Optimizing Last-Mile Delivery: A Multi-Criteria Approach with Automated Smart Lockers, Capillary Distribution and Crowdshipping

Abstract

Background: This publication presents a review, multiple criteria optimization models, and a practical example pertaining to the integration of automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. This publication addresses challenges in logistics and transportation, aiming to enhance efficiency, reduce costs and improve customer satisfaction. This study integrates automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. Methods: A review of the existing literature synthesizes key concepts, such as facility location problems, vehicle routing problems and the mathematical programming approach, to optimize supply chain operations. Conceptual optimization models are formulated to solve the complex decision-making process involved in last-mile delivery, considering multiple objectives, including cost minimization, delivery time optimization, service level minimization, capacity optimization, vehicle minimization and resource utilization. Results: The multiple criteria approaches combine the vehicle routing problem and facility location problem, demonstrating the practical applicability of the proposed methodology in a real-world case study within a logistics company. Conclusions: The execution of multi-criteria models optimizes automated smart locker deployment, capillary distribution design, crowdshipping and last-mile delivery strategies, showcasing its effectiveness in the logistics sector.

Performance Evaluation of Railway Infrastructure Managers: A Novel Hybrid Fuzzy MCDM Model

“…The issue discussed in this research could potentially be resolved through different methods. These may involve using simulations of diverse scenarios (e.g., [35]), optimization models offering mathematical frameworks to systematically identify the best possible solutions given specific constraints and objectives (e.g., [36]), metaheuristic algorithms to effectively explore solution spaces (e.g., [37]), and artificial intelligence methods like neural networks to analyze intricate datasets (e.g., [38]). Each method presents distinct advantages, from offering insights into potential results to utilizing large data sets for predictive analysis.…”