Resilient supplier selection and optimal order allocation have become increasingly critical due to the rising risk of disruptions in procurement. This study proposes a novel two-stage approach that combines a Bow-Tie-based risk assessment method with a mathematical optimization model to address this challenge. In the first stage, a Bow-Tie diagram is used to evaluate disruption risks and quantify supplier resilience by analyzing their ability to absorb, adapt to, and recover from adverse events. To our knowledge, this is the first application of the Bow-Tie model in the context of supply chain disruptions. In the second stage, the resilience scores derived from the Bow-Tie analysis are integrated into a bi-objective mixed-integer linear programming (MILP) model that allocates orders to suppliers based on both cost efficiency and resilience performance while accounting for product substitution and delayed fulfillment under disruption scenarios. The proposed methodology is applied to a vaccine procurement supply chain, and the results provide actionable insights for decision-makers seeking to balance cost and resilience in supplier selection.

Real life stochastic problems are generally large-scale, difficult to model, and therefore, suffer from the curses of dimensionality. Such problems cannot be solved by classical optimization methods. This paper presents a reinforcement learning algorithm using a fuzzy inference system, ANFIS to find an approximate solution for semi Markov decision problems (SMDPs). The performance of the developed algorithm is measured and compared to a classical reinforcement algorithm, SMART in a numerical example. Our numerical examples show that the developed algorithm converges significantly faster as the problem size increases and the average cost calculated by the algorithm gets closer to that of SMART as number of epochs used in the developed algorithm is increased.

This research is the first to introduce a mathematical model for hybrid workforce scheduling with a focus on enhancing in-person interaction. By integrating organizational requirements and individual preferences, it provides a framework that can be tailored to different institutional needs, offering a valuable tool to managers aiming to design hybrid work schedules. A mixed-integer linear programming model is developed to optimize weekly hybrid work schedules by maximizing in-office interactions and minimizing conflicts with employees’ remote work preferences. Numerical experiments, based on randomly generated instances, explore trade-offs between interaction and satisfaction under varying parameter values, including minimum office attendance, fixed office days, and penalties for violating employee preferences. The findings provide practical insights for managers: (1) Increasing structured office attendance substantially enhances interaction but sharply decreases employee satisfaction, particularly when fixed office days remove individual choice. (2) Skewed remote work preferences further complicate scheduling, potentially increasing dissatisfaction and reducing flexibility. (3) However, measures such as moderate enforcement of in-office presence achieves a balance, maintaining relatively high interaction with limited dissatisfaction. The proposed model can serve as a practical decision-support tool, adaptable to diverse organizational contexts and policies, offering a customized approach to equitable hybrid workforce planning.

Demand, inventory, and production continuously influence one another in supply chains. Sudden demand changes, inaccurate records, production disruptions, and external factors can create irregular fluctuations. Although demand or sales data are often available, inventory and production information may be incomplete or delayed. This study applies the Extended Kalman Filter (EKF) to jointly estimate inventory, production quantity, and three model parameters using demand as the only measured variable. Its main contribution is a recursive structure that updates the state and parameter estimates with each new measurement. The method is tested on a three-variable supply chain model exhibiting chaotic behavior. Performance is assessed through numerical errors, convergence, time series, phase portraits, and bifurcation diagrams. Robustness to different initial estimates, sensitivity to process- and measurement-noise covariance settings, and performance under chaotic, semi-chaotic, and ordered regimes are also examined. Parameter errors range from 1.5% to 2.4%, while normalized errors for demand, inventory, and production remain below 3%. A larger assumed measurement-noise covariance slows convergence. Under the selected conditions, the closest agreement occurs in the chaotic regime, but this is not interpreted as a general superiority of the EKF. With real-data validation, the framework could support estimation of missing or delayed information, short-term monitoring, early warning, and decision support.