Genetic algorithm combined with BP neural network in hospital drug inventory management system

Du, Min; Luo, Jian; Wang, Shuping; Liu, Shan

doi:10.1007/s00521-019-04379-3

Cited by 27 publications

(22 citation statements)

References 27 publications

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“…The medical systems in Taiwan’s hospitals have a complete collection of medically related data. This study observed the medical cost of Taiwan’s hospitals and found that drug purchase costs and management costs accounted for a relatively high proportion of overall costs [ 3 , 4 , 26 ]. Kelle et al [ 15 ] and Gebicki et al [ 6 ] also mentioned that drug costs accounted for a high percentage of a hospital’s operating costs; thus, if the drug inventory can be better managed, the hospital drug management costs can be effectively reduced.…”

Section: Methodsmentioning

confidence: 99%

“…Drug inventory management is an important part of hospital management [ 1 , 2 , 3 , 4 , 5 ]. Drug costs account for 20–25% of hospital management costs.…”

Section: Introductionmentioning

confidence: 99%

“…In terms of drug demand forecasting, linear time series forecasting methods have traditionally been used, including moving average (MA), exponential smoothing (ES), linear regression (LR), the autoregressive model (AR), and the autoregressive integrated moving average model (ARIMA). In addition, many studies have used heuristic algorithms or simulation methods to optimize drug inventory [ 4 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Applying Simulation Optimization to Minimize Drug Inventory Costs: A Study of a Case Outpatient Pharmacy

Chen

Lai

et al. 2022

Healthcare

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Drug inventory management is an important part of hospital management. The large amounts of drug data in hospitals bring challenges to optimizing the setting values for the safety stock and the maximum inventory of each drug. This study combined a two-stage clustering method with an inventory policy (s, S) and established a simulation optimization model for the case hospital’s outpatient pharmacy. This research used the simulation optimization software Arena OptQuest, developed by Rockwell Automation Inc. (Rockwell Automation, Coraopolis, PA, USA), in order to determine the minimum and maximum values (s, S) of the best stock amounts for each drug under the considerations of cost and related inventory constraints. The research results showed that the minimum and maximum inventory settings for each drug in the simulation model were better than those set by the case outpatient pharmacy system. The average inventory cost was reduced by 55%, while the average inventory volume was reduced by 68%. The proposed method can improve management efficiency and inventory costs of hospital pharmacies without affecting patient services and increasing the inventory turnover rate of the drugs.

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Section: Methodsmentioning

confidence: 99%

“…Drug inventory management is an important part of hospital management [ 1 , 2 , 3 , 4 , 5 ]. Drug costs account for 20–25% of hospital management costs.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applying Simulation Optimization to Minimize Drug Inventory Costs: A Study of a Case Outpatient Pharmacy

Chen

Lai

et al. 2022

Healthcare

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“…Panda and Panda proposed an optimized ANN model, analyzed and discussed its application in nonlinear system identification and nonlinear channel equalization, and found that the proposed optimized ANN based on the slope shows better performance than the BPNN [17]. Du et al constructed a system model for the drug inventory management mode based on GA and BPNN and revealed the accuracy and thermal sensitivity of the model [18]. Imakura et al proposed an alternate optimization method in which linear and nonlinear seminonnegative matrix factorization is applied to realize the optimization of the traditional BPNN and found that the optimized method has excellent performance in parallel computing [19].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm

2021

Complexity

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This study is to explore the optimization of the adaptive genetic algorithm (AGA) in the backpropagation (BP) neural network (BPNN), so as to expand the application of the BPNN model in nonlinear issues. Traffic flow prediction is undertaken as a research case to analyse the performance of the optimized BPNN. Firstly, the advantages and disadvantages of the BPNN and genetic algorithm (GA) are analyzed based on their working principles, and the AGA is improved and optimized. Secondly, the optimized AGA is applied to optimize the standard BPNN, and the optimized algorithm is named as OAGA-BPNN. Finally, three different cases are proposed based on the actual scenario of traffic flow prediction to analyse the optimized algorithm on the matrix laboratory (MATLAB) platform by simulation. The results show that the average error distribution of the GA-BPNN algorithm is about 1% with small fluctuation range, better calculation accuracy, and generalization performance in contrast to the BPNN. The average output error of the AGA-BPNN fluctuates around 0 and remains in a relatively stable range as a whole in contrast to that of GA-BPNN; the maximum fitness level keeps increasing during the evolution process but approaches the average value in later process, so the population diversity is hard to be guaranteed. The output error of the OAGA-BPNN fluctuates little compared with that of AGA-BPNN, and its maximum fitness continues to increase in the evolution process with guaranteed population diversity. In short, the OAGA-BPNN algorithm can achieve the best performance in terms of calculation accuracy, generalization performance, and population evolution.

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“…In order to make up for the gap in the auxiliary diagnosis of orthopedics and promote the wisdom process of orthopedic disease diagnosis, Li and Zhang [14] proposed an orthopedic auxiliary classification prediction model based on XGBoost algorithm. In order to improve the efficiency of hospital drug inventory management, based on genetic algorithm and BP neural network, Luo et al [15] combined the actual situation of hospital drug inventory forecast to build a system model based on hospital drug management mode. Different from the existing algorithms which are often based on heuristic algorithms, Wang et al [16] proposed a VNE algorithm for data center topology based on the Q-learning algorithm which is a typical reinforcement learning method.…”

mentioning

confidence: 99%

Special issue on deep learning and neural computing for intelligent sensing and control

Zhou

2020

Neural Comput & Applic

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Genetic algorithm combined with BP neural network in hospital drug inventory management system

Cited by 27 publications

References 27 publications

Applying Simulation Optimization to Minimize Drug Inventory Costs: A Study of a Case Outpatient Pharmacy

Applying Simulation Optimization to Minimize Drug Inventory Costs: A Study of a Case Outpatient Pharmacy

Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm

Special issue on deep learning and neural computing for intelligent sensing and control

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