Open AccessDesign and development of a model and optimal planning for supply chain responsibility towards the environment
Author(s) -
Shahram Mokhlesabadi,
Mohammad Reza Kabaranzad Ghadim,
Hasan Ali Aghajani Kasegari,
Mohammad Mahdi Movahedi
Publication year - 2021
Publication title -
nexo
Language(s) - English
Resource type - Journals
eISSN - 1995-9516
pISSN - 1818-6742
DOI - 10.5377/nexo.v34i01.11295
Subject(s) - supply chain , reverse logistics , greenhouse gas , economic order quantity , production (economics) , order (exchange) , product (mathematics) , environmental economics , business , operations research , production planning , computer science , operations management , economics , marketing , engineering , microeconomics , ecology , geometry , mathematics , finance , biology
The responsible management of product return flows in production and inventory environments is a rapidly increasing requirement for companies. This can be attributed to economic, environmental and/or regulatory motivations. Mathematical modeling of such systems has assisted decision-making processes and provided a better understanding of the behavior of such production and inventory environments. This paper reviews the literature on the modeling of reverse logistics inventory systems based on the economic order/production quantity (EOQ/EPQ) and the joint economic lot size (JELS) settings to systematically analyze the mathematics involved in capturing the main characteristics of related processes. The literature is surveyed and classified according to the specific issues faced and modeling assumptions. Special attention is given to environmental issues. There are indications of the need for reverse logistics models' mathematics to follow current trends in ‘greening’ inventory and supply-chain models. The modeling of waste disposal, greenhouse-gas emissions, and energy consumption during production is considered as the most pressing priority for the future of reverse logistics models. An illustrative example for modeling reverse logistics inventory models with environmental implications is presented.