A data-driven sustainable scheduling model for dispatch-steerable last-mile delivery systems with negotiable time windows

With the surge in e-commerce, last-mile delivery (LMD) faces challenges in meeting the dynamic arrival of large-scale customer demand. Existing research considers traditional, static, single or two-echelon, and inflexible vehicle routing-based dispatch strategies, yielding inefficiencies while the sustainability impacts remain unclear. This paper proposes a novel tri-objective mathematical model for dispatch-steerability in sustainable LMD systems by integrating insourced and crowdsourced transport modes with heterogeneous capacities. Deliveries originate from main dispatch points (MDPs) or dynamically generated alternative dispatch points (ADPs), adapting to stakeholder, driver, and customer preferences via negotiable timing through temporary rejections or incentives. The model optimizes economic efficiency (minimizing routing, rejection, and negotiation costs), environmental impact (reducing underutilization of transport modes), and social responsibility (balancing driver workload and just-in-time deliveries). A data-driven density-based spatial clustering of applications with noise (DBSCAN) algorithm identifies ADP locations, enhancing the adaptability of the decisions. Scalability is achieved via a rolling horizon strategy, an improved augmented epsilon-constraint method, and multi-objective meta-heuristics, including a genetic algorithm (GA) and simulated annealing (SA) featuring an archive and grid mechanism with a tailored representation scheme. Results show a 200 % increase in Pareto-optimal solutions as demand rises, and that a dispatch steerable LMD system with ADPs offers 150 % more diverse delivery options than MDP-only systems. Pareto surface analysis underscores LMD adaptation trends and the need for multi-criteria decision-making in sustainable LMD systems. A dispatch steerable last-mile delivery system. • Adapt the delivery process to stakeholder, driver, and customer preferences. • Optimize the economic, environmental, and social aspects. • Support the single/two-echelon last-mile delivery via main/alternative dispatches. • Propose data-driven exact and heuristic algorithms for scalable and stable solutions. • Evaluate sustainability trade-offs with Pareto surface analysis.