Title: Multi-objective optimisation for stochastic inventory model using grey wolf optimiser

Authors: Nguyen Duy Tan; Hwan-Seong Kim; Le Ngoc Bao Long; Duy Anh Nguyen; Sam-Sang You

Addresses: Department of Logistics, Korea Maritime and Ocean University, Busan, South Korea ' Department of Logistics, Korea Maritime and Ocean University, Busan, South Korea ' Department of Logistics, Korea Maritime and Ocean University, Busan, South Korea ' Department of Mechatronics Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam ' Department of Mechanical Engineering, Northeast-Asia Shipping and Port Logistics Research Center, Korea Maritime and Ocean University, Busan, South Korea

Abstract: This paper presents a multi-item and multi-period inventory management model to optimise inventory costs and storage space under budget constraints. To minimise the total inventory costs and the storage space, the framework of an integer nonlinear programming model is presented based on random demands. In addition, a multi-objective grey wolf optimisation (MOGWO) approach is employed to realise the optimal inventory management system. The effectiveness of solutions from MOGWO is also verified using numerical examples based on four different scenarios. Unlike previous approaches in inventory management that only consider a single-objective optimisation problem, this approach aims to optimise inventory costs and storage space utilisation simultaneously. The supply chain performance can be significantly enhanced through visibility. With excellent decision-making schemes powered by optimisation algorithms, inventory management software can react to an ever-fluctuating production flow and anticipate the need for changes in a firm's policies.

Keywords: multi-item multi-period inventory; stochastic demand; grey wolf optimiser; GWO; multi-objective optimisation.

DOI: 10.1504/IJAOM.2023.135803

International Journal of Advanced Operations Management, 2023 Vol.15 No.3, pp.270 - 292

Received: 16 Dec 2022
Accepted: 16 Oct 2023
Published online: 05 Jan 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article