Optimisation and control of the supply of blood bags in hemotherapic centres via Markov decision process with discounted arrival rate

Soares, Henrique; Arruda, Edilson F.; Bahiense, Laura; Gärtner, Daniel; Filho, Luiz Amorim

doi:10.1016/j.artmed.2020.101791

Cited by 9 publications

(6 citation statements)

References 70 publications

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“…All combined 394. 5 recovered patients in Québec) and CBS (which was not permitted access to contacts of recovered patients). Our experience shows that the three components (hub-andspoke distribution model, live CCP inventory system, and mathematical models for CCP allocation) together ensured the efficiency and equitability of CCP allocation in the RCT.…”

Section: Discussionmentioning

confidence: 99%

“…[1][2][3][4] Diverse types of decisions need to be made, including determining the location of blood centers, the capacity of collection processes, inventory policies, issuing policies, and emergency ordering policies. [5][6][7][8] Shortage and wastage of blood products may result in increased costs, delays in treatments, untreated patients, and even deaths. 9,10 Hence, designing and optimizing the decision processes for scarce blood product allocation is vital.…”

Section: Introductionmentioning

confidence: 99%

“…Blood product allocation in the blood supply chain requires complex decisions involving blood collection, processing, transportation with the maintenance of the cold chain, inventory, pre‐transfusion manipulation/testing, and transfusion from donors to patients 1–4 . Diverse types of decisions need to be made, including determining the location of blood centers, the capacity of collection processes, inventory policies, issuing policies, and emergency ordering policies 5–8 . Shortage and wastage of blood products may result in increased costs, delays in treatments, untreated patients, and even deaths 9,10 .…”

Section: Introductionmentioning

confidence: 99%

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A data‐informed system to manage scarce blood product allocation in a randomized controlled trial of convalescent plasma

Zeller

Shih

et al. 2022

Transfusion

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Background Equitable allocation of scarce blood products needed for a randomized controlled trial (RCT) is a complex decision‐making process within the blood supply chain. Strategies to improve resource allocation in this setting are lacking. Methods We designed a custom‐made, computerized system to manage the inventory and allocation of COVID‐19 convalescent plasma (CCP) in a multi‐site RCT, CONCOR‐1. A hub‐and‐spoke distribution model enabled real‐time inventory monitoring and assignment for randomization. A live CCP inventory system using REDCap was programmed for spoke sites to reserve, assign, and order CCP from hospital hubs. A data‐driven mixed‐integer programming model with supply and demand forecasting was developed to guide the equitable allocation of CCP at hubs across Canada (excluding Québec). Results 18/38 hospital study sites were hubs with a median of 2 spoke sites per hub. A total of 394.5 500‐ml doses of CCP were distributed; 349.5 (88.6%) doses were transfused; 9.5 (2.4%) were wasted due to mechanical damage sustained to the blood bags; 35.5 (9.0%) were unused at the end of the trial. Due to supply shortages, 53/394.5 (13.4%) doses were imported from Héma‐Québec to Canadian Blood Services (CBS), and 125 (31.7%) were transferred between CBS regional distribution centers to meet demand. 137/349.5 (39.2%) and 212.5 (60.8%) doses were transfused at hubs and spoke sites, respectively. The mean percentages of total unmet demand were similar across the hubs, indicating equitable allocation, using our model. Conclusion Computerized tools can provide efficient and immediate solutions for equitable allocation decisions of scarce blood products in RCTs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A data‐informed system to manage scarce blood product allocation in a randomized controlled trial of convalescent plasma

Zeller

Shih

et al. 2022

Transfusion

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“…Research on optimizing the supply chain for blood and blood platelets was being studied using different methodologies. These include simulation & Integer programming (Osorio et al, 2016, Najafi et al, 2017), Stochastic Programming ( Hosseini-Motlagh et al, 2019, Hamdan, Diabat, 2019, Zahiri and Pishvaee, 2016, Markov Decision Process (Haijema et al, 2007), Soares et al, 2020, Abbaspour et al, 2020, Approximate Dynamic Programming (ADP) (Abdul Wahab and Wahab, 2014, Powell and Topaloglu, 2014, Fang et al, 2013, Goal Programming (Mahdi Yousefi Nejad Attari, 2017), Mixed Integer Programming (Zahiri et al, 2018, Ensafian and Yaghoubi, 2017, Yaghoubi et al, 2019), Taguchi Method (Seyed Mojib Zahraee et al, 2015.…”

Section: Literature Reviewmentioning

confidence: 99%

Dual Supply Blood Platelet Inventory Management: An Approximate Dynamic Programming Approach

Kundavaram

2024

Preprint

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This report formulates a dual-source model that considers artificial blood along with natural blood as sources of blood platelet supply. The model is formulated for a single hospital by considering deterministic lead time while ordering, uncertainty in supply, and demand. A newsvendor model is used to find the optimal order quantity for each day and approximate dynamic programming (ADP) is used to deal with uncertainty over a year. Three different step size rules are being studied and compared in terms of average value function approximation (VFA) per timestep, with two being deterministic step size and the other being an optimal stochastic step size to deal with non-stationary data. The project aims is to select a policy that can reduce shortages, outages, and maximize the rewards for the developed model. Eleven policies are being investigated while introducing artificial blood platelet rewards. Numerical analysis is implemented by considering varied demand and donations for different acceptance percentages and inventory levels of artificial blood under each policy. Another analysis is done to show how shortage and outage values vary for a single source supply vs a dual source (0% acceptance vs 50% acceptance) and are computed for three policies. Computed results show that first in first out (FIFO) is the best policy with the least shortage and outage for various acceptance rates, for varied demand and donations. As the acceptance rate and the inventory level for artificial blood are increased, for 10% a shortage percentage of 5.8% along with 5.9% of outage for natural and 8.8% for artificial is observed. When increased to 30%, 40%, we see an increase in the outage of natural platelets from5.9% to 6.1% along with shortage levels slightly falling. For 50% shortage values falls below 5.2%, outage values fall below 5.7% with outage of artificial from 9% to 8.6%. For the second analysis 0% vs 50%, considering a secondary source can help reduces shortage from 5.8% to 5.1% and outage from 6.3% to 5.7%.

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“…Blood transfusion is an important part of contemporary medicine. 85 million blood transfusions are carried out annually across the globe, which translates to three blood transfusions per second approximately [1,2]. Therefore, sustainable blood donation provided is important.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Demand for Red Blood Cells Using Artificial Intelligence Methods

GÖKLER

BORAN

2022

Academic Platform Journal of Engineering and Smart Systems

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Blood is a vital product with limited resources, available only from volunteers. For this reason, the blood components to be sent from the blood bank to the transfusion centers (hospitals) should be accurately predicted. There are many variables that affect the demand prediction. In this study, fifteen different qualitative and quantitative variables were determined. Artificial intelligence (AI) methods are used because the prediction has nonlinear, complex and uncertain relationships and thus it is also difficult to mathematically express on relationship in between input and output variables. AI methods have the feature of predicting the information that is not given or that may occur in the future by learning the past data. In the study, AI methods such as Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Deep Learning (DL) were applied to blood bank providing blood supply to public and private hospitals operating in four provinces. The data obtained from the prediction results of AI methods were compared with performance criteria (MAPE, MSE, MAE RMSE and R2) and values of overprediction, underprediction, minimum and maximum deviation. The weekly average over predictions are calculated as 9.69, 5.29, 8.45, and 15.65 and weekly average underpredictions as 17.57, 3.03, 3.94, and 14.69 for DT, SVM, ANN, and DL methods, respectively. SVM method was determined as giving the best prediction values. Therefore, it is envisaged that the blood component demand prediction can be calculated using the SVM method.

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Optimisation and control of the supply of blood bags in hemotherapic centres via Markov decision process with discounted arrival rate

Cited by 9 publications

References 70 publications

A data‐informed system to manage scarce blood product allocation in a randomized controlled trial of convalescent plasma

A data‐informed system to manage scarce blood product allocation in a randomized controlled trial of convalescent plasma

Dual Supply Blood Platelet Inventory Management: An Approximate Dynamic Programming Approach

Prediction of Demand for Red Blood Cells Using Artificial Intelligence Methods

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