Advanced Optimization Methods for Transportation and Transshipment Problems under Uncertainty
Keywords:
Novel heuristics, Penalty Cost Method (PCM)Abstract
This paper presents advanced optimization methods for transportation and trans- shipment problems under uncertainty. We extend classical models by integrating mul- tiple factors such as transportation cost, transit time, capacity constraints, and uncer- tainty (modeled via fuzzy and interval data) into a unified framework. Novel heuristics, including the Penalty Cost Method (PCM), are proposed to generate high-quality ini- tial solutions, while multi-objective and time-minimizing transshipment models address dynamic and deadline-constrained scenarios. Rigorous analytical proofs—including convergence, duality, and error estimates—support our methods, which are validated by extensive numerical experiments, sensitivity analyses, and graphical comparisons. The results demonstrate significant improvements in cost efficiency, computational performance, and robustness, making the proposed methodologies highly relevant for modern logistics and supply chain management.
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