A Greedy Primal-Dual Type Heuristic to Select an Inventory Control Policy (original) (raw)
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A Heuristic for Selecting Multi-item Inventory Review Policies
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.
Point-of-Use Hybrid Inventory Policy for Hospitals
Decision Sciences, 2014
Modern point-of-use technology at hospitals has enabled new replenishment policies for medical supplies. One of these new policies, which we call the hybrid policy, is currently in use at a large U.S. Midwest hospital. The hybrid policy combines a low-cost periodic replenishment epoch with a high-cost continuous replenishment option to avoid costly stockouts. We study this new hybrid policy under deterministic and stochastic demand. We develop a parameter search engine using simulation to optimize the long-run average cost per unit time and, via a computational study, we provide insights on the benefits (reduction in cost, inventory, and number of replenishments) that hospitals may obtain by using the hybrid policy instead of the commonly used periodic policies. We also use the optimal hybrid policy parameters from the deterministic analysis to propose approximate expressions for the stochastic hybrid policy parameters that can be easily used by hospital management.
A Heuristic Approach for Integrated Storage and Shelf-Space Allocation
Lecture Notes in Management and Industrial Engineering, 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.
Optimizing Multi-Item Inventory Management Decisions in Healthcare Facilities
2017
Healthcare costs in the United States continue to grow at a significant rate. In many healthcare settings material supply and inventory management represent significant areas of opportunity for managing healthcare costs more effectively. In this dissertation, we explore three topics related to these areas. In the first chapter, we propose methodologies to help clinicians store medications and medical supplies optimally in space-constrained, decentralized Automated Dispensing Cabinets (ADCs) located on hospital patient floors. This is significant for many reasons: first, locating and storing medical supplies and pharmaceutical products within automated dispensing devices on patient floors is often not done efficiently and these devices are not utilized optimally. The primary purpose of an ADC is to ensure ready access of pharmaceuticals and medical supplies at floor locations within a hospital. However, the allocation of the limited space within an ADC to these items is typically not...
An innovative model to optimise inventory management: a case study in healthcare sector
International Journal of Services and Operations Management, 2017
In the age of competition, no industry can survive without pondering much about reducing expenditures wherever possible. Management of inventories represents one of the most important areas in business, trade and industry. The scheduling of production lots, as well as their sizing, is an area of increasing research attention within the wider field of production planning and scheduling. In the present paper a new mathematical model to find the target stocking level which minimises the total cost of the system while satisfying the service level constraint is presented. Starting from the analysis of inventory management models in literature, a completely original model has been developed and applied to a real case in healthcare sector. The analysis of the results, arising from the case study, allowed to evaluate the effectiveness of the proposed models and to highlight the differences. Nevertheless the proposed model has characteristics of generality that allow the application in other areas.
An (s, S) Inventory Optimization Problem
Advances in Public Policy and Administration, 2019
Inventory management is one essential lever to use the resources efficiently. However, managing inventories in hospitals is a challenging task because of the several issues: a high service level of medical supplies is required under the unpredictable demand, medical products constitute a significant portion of the overall costs, and the management of these supplies requires considerable effort to check the levels to track usage and to distribute them. Therefore, it is pertinence to apply operations research tools to cope with the managerial issues of the hospital inventory system. In this chapter, the authors implement an (s, S) inventory model by using simulation in a case study of a hospital in Izmir, Turkey. They aim to analyze the unpredictable nature of demand of medical supplies in this hospital and its implications on the developed inventory policy.
Multi-Item Replenishment and Storage Problem (MIRSP): Heuristics and Bounds
Operations Research, 1991
Automated warehouses are often faced with the problem of smoothing their stock volume over time in order to minimize the cost due to space acquisition. In this paper, we consider an infinite-horizon, multi-item replenishment problem: In addition to the usual setup and holding costs incurred by each item, an extra charge proportional to the peak stock volume at the warehouse is due. This last cost raises the need for careful coordination while making decisions on the individual item order policies. We restrict ourselves to the class of policies that follows a stationary rule for each item separately. We derive a lower bound on the optimal average cost over all policies in this class. Then we investigate the worst case of the Rotation Cycle policy. We show that depending on the problem's parameters, the Rotation Cycle policy may yield an extremely good solution but in other settings this heuristic may generate an extremely poor policy. We also develop a new heuristic whose performance is at least as good as that of the Rotation Cycle procedure, and moreover, it is guaranteed to come, independently of the problem's parameters, within no more than 41 % of the optimal solution! Subject classifications: Analysis of algorithms: worst case bounds. Inventory/production: storage space requirement in automated warehouses. Inventory/production, deterministic models: EOQ with annual linear cost for storage space.
In this paper we study the coordination of different activities in a supply chain issued from a real case. Multiple suppliers send raw materials (RMs) to a distribution center (DC) that delivers them to a unique plant where the storage of the RMs and the finished goods is not possible. Then, the finished goods are directly shipped to multiple customers having just-in-time ( JIT) demands. Under these hypotheses, we show that the problem can be reduced to multiple suppliers and one DC. Afterwards, we analyze two cases; in the first, we consider an uncapacitated storage at DC, and in the second, we analyze the capacitated storage case. For the first case, we show that the problem is NP-hard in the ordinary sense using the Knapsack decision problem. We then propose two exact methods: a mixed integer linear program (MILP) and a pseudopolynomial dynamic program. A classical dynamic program and an improved one using the idea of Shaw and Wagelmans are given. With numerical tests we show that the dynamic program gives the optimal solution in reasonable time for quite large instances compared with the MILP. For the second case, the capacity limitation in DC is assumed, which makes the problem solving more challenging. We propose an MILP and a dynamic programming-based heuristic that provides solutions close to the optimal solution in very short times.
A heuristic algorithm for managing inventory in a multi-echelon environment
Journal of Operations Management, 1989
The proper management of finished goods inventory in a multi-echelon environment is an extremely difficult problem to solve. Optimization approaches for solving this problem are intractable, and currently available heuristic techniques have serious deficiencies. Pull systems and independent demand based push systems do not adequately deal with the lumpy demand caused by the dependent relationships of stocking locations in a multi-echelon environment.
Storage-Space Capacitated Inventory System with ( r, Q ) Policies
Operations Research, 2007
We deal with an inventory system with limited storage space for a single item or multiple items. For the single-item system, customers' demand is stochastic. The inventory is controlled by a continuous-review r Q policy. Goods are replenished to the inventory system with a constant lead time. An optimization problem with a storage-space constraint is formulated for computing a single-item r Q policy that minimizes the long-run average system cost. Based on some existing results in the single-item r Q policy without a storage-space constraint in the literature, useful structural properties of the optimization problem are attained. An efficient algorithm with polynomial time computational complexity is then proposed for obtaining the optimal solutions. For the multi-item system, each item possesses its particular customers' demand that is stochastic, its own r Q policy that controls the inventory, and its individual lead time that is constant. An important issue in such inventory systems is the allocation of the storage space to the items and the values of r and Q for each item. We formulate an optimization problem with a storage-space constraint for multi-item r Q policies. Based on the results in the single-item r Q policy with a storage-space constraint, we find useful structural properties of the optimization problem. An efficient algorithm with polynomial time computational complexity is then proposed for obtaining undominated solutions.