Outpatient clinics are integral to healthcare, offering vital services without the need for hospital admission. However, appointment scheduling in these settings remains challenging due to uncertainties such as patient no-shows and variable service times. This study proposes a data-driven approach to minimize physician idle time and patient waiting time by analyzing an eight-year (2016-2023) dataset from primary and specialized care for American veterans. Adopting the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, four predictive models: Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Artificial Neural Networks (ANN), were developed for both classification (noshow) and regression (service time) tasks. The ANN model demonstrated superior predictive performance in both domains. The key predictors of no-shows included waiting time, type of care and care provider, while type of care, care provider, and veteran ZIP code were the most influential in forecasting service time. These findings highlight the potential of machine learning to improve appointment scheduling in outpatient clinics.

Amid growing concerns over the environmental and social impacts of supply chains, sustainable network design has become a key focus in both research and practice. This paper presents a comprehensive review of 184 publications on network design in sustainable supply chains. We explore how sustainability dimensions, especially the often-overlooked social aspect, are integrated into network design decisions. The review categorizes the main modeling approaches, objectives, and uncertainty considerations, and highlights emerging trends such as circular economy and responsible sourcing. Finally, we identify research gaps and outline future directions, with a particular emphasis on enhancing the social sustainability of supply chain networks.

Effective replenishment planning and inventory control are essential for the smooth operation and adaptability of supply chains. These aspects play a pivotal role in upholding a company’s competitiveness and triumph in today’s fiercely competitive markets. Supply chain planners encounter significant hurdles in choosing the most appropriate suppliers in diverse scenarios, reducing average inventory levels, and determining optimal safety lead times. This research tackles these challenges by examining and evaluating a multi-period replenishment planning issue within the framework of dynamic demand and multiple suppliers. The suppliers are pre-selected and defined by procurement costs, with lead times considered as independent discrete random variables with known and limited probability distributions. The goal is to optimize the distribution of order quantities among these pre-selected suppliers while minimizing the anticipated total cost. Two strategies and corresponding linear models are suggested to investigate the impact of dividing orders between suppliers, order crossover, and order flexibility. Numerical experiments provide evidence that concurrently considering splitting and flexibility yields benefits in terms of cost optimization.

This article studies a bi-objective stochastic optimization problem for multi-period multi-sourcing supply planning. The formulated problem accounts for stochastic lead times, degressive pricing, holding and backlog costs, delivery flexibility costs, as well as both holding and transportation carbon footprint. The first objective is to minimize the expected total cost, while the second objective is to minimize the expected total footprint. These objectives must be achieved while adhering to suppliers’ capacity constraints and meeting customer demand. In this paper, the proposed stochastic integer linear program is detailed, and the ϵ-constraint method used to solve it is described. The first results of experiments are presented and discussed.

In order to meet item demands, end-of-life (EOL) product and subassembly ordering and disassembly schedules are determined by disassembly lot sizing, which is the subject of this study. We take into consideration a stochastic version with undetermined ordering lead time (OLT). In this case, OLT stands for the amount of time that passes between placing and receiving an order (we can only order EOL products). Throughout the planning horizon, scenarios are used to model the stochasticity. The objective is to reduce the expected total of setup, purchasing, inventory, and backlog expenses. This is achieved by expressing the problem as a two-stage mixed integer linear programming (2S-MILP) model across all potential scenarios. The 2S-MILP is unsolvable since it is predicated on every scenario conceivable. A Simultaneous Backward Reduction approach is proposed to make it tractable. To confirm the suggested method’s efficacy, it is assessed in a variety of environments.

This paper studies the assembly line balancing problem with collaborative robots in light of recent efforts to implement collaborative robots in industrial production systems under random processing time. A stochastic version with uncertain human processing time is considered for the first time. The issue is defined by the potential for simultaneous human and robot task execution at the same workpiece, either in parallel or in collaboration. We provide stochastic mixed-integer programming based on Monte Carlo sampling approach for the balancing and scheduling of collaborative robot assembly lines for this novel issue type. In order to minimise the line cost including fixed workstation operating costs and resource costs caused by exceeding cycle time, the model determines both the placement of collaborative robots at stations and the distribution of work among humans and robots.