Efficient container stacking in terminal yards plays a critical role in improving the sustainability and productivity of port operations, as it directly affects unproductive movements, energy consumption, and cargo flows between vessels and the hinterland. This study addresses a multi-objective container stacking problem that jointly considers operational productivity and energy efficiency of yard equipment. A scattered stacking policy can reduce container reshuffles and improve service levels, but may increase energy consumption due to longer travel distances of yard cranes and trucks. Conversely, consolidating containers within smaller yard areas can reduce energy consumption but may result in higher reshuffle rates. To capture this trade-off, two evolutionary multi-objective optimization algorithms, the Pareto Archived Evolutionary Strategy (PAES) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), are adapted to the container stacking problem through a problem-specific solution representation and tailored initialization procedures, enabling the generation of diverse sets of non-dominated solutions. Extensive computational experiments on 450 instances derived from realistic terminal layouts and operational data are conducted to compare the performance of the two algorithms. The results show that NSGA-II consistently provides higher-quality Pareto front approximations, outperforming PAES by up to 115% on average. In addition, a case study based on 30 real-world instances from the Vigo Container Terminal in Spain demonstrates that the proposed approach can reduce unproductive container movements by approximately 74% while improving energy efficiency by up to 26%, highlighting its potential to support more sustainable container terminal operations.

Companies are under increasing legal and societal pressure to reduce CO₂ emissions from their delivery vehicles, while maximizing profit remains their prime objective. We study a problem where a company sends engineers with vehicles to customer sites to provide services. Customers request the service at their preferred time windows through a website or by calling a call center, and the company needs to allocate these service tasks to time windows and decide on how to schedule these tasks among its vehicles. We propose an approach to this problem that applies low-emission vehicle-scheduling techniques with dynamic pricing to reduce CO₂ emissions and maximize profit. When a customer requests a service with a preferred time window, the company will provide the customer with different service time window options and their corresponding prices. Incentives are included in the prices to influence the customers to reduce CO₂ emissions. Our approach solves the problem in two phases: the first phase solves time-dependent vehicle scheduling models with the objective of minimizing CO₂ emissions, and the second phase solves a dynamic pricing model to maximize profit. Results show that our approach significantly reduces CO₂ emissions and increases profits. A GenAI-based interpretation tool is used to translate the optimization outputs into actionable guidance for planners.

Demand uncertainty plays a crucial role in hub location decisions since hub facilities represent long-term infrastructure investments. This paper studies a hub location–routing problem under demand uncertainty, motivated by the growing involvement of shipping lines in container terminal operations within maritime supply chains. The problem is formulated as a possibilistic mixed-integer linear program with imprecise origin–destination demands and transformed into an auxiliary crisp counterpart using a fuzzy measure-based chance constrained programming approach. To efficiently solve large-scale instances, we develop a Tabu Search algorithm and a decomposition heuristic that iteratively addresses three interrelated subproblems. Computational experiments based on real-world data from major Mediterranean container terminals demonstrate that the proposed methods obtain optimal or near-optimal solutions for instances with up to 56 nodes. The results also show that demand uncertainty can substantially influence hub investment decisions, network configuration, and total costs, highlighting the importance of explicitly incorporating uncertainty into strategic hub-and-spoke network design in maritime transportation systems.