Evaluating city logistics measures using a multi-agent model

From Managing Urban Freight to Smart City Logistics Networks

“…Agent-based simulation models have been proposed to evaluate possible single-tier City Logistics configurations with different stakeholder involvement (e.g., Tamagawa et al, 2010;Wangapisit et al, 2014) The agent methodology provides the framework to define classes of stakeholders and a large number of individuals within each class, potentially each with an individual procedure defining its activities (e.g., vehicle routing algorithms for carrier agents) and behaviour in interacting with the other agents, as well as communication and learning mechanisms. Such methodology appears appropriate to evaluate proposals at a rather aggregate level, where the competition or collaboration among stakeholders may be represented (through, e.g., economic/game theory models).…”

Efficiency and Effectiveness Analysis of Public Transport of Brazilian Cities

“…DEA is the most used tool for comparing the performance of organizations with multiple variables and has been utilized to evaluate various contexts. Some applications of DEA in the area of logistics include Karlaftis (2004); Karlaftis and Tsamboulas (2012); Ozbek et al (2009);Novaes (2001);and Tamagawa, et. al (2010), among others.…”

Consolidation & coordination in urban freight transport

“…Agent-based simulation is not fit to study detailed interactions (Bektaş et al, 2015), yet it is suitable to deduce generic insights into the system performance. A number of agent-based simulation studies in the context of urban logistics have been performed (Taniguchi and Van Der Heijden, 2000;Tamagawa et al, 2010;Van Duin et al, 2012;Wangapisit et al, 2014). They define various stakeholders in the urban areas, and test a number of measures, mainly governmental policies.…”

“…A prerequisite for success is that a gain-sharing mechanism is installed, such that both carriers and shippers profit from the efficiency gains. Tamagawa et al (2010) perform an agent-based simulation study in which they heuristically solve a VRP and iteratively update the actions of five agent types. They compare two learning mechanisms that the agents use to update their decisions, namely Monte Carlo simulation and Q-learning, which is a model-free reinforcement learning technique.…”

“…Carriers are able to cooperate with other carriers, thereby increasing vehicle fill rates. Tamagawa et al (2010) study a 5 × 5 grid (representing a 25km 2 area), with a setup similar to the one presented in Taniguchi et al (2007). They test the effects of truck bans and road pricing, both as isolated measures and in combination with each other.…”