Vishwas Deep Joshi¹, Priya Agarwal¹, Lenka Čepová², Huda Alsaud³, Ajay Kumar^4,*, B. Swarna⁵, Ashish Kumar⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 969-1011, 2025, DOI:10.32604/cmes.2025.063996 - 31 July 2025

Abstract In a world where supply chains are increasingly complex and unpredictable, finding the optimal way to move goods through transshipment networks is more important and challenging than ever. In addition to addressing the complexity of transportation costs and demand, this study presents a novel method that offers flexible routing alternatives to manage these complexities. When real-world variables such as fluctuating costs, variable capacity, and unpredictable demand are considered, traditional transshipment models often prove inadequate. To overcome these challenges, we propose an innovative fully fuzzy-based framework using LR flat fuzzy numbers. This framework allows for more… More >

Gul Freen¹, Sajida Kousar¹, Nasreen Kausar², Dragan Pamucar³, Georgia Irina Oros^4,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4861-4879, 2023, DOI:10.32604/cmc.2023.032985 - 28 December 2022

Abstract The petroleum industry has a complex, inflexible and challenging supply chain (SC) that impacts both the national economy as well as people’s daily lives with a range of services, including transportation, heating, electricity, lubricants, as well as chemicals and petrochemicals. In the petroleum industry, supply chain management presents several challenges, especially in the logistics sector, that are not found in other industries. In addition, logistical challenges contribute significantly to the cost of oil. Uncertainty regarding customer demand and supply significantly affects SC networks. Hence, SC flexibility can be maintained by addressing uncertainty. On the other… More >

Sumbal Khalil¹, Sajida Kousar¹, Nasreen Kausar², Muhammad Imran³, Georgia Irina Oros^4,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6207-6224, 2022, DOI:10.32604/cmc.2022.030547 - 28 July 2022

Abstract Bipolar Interval-valued neutrosophic set is another generalization of fuzzy set, neutrosophic set, bipolar fuzzy set and bipolar neutrosophic set and thus when applied to the optimization problem handles uncertain data more efficiently and flexibly. Current work is an effort to design a flexible optimization model in the backdrop of interval-valued bipolar neutrosophic sets. Bipolar interval-valued neutrosophic membership grades are picked so that they indicate the restriction of the plausible infringement of the inequalities given in the problem. To prove the adequacy and effectiveness of the method a unified system of sustainable medical healthcare supply chain More >

Şahin Emrah Amrahov¹, Iman N.Askerzade¹

CMES-Computer Modeling in Engineering & Sciences, Vol.70, No.1, pp. 1-10, 2010, DOI:10.3970/cmes.2010.070.001

Abstract In this paper we define multivariable fuzzy functions (MFF) and corresponding multivariable crisp functions (MCF). Then we give a definition for the maximum value of MFF, which in some cases coincides with the maximum value in Pareto sense. We introduce generalized maximizing and minimizing sets in order to determine the maximum values of MFF. By equating membership functions of a given fuzzy domain set and the corresponding maximizing set, we obtain a curve of equal possibilities. Then we use the method of Lagrange multipliers to solve the resulting nonlinear optimization problem when the membership functions More >