Title: A multi-item EOQ model for deterioration items with ramp-type demand and partial backlogging under carbon emission and inflationary environment

Authors: Vipin Kumar; Anupama Sharma; C.B. Gupta

Addresses: Department of Mathematics, B.K. Birla Institute of Engineering and Technology, Pilani (Raj.) 333031, India ' Department of Mathematics, B.K. Birla Institute of Engineering and Technology, Pilani (Raj.) 333031, India ' Department of Mathematics, The North Cap University, Gurugram, (Haryana) 122001, India

Abstract: Carbon emissions contribute most in the global warming. To minimise the adverse effect of global warming on society, countries are focusing to control CO₂ and industries are investing in green products. All of this inclined the regulatory authorities and customers towards the environment. Thus, present study considers a novel multi-item inventory model for green products. Here, demand rate is considered as a function of initial inventory level, time, and carbon emission. This study also incorporates the carbon emissions associated with different attributes of inventory management such as procurement, purchasing, and holding inventory. Shortages are allowed and depend on the waiting time. The retailer adopts the supplier's trade credit strategy and study is carried out the inflationary environment. This model aims to optimise the total inventory cost subject to the upper cap of carbon emission. Observation based on analysis indicates some empirical observation. The sensitivity analysis concerning critical parameters showed that the optimal policy for green products is highly sensitive to the costs of shortages, and backlogging. Results indicate that incorporation of carbon issue while modelling for the green products is worthwhile for environment. Around 38% changed is observed in the inventory cost due to the change in scale parameter of demand.

Keywords: green products; trade credit; partial backlogging; multivariate ramp-type demand; deterioration.

DOI: 10.1504/IJOR.2024.142255

International Journal of Operational Research, 2024 Vol.51 No.2, pp.260 - 296

Received: 24 Aug 2021
Accepted: 20 Nov 2021
Published online: 16 Oct 2024 *

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