This research introduces a cloud-based hybrid simulation model that combines discrete-event simulation (DES) and agent-based modeling (ABM) to enhance Amazon warehouse yard operations, which are crucial for efficient logistics. By leveraging cloud technology for scalability and real-time data integration, the model dynamically simulates sequential processes and the interactions of autonomous agents, such as trucks and yard staff.
Long-haul trailer operations are a critical part of supply chains in many of the world’s developed economies. In the UK, it is estimated that long-haul logistics contributes around 45% of all greenhouse gas emissions from road freight.
One way to reduce greenhouse gas emissions in this sector is by fitting a battery on the trailer. However, long-haul operations are very energy-intensive and electric vehicles would require batteries of considerable size and weight. Applying agent-based modelling and simulation, this paper aims at analyzing if electrification (e.g., electric vehicle fleet, electric road system, etc.) would help reduce greenhouse gas emissions.
Due to numerous uncertainties such as bad weather conditions, frequent changes in the schedules of vessels, breakdowns of equipment, port managers are aiming at providing adaptive and flexible strategic planning of their facilities, especially intermodal terminals (dry ports).
This research shows that the combination of the agent-based modeling with other simulation approaches simplifies the process of designing simulation models and increases their visibility. The developed set of models allows the researchers to compute the balanced values of the parameters. Consequently, it helps achieve effective operation of a seaport – intermodal terminal system. The provided case study on one of the busiest ports in China proves the adequacy and validity of the developed simulation models.
For shuttle trains with a fixed transport capacity which are the dominant operating form in intermodal transport, increasing capacity utilization is of crucial importance due to the low marginal costs of transporting an additional loading unit. Hence, offering rail-based transport services for non-cranable semi-trailers can result in additional earnings for railway companies. However, these earnings have to compensate for the investment costs of the technology. Based on a dynamic investment calculation, this paper presents a simulation model to evaluate the economic profitability of transshipment technologies for non-cranable semi-trailers from the railway company’s perspective. The results depend on the capacity utilization risk faced by the railway company. In particular, if the railway company does not sell all the train capacity to freight forwarders or intermodal operators on a long-term basis, investing in technology for the transshipment of non-cranable semi-trailers can be economically profitable.
Regional freight transportation policy planning is a difficult task, as few policy-planners have adequate tools to aid their understanding of how various policy formulations affect this complex, socio-technical system. In this paper, we develop a proof-of-concept model to simulate the impacts of public policies on freight transportation in a simulated region. We use the techniques of multi-disciplinary system design and optimization to analyze the formulation of regional freight transportation policies and examine the relative effects of policies and exogenous forces on the region in order to provide insight into the policy-planning process. Both single objective and multi-objective analysis is performed to provide policy-planners with a clear understanding of the trade-offs made in policy formulation.
This paper presents preliminary analysis of the Panama Canal Expansion from the viewpoint of salinity in the Gatun Lake and the utilization of neural networks. This analysis utilized simulation modeling and artificial intelligence.
Various actors are involved in hinterland transportation of incoming rail containers along the maritime transport chain. To coordinate each actor’s logistics processes, and therefore to improve utilization of existing transport capacity, the early provision of information, e.g. in form of estimated time of arrival (ETA), is inevitable.
A dual toll pricing is a conceptual policy in which policy maker imposes toll on both hazardous materials (hazmat) vehicles as well as regular vehicles for using populated road segments to mitigate a risk of hazmat transportation. It intends to separate the hazmat traffic flow from the regular traffic flow via controlling the dual toll. In order to design the dual toll pricing policy on a highly realistic road network environment and detailed human behaviors, an extended Belief-Desire-Intention (BDI) framework is employed to mimic human decision behaviors in great detail. The proposed approach is implemented in AnyLogic agent based simulation software with using a traffic data of Albany, NY. Also, search algorithms in OptQuest are used to determine the optimum dual toll pricing policy which results in the minimum risk and travel cost based on the simulation results. The result reveals the effectiveness of the proposed approach in devising a reli-able policy under the realistic road network conditions.
Several challenges of port/terminal and/or ferry company managers pertain to decisions for the justification of investments and concurrent operational tasks of roll-on/roll-off passenger (RoPax) and roll-on/roll-off (RoRo) systems. This paper explores the possible uses of Modeling and Simulation (M&S) techniques as a decision-support aid for a RoPax/RoRo system managers.