorandom linear programming (LP) model. As a result, some of theorems show that a fuzzy random optimal solution of a fuzzy pseudorandom LP problem is combined with a series of random optimal solutions of relative pseudorandom LP problems. As an application, the developed approach is implemented to an inventory management problem by taking the parameters as trapezoidal fuzzy numbers, ultimately resulting in a new initiative for modelling real-world problems for optimization. In the last, some numerical examples are introduced to clarify the obtained results and their applicability.

Decision-making in uncertain and dynamic environments often requires models capable of accurately representing both expert disagreement and incomplete information. This paper proposes a comprehensive decision-making framework based on the Quasi-Rung Orthopair Diophantine Fuzzy (QRODF) set, which extends existing fuzzy theories by introducing five adjustable parameters \(p, q, \rho, \varrho\) and \(\:\sigma\:\) to model optimism, hesitation, and rejection with high flexibility. The fundamental operations and properties of the QRODF set are formally defined, and two aggregation operators, namely the QRODF Weighted Averaging (QRODFWA) and QRODF Weighted Geometric (QRODFWG) operators, are developed to integrate multi-expert evaluations and criteria. The Analytic Hierarchy Process (AHP) and entropy weighting methods are adapted to the QRODF environment to ensure dynamic and context-aware weight assignment. The proposed model is validated through a green supplier selection problem involving five alternatives, five criteria, and three experts. Results demonstrate strong performance with a decision accuracy of 97.8%, ranking stability of 98.6%, and an average computation time of 0.82 s, confirming both robustness and scalability. Comparative analysis shows that traditional fuzzy, intuitionistic fuzzy, and \(\:q-\)rung orthopair fuzzy models are less stable and adaptable under uncertainty. Overall, this study contributes a mathematically rigorous and practically effective framework for complex multi-criteria decision-making, offering valuable applications in sustainable manufacturing and intelligent supply chain management.