Evaluation and Selection of Vertical Conveyor Systems for Material Handling in Transport and Logistics Using an Adaptive Balanced MCDM Framework

The selection of vertical conveyor systems is a complex decision problem in transport, warehousing, and logistics infrastructure, requiring simultaneous consideration of capacity, energy demand, space utilization, and operational flexibility. This study develops the Self-Monitoring Adaptive Ranking Technique Based on Balanced Additive Weighting (SMART-BAW), an adaptive multi-criteria decision-making (MCDM) framework designed to support the evaluation and ranking of vertical conveyor alternatives. The proposed method introduces a balance-control mechanism that adjusts the influence of performance deviations according to the variability of the decision matrix, reducing the dominance of extreme criterion values and favoring alternatives with more balanced overall performance. Objective criterion weights are derived using the entropy method. The applicability of the framework is demonstrated through a case study involving different vertical conveyor technologies used in logistics and material handling operations. The results highlight trade-offs between continuous and intermittent conveying solutions, particularly regarding throughput, energy efficiency, footprint, and functional adaptability. The ranking results are validated through comparison with TOPSIS, VIKOR, EDAS, MARCOS, and RAWEC, while sensitivity analysis confirms ranking stability under changes in the adaptive balance coefficient and criterion-weight scenarios. The proposed framework provides a transparent and computationally efficient decision-support tool for sustainable logistics infrastructure planning.