On perishable inventory in healthcare: Random expiration dates and age discriminated demand

The management of perishable inventories is particularly challenging in healthcare field. One of the reasons lashing this is volatility, which is driven by irregular supply and stochastic demand. Exacerbated by a number of other factors such as the relatively short products shelf life, accelerated degradation and deterioration (premature expiration) and user specificity, such irregular supply and stochastic demand of perishable inventories bring additional challenges to the delivery of health services. This variability is likely to expose not only those in direct receipt of such healthcare products, but also the general population to insecure supplies. The norm in perishable products is to set an expiration date by which the product is best consumed. However, deterioration in products may accelerate, thereby; causing products to expire prematurely in random before their anticipated expiration date. With this in mind, the aim of the study is to explore how best to mitigate against inventory volatility in perishable inventory, which is characterized by random premature expiration, random demand, irregular supply, age differentiated demand and custom replenishment guidelines. Through the adoption of simulation-optimization along with new settings and replenishment policies, the optimized quantity level of daily orders could be determined for this combination of inventory restrictions. Owed to their custom medical compatibility guidelines, and their notable accelerated expiration, blood platelets were considered here. As study outcome, the emergent model presents a perspective of supply chains and their healthcare imperatives that will enable healthcare supply chain managers not only to discern, but also to interpret and facilitate the management and implementation of optimal inventories.<br/

Rolling-Horizon Heuristics for Capacitated Stochastic Inventory Problems with Forecast Updates

In this paper we propose a practical optimization approach based on the rolling-horizon paradigm to address general single-product periodic-review inventory control problems. Our framework supports many constraints and requirements that are found in real inventory problems and does not rely on any assumption on the statistical distribution of random variables. Ambiguous demand and costs, forecast updates, constant lead time, lost sales, flexible inventory capacity and product availability can all be taken into account. Three, increasingly sophisticated, solution methods are proposed and implemented within our optimization framework: a myopic policy, a linear programming model with risk penalization and a scenario-based stochastic programming model. The effectiveness of our approach is proved using a dataset of realistic instances