The paper discusses how process mining can be used to gain insights and automatically regulate processes in real time. It highlights the need for thorough testing to avoid side effects and proposes using discrete-event simulation in production and logistics to mitigate risks.
Creating discrete-event simulation models for adaptable material flow systems is challenging due to the need for various structural variants. This paper unveils an innovative method to automate this process, illustrated by a case study, which makes it easier and more efficient.
The research paper on e-commerce addresses urban logistics issues worsened by the COVID-19 pandemic. The scholars used system dynamics simulation modeling with the ε-constraint method to design a parcel locker delivery network and forecast demand. The model suggested the lockers’ locations for improved delivery results and helped to develop a strategy for minimizing environmental impact.
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.
Cross-docking is a warehousing method that allows goods to move quickly from inbound suppliers directly to outbound customers, minimizing storage time. This study focuses on developing a real-time multi-agent truck scheduling model to optimize the process of cross-docking in warehouses, aiming for quick and efficient synchronization of incoming and outgoing freight.
Maritime traffic in winter in the Baltic Sea (particularly the northern part) is challenged by heavy ice formation. This work presented an integration of ice characteristics, operational-level details of ships, and system-level details such as traffic flows and icebreaker scheduling through a simulation framework.
The constant rise of e-commerce coupled with extremely fast deliveries is a significant contributor to saturate city centers’ mobility. To address this issue, the development of a convenient Automated Parcel Lockers (APLs) network improves last-mile distribution by reducing the number of transportation vehicles, the distances driven, and the delivery stops. An agent-based model was implemented in the current paper to forecast parcel demand placed on APLs based on socio-economic factors.
The bullwhip effect, a phenomenon of progressively larger distortion of demands across a supply chain, can cause chaos and disorder with amplified supply and demand misalignment. An agent-based simulation model was developed to evaluate how risk pooling and information sharing between distinct entities in a supply chain can reduce the bullwhip effects. In agent-based paradigm different components of a system were described as agents which interact with each other in an environment.
This study investigates a different source of uncertainty, which is the waiting time for the next vessel that is scheduled on a specific route, connecting two international ports. The aim of this research was to determine the booking size for vessels in oversea delivery to minimize transportation costs. The simulation model and all the respective processes included in the oversea supply chain were developed in AnyLogic with a discrete-event paradigm.
This paper considers a loading facility that uses Truck Appointment System (TAS) for slot management and faces incoming truck arrival uncertainty due to traffic congestion. The researchers developed an integer mathematical model to represent the adaptive behavior that determines the optimal reschedules by minimizing the average truck waiting time. The model was aimed to emulate the adaptive system. It was created in AnyLogic simulation software based on the conceptual model.