A centralized stochastic inventory control model for perishable products considering age-dependent purchase price and lead time

• Ehsan Ahmadi ^[1] ; Dale T. Masel ^[2] ; Seth Hostetler ^[3] ; Reza Maihami ^[1] ; Iman Ghalehkhondabi ^[1]
  1. [1] Our Lady of the Lake University

     Our Lady of the Lake University

     Estados Unidos

     
  2. [2] Ohio University

     Ohio University

     Township of Athens, Estados Unidos

     
  3. [3] Center for Healthcare Systems Re-engineering Department, Geisinger Health System, USA

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• Localización: Top, ISSN-e 1863-8279, ISSN 1134-5764, Vol. 28, Nº. 1, 2020, págs. 231-269
• Idioma: inglés
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  • In this paper, we present a stochastic multi-period multi-echelon (s,S) periodic inventory control model for perishable products in a healthcare environment. We consider age-dependent purchase price, order lead time, and allow for backorder. The model is inspired by a real case where a regional health system operates multiple hospitals in addition to managing a central warehouse. The warehouse centralizes the purchasing of the products and then distributes them among hospitals while demand for products in each hospital is uncertain. We formulate the problem as a stochastic mixed-integer linear programming model and study two purchasing strategies. The first and traditional strategy assumes that the warehouse only orders fresh products, i.e., products with the latest expiry date and most expensive, referred to as purchasing constant remaining life (CRL). The second strategy considers a discount function with a lower purchase price for products with a shorter remaining life, referred to as different remaining life (DRL). The designed models are naturally complex and intractable to obtain optimal solutions by an analytical approach for large instances of the problem. Thus, we developed a genetic algorithm to solve the problem and benchmarked its performance against an exact method. We also verified the results of the stochastic models through simulation. Extensive sensitivity analysis is also conducted to investigate the behaviors of both CRL and DRL strategies about the risk of product shortage, product expiration and the total cost imposed on the system.