Examinando por Autor "Giordani, Stefano"

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Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation
(Hindawi, 2020-07-16) Lujak, Marin; Giordani, Stefano; Omicini, Andrea; Ossowski, Sascha
One of the major challenges in the coordination of large, open, collaborative, and commercial vehicle fleets is dynamic task allocation. Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this paper, we review the literature on scalable and dynamic task allocation focusing on deterministic and dynamic two-dimensional linear assignment problems. We focus on multiagent system representation of open vehicle fleets where dynamically appearing vehicles are represented by software agents that should be allocated to a set of dynamically appearing tasks. We give a comparison and critical analysis of recent research results focusing on centralized, distributed, and decentralized solution approaches. Moreover, we propose mathematical models for dynamic versions of the following assignment problems well known in combinatorial optimization: the assignment problem, bottleneck assignment problem, fair matching problem, dynamic minimum deviation assignment problem, Σk-assignment problem, the semiassignment problem, the assignment problem with side constraints, and the assignment problem while recognizing agent qualification; all while considering the main aspect of open vehicle fleets: random arrival of tasks and vehicles (agents) that may become available after assisting previous tasks or by participating in the fleet at times based on individual interest.
How to Fairly and Efficiently Assign Tasks in Individually Rational Agents’ Coalitions? Models and Fairness Measures
(ComSIS Consortium, 2024) Lujak, Marin; Salvatore, Alessio; Fernández, Alberto; Giordani, Stefano
An individually rational agent will participate in a multi-agent coalition if the participation, given available information and knowledge, brings a pay-off that is at least as high as the one achieved by not participating. Since agents’ performance and skills may vary from task to task, the decisions about individual agent-task assignment will determine the overall performance of the coalition. Maximising the efficiency of the one-on-one assignment of tasks to agents corresponds to the conventional linear sum assignment problem, which considers efficiency as the sum of the costs or benefits of individual agent-task assignments obtained by the coalition as a whole. This approach may be unfair since it does not explicitly consider fairness and, thus, is unsuitable for individually rational agents’ coalitions. In this paper, we propose two new assignment models that balance efficiency and fairness in task assignment and study the utilitarian, egalitarian, and Nash social welfare for task assignment in individually rational agents’ coalitions. Since fairness is a relatively abstract term that can be difficult to quantify, we propose three new fairness measures based on equity and equality and use them to compare the newly proposed models. Through functional examples, we show that a reasonable trade-off between efficiency and fairness in task assignment is possible through the use of the proposed models.