Hybrid ordering policies for platelet inventory management under demand uncertainty

The benefits (or detriments) of adapting to demand disruptions in a hospital pharmacy with supply chain disruptions

Supply chain disruptions and demand disruptions make it challenging for hospital pharmacy managers to determine how much inventory to have on-hand. Having insufficient inventory leads to drug shortages, while having excess inventory leads to drug waste. To mitigate drug shortages and waste, hospital pharmacy managers can implement inventory policies that account for supply chain disruptions and adapt these inventory policies over time to respond to demand disruptions. Demand disruptions were prevalent during the Covid-19 pandemic. However, it remains unclear how a drug's shortage-waste weighting (i.e., concern for shortages versus concern for waste) as well as the duration of and time between supply chain disruptions influence the benefits (or detriments) of adapting to demand disruptions. We develop an adaptive inventory system (i.e., inventory policies change over time) and conduct an extensive numerical analysis using real-world demand data from the University of Michigan's Central Pharmacy to address this research question. For a fixed mean duration of and mean time between supply chain disruptions, we find a drug's shortage-waste weighting dictates the magnitude of the benefits (or detriments) of adaptive inventory policies. We create a ranking procedure that provides a way of discerning which drugs are of most concern and illustrates which policies to update given that a limited number of inventory policies can be updated. When applying our framework to over 300 drugs, we find a decision-maker needs to update a very small proportion of drugs (e.g., < 5 % ) at any point in time to get the greatest benefits of adaptive inventory policies.

A multi-product multi-period stochastic model for a blood supply chain considering blood substitution and demand uncertainty

This paper presents a multi-product multi-period stochastic program for an integrated blood supply chain that considers red blood cells and platelets while accounting for multi-product interactions, demand uncertainty, blood age information, blood type substitution, and three types of patients. The aim is to minimize the total cost incurred during the collection, production, inventory, and distribution echelons under centralized control. The supply chains for red blood cells and platelets intertwine at the collection and production echelons as collected whole blood can be separated into red blood cells and platelets at the same time. By adapting to a real-world blood supply chain with one blood center, three collection facilities, and five hospitals, we found a cost advantage of the multi-product model over an uncoordinated model where the red blood cell and platelet supply chains are considered separately. Further sensitivity analyses indicated that the cost savings of the multi-product model mainly come from variations in the number of whole blood donors. These findings suggest that healthcare managers are able to see tremendous improvement in cost efficiency by considering red blood cell and platelet supply chains as a whole, especially with more whole blood donations and a higher percentage of whole blood derived platelets pooled for transfusion.

Multiobjective location-routing problem of relief commodities with reliability

The applicability and efficiency of location-routing problems for relief commodities motivate several researchers to develop optimization models and algorithms with regards to real-world cases. This study by presenting a multiobjective mixed integer mathematical model for the location-routing of the medical relief problem at the time of a disaster, proposes a new extension to this applicable model with reliability considerations. The proposed model focuses on the location of temporary relief centers and delivers the pharmaceutical commodities to centers at the shortest possible time with reliability assurance. The model includes three simultaneous objectives of minimizing response time, minimizing operational costs, and maximizing the reliability of the transportation network. As far as we know, this study firstly optimizes these three objectives simultaneously. Another novelty is to add the uncertainty of the problem. In this regard, the inherent uncertainty is formulated by a scenario-based approach. Considering the multiobjectiveness of the proposed model, the Epsilon constraint method as a solution algorithm has been used to solve the model. Results are tested for numerical examples via different scenarios. The results represent the excellent performance of the model to minimize the costs and to increase the reliability for the proposed location-routing problem of relief commodities.

A simple empirical inventory model for managing the processed corneal tissue equitably in hospitals with demand differentiation

This paper peruses one of the most challenging healthcare concerns related to human organ transplantation. In this regard, a medical system’s health-economic objectives are investigated to (1) achieve a desirable level of health equity in offering healthcare services to patients who are differentiated in terms of medical conditions, and (2) minimize total costs incurred across managing the inventory. This paper presents the first-ever operational research study to manage processed corneal tissue (PCT). Besides, hybrid demand-oriented policies have been proposed to equitably issue and assign specific inventories to demands with distinct medical urgent levels (ULs). In this regard, a practical multi-objective mixed-integer linear programming (MOMILP) model is proposed for a system with multiple products that can be substituted with each other according to medical priorities. A goal programming (GP) approach is utilized to find the optimum solution. The applicability of the model is validated through a real case study. Finally, several sensitivity analyses are conducted to examine the effect of critical parameters on the solutions to gain useful managerial insights. The results show that the proposed health-economic trade-off is well performed and can efficiently handle the real case.

Stochastic Inventory Model for Minimizing Blood Shortage and Outdating in a Blood Supply Chain under Supply and Demand Uncertainty

Purpose

Blood, like fresh produce, is a perishable element, with platelets having a limited lifetime of five days and red blood cells lasting 42 days. To manage the blood supply chain more effectively under demand and supply uncertainty, it is of considerable importance to developing a practical blood supply chain model. This paper proposed an essential blood supply chain model under demand and supply uncertainty.

Methods

This study focused on how to manage the blood supply chain under demand and supply uncertainty effectively. A stochastic mixed-integer linear programming (MILP) model for the blood supply chain is proposed. Furthermore, this study conducted a sensitivity analysis to examine the impacts of the coefficient of demand and supply variation and the cost parameters on the average total cost and the performance measures (units of shortage, outdated units, inventory holding units, and purchased units) for both the blood center and hospitals.

Results

Based on the results, the hospitals and the blood center can choose the optimal ordering policy that works best for them. From the results, we observed that when the coefficient of demand and supply variation is increased, the expected supply chain cost increased with more outdating units, shortages units, and holding units due to the impacts of supply and demand fluctuation. Variation in the inventory holding and expiration costs has an insignificant effect on the total cost.

Conclusions

The model developed in this paper can assist managers and pathologists at the blood donation centers and hospitals to determine the most efficient inventory policy with a minimum cost based on the uncertainty of blood supply and demand. The model also performs as a decision support system to help health care professionals manage and control blood inventory more effectively under blood supply and demand uncertainty, thus reducing shortage of blood and expired wastage of blood.