Enhancing Efficiency in the Healthcare Sector Through Multi-Objective Optimization of Freight Cost and Delivery Time in the HIV Drug Supply Chain Using Machine Learning DOI Creative Commons

Amirkeyvan Ghazvinian,

Bo Feng, Junwen Feng

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(2), P. 91 - 91

Published: Jan. 31, 2025

The purpose of this paper is to examine the optimization HIV drug supply chain, with a dual focus on minimizing freight costs and delivery times. With help dataset containing 10,325 instances chain transactions, key variables, including “Country”, “Vendor INCO Term”, “Shipment Mode”, were examined in order develop predictive model using Artificial Neural Networks (ANN) employing Multi-Layer Perceptron (MLP) architecture. A set ANN models trained forecast “freight cost” “delivery time” based four principal design variables: “Line Item Quantity”, “Pack Price”, “Unit Measure (Per Pack)”, “Weight (Kilograms)”. According performance metrics analysis, these demonstrated accuracy following training. An algorithm, configured an “active-set” was then used minimize combined objective function cost time. Both times significantly reduced as result optimization. This study illustrates potent application machine learning algorithms enhancement efficiency. provides blueprint for reduction improved service critical medication chains methodology outcomes.

Language: Английский

Enhancing Efficiency in the Healthcare Sector Through Multi-Objective Optimization of Freight Cost and Delivery Time in the HIV Drug Supply Chain Using Machine Learning DOI Creative Commons

Amirkeyvan Ghazvinian,

Bo Feng, Junwen Feng

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(2), P. 91 - 91

Published: Jan. 31, 2025

The purpose of this paper is to examine the optimization HIV drug supply chain, with a dual focus on minimizing freight costs and delivery times. With help dataset containing 10,325 instances chain transactions, key variables, including “Country”, “Vendor INCO Term”, “Shipment Mode”, were examined in order develop predictive model using Artificial Neural Networks (ANN) employing Multi-Layer Perceptron (MLP) architecture. A set ANN models trained forecast “freight cost” “delivery time” based four principal design variables: “Line Item Quantity”, “Pack Price”, “Unit Measure (Per Pack)”, “Weight (Kilograms)”. According performance metrics analysis, these demonstrated accuracy following training. An algorithm, configured an “active-set” was then used minimize combined objective function cost time. Both times significantly reduced as result optimization. This study illustrates potent application machine learning algorithms enhancement efficiency. provides blueprint for reduction improved service critical medication chains methodology outcomes.

Language: Английский

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