Esmaili, N, Norman, BA & Rajgopal, J 2019, 'Exact analysis of (R, s, S) inventory control systems with lost sales and zero lead time', NAVAL RESEARCH LOGISTICS, vol. 66, no. 2, pp. 123-132.View/Download from: UTS OPUS or Publisher's site
Esmaili, N, Norman, B & Rajgopal, J 2018, 'Shelf-space optimization models in decentralized automated dispensing cabinets', Operations Research for Health Care, vol. 19, pp. 92-92.View/Download from: UTS OPUS or Publisher's site
We propose a mixed integer programming (MIP) model to help clinicians store medications and medical supplies optimally in space-constrained, decentralized Automated Dispensing Cabinets (ADCs) located on hospital patient floors. We also propose a second MIP model that addresses human errors associated with the selection of pharmaceuticals from floor storage, and not only selects the best set of medications for storage but also determines their optimal layout within the cabinet. To improve the computational performance of these MIP models, we investigate several valid inequalities and relaxations that allow us to solve large, real-world instances in reasonable times. These models are applicable to very general ADCs and are illustrated using real-world data from ADCs at hospitals. Our results indicate that using these models can significantly reduce the time spent by clinical staff on routine logistical functions, while making efficient use of limited space and decreasing risks associated with errors in the selection of medication.
Esmaili, N, Norman, B & Rajgopal, J 2017, 'A Greedy Primal-Dual Type Heuristic to Select an Inventory Control Policy' in Engineering Systems and Networks, Springer.
© Springer International Publishing Switzerland 2015. We address the joint allocation of storage and shelf-space, using an application motivated by the management of inventory items at Outpatient Clinics (OCs). OCs are limited health care facilities that provide patients with convenient outpatient care within their own community, as opposed to having them visit a major hospital. Currently, patients who are prescribed a prosthetics device during their visit to an OC must often wait for it to be delivered to their homes from a central storage facility. An alternative is the use of integrated storage cabinets at the OCs to store commonly prescribed inventory items that could be given to a patient immediately after a clinic visit. We present, and illustrate with an actual example, a heuristic algorithm for selecting the items to be stocked, along with their shelf space allocations. The objective is to maximize total value based on the desirability of stocking the item for immediate dispensing. The heuristic model considers cabinet characteristics, item size and quantity, and minimum and maximum inventory requirements in order to arrive at the best mix of items and their configuration within the cabinet.
Esmaili, N, Norman, BA & Rajgopal, J 2015, 'A heuristic for selecting multi-item inventory review policies', IIE Annual Conference and Expo 2015, pp. 1050-1059.
The frequent use of PAR levels for controlling inventories, and the associated manual effort for tracking usage and ordering replenishments leads to a great deal of inefficiency in inventory management in hospitals and clinics. These processes not only waste significant time and money but also result in numerous errors. Given that each item has its own unique characteristics, the best inventory control system for individual items might be different, and selecting the best system can greatly enhance performance and efficiency. The current practice of simply using the Periodic Automatic Replenishment (PAR) level as the primary approach results in outcomes that are inefficient and time consuming. We present a heuristic algorithm for selecting the best inventory control policy for each item, with the objective of minimizing the average effort to replenish items over a suitable interval of time subject to limited storage space availability. We consider the PAR level and two-bin Kanban policies as they represent two common inventory control approaches in healthcare. We also consider (S, s) and (Q, s) policies even though they are less common in hospitals. We illustrate the model with actual data from a hospital.