This study aimed to evaluate the integration of connected and autonomous vehicles (CAVs) into existing transportation networks, comprising highways and urban roads. To quantify their impact, agent-based simulation models were developed and validated.
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.
This paper presented a concept for simulation-based optimization of the system design of modular production systems using a classical Genetic Algorithm (GA) and the NSGA-II algorithm, which supported a good activity assignment to the individual manufacturing cells.
An animated, data-driven and data-generated simulation model was developed to support the development of both the design and configuration methodology and operational control strategy of Shuttle-based Storage and Retrieval Systems.
A novel data trading approach was presented in this paper – one where trading was controlled by seller preferences. The approach followed the principles of seller’s rights protection and control over the data sharing available in a community of users. A hybrid approach was shown to combine market and technology simulations and enable system developers to test robust future scenarios.
Blood supply chains are an essential element of healthcare systems. Simulation provides the means to test out digital innovations for blood supply chains regarding their operational efficiency and expected costs.
Despite numerous attempts to quantify the impacts of factors influencing productivity in the construction industry, such factors are still perceived as static and independent, resulting in unrealistic productivity estimates. Two generic agent-based models were built to simulate the outcomes of a project through varying levels of detail, each investigating a certain set of impacts. Findings proved the accuracy of the proposed comprehensive approach in estimating durations compared to planned durations and to those obtained from the traditional approach.
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 article presents a simulation model for feed supply networks consisting of a number of customers, retailers, and manufacturers which are agents and placed on Geographic Information System (GIS) map. The simulation model proposed a fuzzy-based decision strategy for customers to decide when to order specific products. The AnyLogic model described a possible decision strategy for retailers to optimize their transport routes by selecting viable manufacturers.