Abstract
The viability of biomass to be used as a consumer product relies heavily on the cost of a Biomass supply network (BSN) that links biomass producers with biorefineries and, finally, with end customers. The current study aims to establish a cost optimization model to minimize the financial burden of BSN. A MILP model has been established and implemented to reduce the costs of a BSN. A comparatively lesser-used stochastic technique, Ant Colony Optimization (ACO), has been used in the present paper to minimize the cost of BSN. Although the ACO technique has succeeded in other settings, it is seldom tested in the context of BSN. The results from the ACO approach have been compared with another popular stochastic optimization technique called the Non-sorting Genetic Algorithm (NSGA-II). According to empirical research, the ACO approach is the most cost-effective optimization technique to lower BSN-related costs. The management may use the blueprint of the optimization model and techniques to develop cost-cutting measures for BSN.
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Arya, A.K., Gautam, V., Kumar, A. (2024). Comparative Studies of Stochastic Techniques to Minimize the Cost of Biomass Supply Networks. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2027. Springer, Cham. https://doi.org/10.1007/978-3-031-53085-2_30
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