میلاد سالکی

Amid growing environmental concerns and stringent regulatory requirements, Green Supply Chain Management (GSCM) has emerged as an essential strategic approach for organizations. This study introduces an advanced optimization model that incorporates carbon emission costs in both transportation and product storage, utilizing heterogeneous vehicle fleets. The proposed model integrates inventory and transportation decisions while considering both environmental and economic impacts, with the objective of optimizing energy consumption and resource allocation across the supply chain. This research formulates and evaluates a multi-product, multi-period optimization framework within the context of GSCM, specifically aimed at minimizing carbon emissions. Initially developed using Mixed-Integer Non-Linear Programming (MINLP), the model is subsequently linearized into a Mixed-Integer Linear Programming (MILP) formulation. It includes various cost factors, such as carbon emission costs in transportation and product storage. The findings demonstrate that increasing the carbon tax rate significantly reduces carbon emissions but concurrently elevates overall supply chain costs. While extending the planning horizon increases overall and transportation costs, inventory holding costs and carbon emissions from logistics and storage remain relatively stable. By presenting a comprehensive optimization framework, this study provides supply chain managers with a robust decision-making tool to mitigate carbon emissions and promote both environmental and economic sustainability.

Generally, selecting an investment portfolio with appropriate returns that is also secure and auditable has been one of the issues raised in recent decades. For this purpose, the present research proposes an appropriate approach using ideal and anti-ideal values, ideal values, as well as maximum deviations of each objective, considering the sample in the examined market, fuzzy goals, interval fuzzy values for each asset, and their combination with satisfaction functions, fuzzy ideal planning, and weighting objectives using expert decision-makers' opinions, as well as the development of fuzzy basic weighting method. It seeks to select an investment portfolio in the digital currency market.

In this research, a new approach to selecting an investment portfolio based on uncertain data and multi-objective uncertain planning is proposed, and ultimately, the proposed approach is implemented in the digital currency market for portfolio selection.

The results of the present study show that the proposed model of investment portfolio compared to the base model not only led to higher returns but also had higher audibility and better risk control. In other words, the proposed model outperformed the base model in all the objectives under study.

Originality/Value:

 As distinguishing features of the proposed model of this research, one can mention: 1) constructing and using fuzzy distribution functions and calculating ideal values and expected ranges for all desired objectives considering the conditions of the examined market research using simple mathematical modeling, 2) utilizing the experience of financial market experts in planning model for selecting suitable investment portfolios in emerging financial markets, 3) presenting an approach to calculating portfolio risk in conditions of information scarcity in the problem environment using fuzzy theory, 4) development of the fuzzy benchmark-criterion method for weighting the objectives under study in the problem considering the expertise of financial market experts, and 5) simple modeling, considering interval fuzzy values in the model, and being usable for all individuals with different levels of investment knowledge.