Greening concept in inventory system for deteriorating items with preservation investment and price and stock dependent demand via marine predators algorithm

This study develops a comprehensive inventory model for deteriorating items by incorporating preservation technology and addressing sustainability-driven customer behavior. Demand is modeled as a nonlinear function influenced by three key factors: selling price, green level (reflecting the environmental friendliness of the product and its production), and available inventory level. Recognizing rising environmental consciousness, the green level directly shapes consumer demand, while preservation investment reduces deterioration and extends the shelf life of perishable goods. The objective of the model is to maximize the total profit by jointly optimizing five decision variables: selling price, green level investment, preservation effort, cycle length, and replenishment quantity. The resulting objective function is highly nonlinear and complex. To solve it efficiently, this study employs the Marine Predators Algorithm (MPA)-a newly developed metaheuristic algorithm well-suited for continuous, nonlinear optimization. Model authenticity is established through a combination of sensitivity analysis, convergence behavior examination, and validation against benchmark test problems from existing literature. The robustness of the solution method is further demonstrated by comparing the MPA's performance with other optimization techniques in terms of solution quality and computational efficiency. Although the study is theoretical in nature, data assumptions are grounded in real-world parameter ranges drawn from validated case studies and academic sources. Parameters such as deterioration rates, green investment cost coefficients, and preservation effectiveness are selected to reflect practical supply chain conditions. This ensures the credibility of the model output and applicability in realistic scenarios. The study offers critical managerial insights, including how to balance sustainability initiatives, pricing decisions, and preservation investments for optimal inventory control. These insights are particularly valuable for supply chains dealing with environmentally sensitive, perishable products, helping businesses enhance operational efficiency while supporting green objectives.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-08-04
DOI
https://doi.org/10.1038/s41598-025-13565-4
Primary Topic
Supply Chain and Inventory Management
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Greening concept in inventory system for deteriorating items with preservation investment and price and stock dependent demand via marine predators algorithm

Jeonghwan Gwak, Hachen Ali, Gobinda Chandra Panda, Ali Akbar Shaikh et al.
Scientific Reports
Supply Chain and Inventory Management
article

Greening concept in inventory system for deteriorating items with preservation investment and price and stock dependent demand via marine predators algorithm

Jeonghwan Gwak, Hachen Ali, Gobinda Chandra Panda, Ali Akbar Shaikh, Adel Fahad Alrasheedi
article en

Abstract

This study develops a comprehensive inventory model for deteriorating items by incorporating preservation technology and addressing sustainability-driven customer behavior. Demand is modeled as a nonlinear function influenced by three key factors: selling price, green level (reflecting the environmental friendliness of the product and its production), and available inventory level. Recognizing rising environmental consciousness, the green level directly shapes consumer demand, while preservation investment reduces deterioration and extends the shelf life of perishable goods. The objective of the model is to maximize the total profit by jointly optimizing five decision variables: selling price, green level investment, preservation effort, cycle length, and replenishment quantity. The resulting objective function is highly nonlinear and complex. To solve it efficiently, this study employs the Marine Predators Algorithm (MPA)-a newly developed metaheuristic algorithm well-suited for continuous, nonlinear optimization. Model authenticity is established through a combination of sensitivity analysis, convergence behavior examination, and validation against benchmark test problems from existing literature. The robustness of the solution method is further demonstrated by comparing the MPA's performance with other optimization techniques in terms of solution quality and computational efficiency. Although the study is theoretical in nature, data assumptions are grounded in real-world parameter ranges drawn from validated case studies and academic sources. Parameters such as deterioration rates, green investment cost coefficients, and preservation effectiveness are selected to reflect practical supply chain conditions. This ensures the credibility of the model output and applicability in realistic scenarios. The study offers critical managerial insights, including how to balance sustainability initiatives, pricing decisions, and preservation investments for optimal inventory control. These insights are particularly valuable for supply chains dealing with environmentally sensitive, perishable products, helping businesses enhance operational efficiency while supporting green objectives.

Scientific ReportsVol. 15(1)
Korea National University of Transportation (KR), University of Burdwan (IN), King Saud University (SA), Biju Patnaik University of Technology (IN)
King Saud University
Life below water
Openalex Percentile: Top 67%
Supply Chain and Inventory Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.