The public sector is becoming increasingly appealing. In the context of declining public money to support health studies and public health interventions, public–private partnerships with entities (including government agencies and scientific research institutes) are becoming increasingly important. When forming this type of cooperation, the participants highlight synergies between the private partners and the public’s missions or goals. The tasks of private and public sector actors, on the other hand, frequently diverge significantly. The integrity and honesty of public officials, institutions, trust, and faith in those individuals and institutions may all be jeopardized by these collaborations. In this study, we use the institutional corruption framework to highlight systemic concerns raised by PPPs affiliated with the governments of one of South Asia’s countries. Overall analytical frameworks for such collaborations tend to downplay or disregard these systemic impacts and their ethical implications, as we argue. We offer some guidelines for public sector stakeholders that want to think about PPPs in a more systemic and analytical way. Partnership as a default paradigm for engagement with the private sector needs to be reconsidered by public sector participants. They also need to be more vocal about which goals they can and cannot fulfill, given the limitations of public financing resources.

As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm.

Any country’s healthcare system is vital for its progress, quality of life, and long-term viability. During the pandemic, many developed countries encountered challenges of differing degrees in the administration of their healthcare systems. The overloading of healthcare services is common, leading to prolonged waiting times for medical services. Thus, the wastage of hospital resources must be taken seriously. In this paper, we examine the problem of no-shows and cancellations in outpatient clinics. By examining the literature and drawing from practical industry experience, we uncover the operational procedures of these clinics. We then suggest a robust optimization strategy for overbooking, incorporating both a conventional overbooking model and a resilient system approach. The proposed model seeks to address the substantial uncertainties in parameters encountered during the pandemic. Taking into account risk aversion, we develop an optimal overbooking policy that considers the associated costs. The primary contribution lies in introducing an alternative approach to manage the uncertainty of no-shows and cancellations through the utilization of an overbooking technique.

The healthcare system is crucial to the development, standard of living, and sustainability of any country. Amid the pandemic, most developed countries are experiencing varying levels of difficulty managing their healthcare systems. Overloading of healthcare services is common, leading to prolonged waiting times for medical services; thus, the wastage of hospital resources must be addressed seriously. In this paper, we examine the problem of no-shows in outpatient clinics. Through a review of the literature and practical industrial experience, we identify the processes of these clinics. We then propose a robust optimisation approach for overbooking using a traditional overbooking model and a robust system. This model aims to mitigate the significant parameter uncertainties encountered during the pandemic. Considering risk aversion, the optimal policy for overbooking can be established while accounting for the costs associated with overbooking. The main contribution of this paper is the introduction of an alternative method to address the uncertainty of no-shows using an overbooking technique.

Background/Objectives: Hospital-wide bed pooling is widely used in bed-management reform to reduce ward-level mismatch and improve the use of scarce inpatient capacity. However, formally pooled beds are not automatically usable beds. Cross-ward reassignment may require coordination, placement judgment, and timely execution, especially when several wards are under pressure. This study examines hospital bed governance as a healthcare management problem of where to draw the boundary between local protection and centralized pooling. Methods: We develop an $\alpha$-based governance framework that places full decentralization, bounded centralization, and full centralization within a common design space. The parameter $\alpha$ determines how much ward-level capacity is pooled and how much remains locally protected. The analysis first establishes a frictionless pooling benchmark, and then introduces coordination friction, stress-state pooled-allocation imperfection, and the protective value of local capacity. Scenario-based computational experiments examine the mechanism across operating conditions, alternative friction specifications, and multi-ward extensions. Results: In the frictionless benchmark, full centralization weakly dominates because it has the broadest feasible allocation set. When pooled allocation remains fully effective, full centralization is not substantively outperformed. Coordination friction can move the raw maximizing boundary inward, but this movement does not necessarily imply a meaningful improvement over full centralization. When reassignment becomes less reliable in high-pressure states, however, an interior governance boundary can become substantively attractive by preserving some locally accessible capacity. Conclusions: The findings do not reject centralized pooling. They suggest that bed reform should evaluate both the formal scope of pooling and the operational usability of pooled capacity under pressure.