A recurring challenge for transportation companies is the inefficiency of returning (partially) empty vehicles, or backhauling, after delivering orders. To address this issue, companies search on freight exchange platforms for profitable pickup and delivery orders, aiming to reduce the costs associated with empty return trips. The increasing reliance on freight exchange platforms presents both an opportunity and a challenge: while they offer access to profitable loads, effectively selecting the right combination of orders to maximize returns is challenging. This paper addresses this challenge by introducing the Selective Multiple Depot Pickup and Delivery Problem with Multiple Time Windows and Paired Demand (SMDPDPMTWPD). We formulate the SMDPDPMTWP as a Mixed-Integer Linear Program (MILP) to maximize profit and optimize freight selection for return trips. In addition to the main model, three problem extensions are proposed: (i) profit maximization including CO₂ costs, (ii) soft time windows, and (iii) soft time windows including CO₂ costs. Given the complexity of the problem, we develop an Adaptive Large Neighborhood Search (ALNS) metaheuristic to solve large instances within reasonable computing times and compare it with a Simulated Annealing (SA) heuristic. Results show that ALNS outperforms SA and finds the same optimal solutions as the MILP formulation for small instances. Furthermore, ALNS achieves an average improvement of 308.17% over the initial solutions for the profit maximization variant. The model variant with CO₂ costs shows a slight sensitivity of the routing schedules to the CO₂ emissions costs, whereas we observe a significant change when allowing soft time windows. Finally, soft time windows significantly increase the profits earned compared to the hard time windows (179.54% on average), due to the additional flexibility created when late arrivals are possible.

Recent supply chain disruptions and crisis response policies (e.g., the COVID-19 pandemic and the Red Sea crisis) have highlighted the role of container terminals as crucial and scarce resources in the global economy. To tackle these challenges, the industry increasingly aims for advanced operational collaboration among multiple stakeholders, as demonstrated by the ambitions of the recently founded Gemini alliance. Nonetheless, collaborative planning models often disregard the requirements and incentives of stakeholders or simply solve idealized small instances. Motivated by the above, we design novel and effective collaboration mechanisms among terminal operators that share the resources (berths and quay cranes). We first define the collaborative berth allocation problem and propose a mixed integer linear programming (MILP) model to minimize the total cost of all terminals, referred to as the coalitional costs. We adopt the core and the nucleolus concepts from cooperative game theory to allocate the coalitional costs such that stakeholders have stable incentives to collaborate. To obtain solutions for realistic instance sizes, we propose two exact row-generation-based core and nucleolus algorithms that are versatile and can be used for various combinatorial optimization problems. To the best of our knowledge, the proposed row-generation approach for the nucleolus is the first of its kind for combinatorial optimization problems. Extensive experiments demonstrate that the collaborative berth allocation approach achieves up to 28.44% of cost savings, increasing the solution space in disruptive situations, while the proposed core and nucleolus solutions guarantee the collaboration incentives for individual terminals.

Port-related emissions are a growing problem for urban areas often located directly next to maritime container terminals highly frequented by container trucks. Empty truck trips, caused by a lack of coordination among truckers, are responsible for a significant share of these emissions, and collaboration among truckers is seen as the major opportunity to address this issue. Truck appointment systems (TASs) schedule truck activities and enable collaboration for transportation between terminals and client locations. The aim of this work is to introduce a collaborative planning model to be operated within a TAS and to investigate its impact on emission and cost objectives. Starting with a review of requirements for a TAS with collaboration, an optimization model based on the multiple traveling salesman problem with time windows is developed, leveraging collaboration to reduce costs and emissions. The results for a real-world case demonstrate that the developed approach provides appropriately coordinated truck schedules and effectively reduces truck emissions and costs.