Dynamism was originally defined as the proportion of online versus o✏ine orders in the literature on dynamic logistics. Such a definition however, loses meaning when considering purely dynamic problems where all customer requests arrive dynamically. Existing measures of dynamism are limited to either 1) measuring the proportion of online versus o✏ine orders or 2) measuring urgency, a concept that is orthogonal to dynamism, instead. The present paper defines separate and independent formal definitions of dynamism and urgency applicable to purely dynamic problems. Using these formal definitions, instances of a dynamic logistic problem with varying levels of dynamism and urgency were constructed and several route scheduling algorithms were executed on these problem instances. Contrary to previous findings, the results indicate that dynamism is positively correlated with route quality; urgency, however, is negatively correlated with route quality. The paper contributes the theory that dynamism and urgency are two distinct concepts that deserve to be treated separately.

A common hypothesis in multi-agent systems (MAS) literature is that decentralized MAS are better at coping with dynamic and large scale problems compared to centralized algorithms. Existing work investigates this hypothesis in a limited way, often with no support for further evaluation, slowing down the advance of more general conclusions. Investigating this hypothesis more systematically is time consuming as it requires four main components: 1) formal metrics for the variables of interest, 2) a problem instance generator using these metrics, 3) (de)centralized algorithms and 4) a simulation platform that facilitates the execution of these algorithms. Present paper describes the construction of an instance generator based on previously established formal metrics and simulation platform with support for (de)centralized algorithms. Using our instance generator, a benchmark logistics dataset with varying levels of dynamism and scale is created and we demonstrate how it can be used for systematically evaluating MAS and centralized algorithms in our simulator. This benchmark dataset is essential for enabling the adoption of a more thorough and systematic evaluation methodology, allowing increased insight in the strengths and weaknesses of both the MAS paradigm and operational research methods.

In dynamic dial-a-ride problems a fleet of vehicles need to handle transportation requests within time. We research how to create a decentralized multi-agent system that can solve the dynamic dial-a-ride problem. Normally multi-agent systems are hand designed for each specific application. In this paper we research the applicability of genetic programming to automatically program a multi-agent system that solves dial-a-ride problems. We evaluated the evolved system by running a number of simulations and compared it's performance to a selection hyper-heuristic. The results shows that genetic programming can be a viable alternative to hand constructing multi-agent systems.