Towards systematic evaluation of multi-agent systems in large scale and dynamic logistics

Abstract

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.