Title: Perishable inventory management using GA-ANN and ICA-ANN

Authors: Saeideh Farajzadeh Bardeji; Amir Mohammad Fakoor Saghih; Alireza Pooya; Seyed-Hadi Mirghaderi

Addresses: Department of Management, Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad (FUM), Mashhad, Iran ' Department of Management, Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad (FUM), Mashhad, Iran ' Department of Management, Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad (FUM), Mashhad, Iran ' Department of Management, Shiraz University, Shiraz, Iran

Abstract: We have developed a multi-objective multi-product inventory management model for perishable products, focusing on the inventory management of veterinary drugs. This model minimises holding, shortage, and expired costs and also demand forecast error simultaneously. The number of expired and shortage drugs can be calculated for each period using this model. Data from three types of veterinary drugs have been collected from a distribution centre (DC). In this research, multi-layer perceptron (MLP) neural network is used to forecast the demand and genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used to solve and find satisfactory solutions. In this research, artificial neural network (ANN) is combined with the two above-mentioned algorithms to solve the problem. The results show that the proposed model can find high-quality solutions because it reduces inventory costs and forecast errors in the DC. Finally, the results of combining ANN with each of the algorithms were compared and it was concluded that the combination of ANN and ICA produced better solutions.

Keywords: inventory management; genetic algorithm; GA; imperialist competitive algorithm; ICA; artificial neural network; ANN; multi-layer perceptron; MLP; veterinary drug; multi-objective multi-product model.

DOI: 10.1504/IJPM.2020.10022121

International Journal of Procurement Management, 2020 Vol.13 No.3, pp.347 - 382

Received: 11 Feb 2019
Accepted: 17 Apr 2019

Published online: 29 May 2020 *

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