Examinando por Autor "Lujak, Marin"

Mostrando 1 - 10 de 10

A Proposal for Situation-Aware Evacuation Guidance Based on Semantic Technologies
(Springer, Cham, 2017-06-23) Billhardt, Holger; Dunkel, Juergen; Fernández, Alberto; Lujak, Marin; Hermoso, Ramón; Ossowski, Sascha
Smart Cities require reliable means for managing installations that offer essential services to the citizens. In this paper we focus on the problem of evacuation of smart buildings in case of emergencies. In particular, we present a proposal for an evacuation guidance system that provides individualized evacuation support to people in case of emergencies. The system uses sensor technologies and Complex Event Processing to obtain information about the current situation of a building in each moment. Using semantic Web technologies, this information is merged with static knowledge (special user characteristics, building topology, evacuation knowledge) in order to determine (and dynamically update) the most appropriate individualized evacuation routes for each user.
Agreement Technologies for Coordination in Smart Cities
(MDPI, 2018-05-18) Billhardt, Holger; Fernández, Alberto; Lujak, Marin; Ossowski, Sascha
Many challenges in today’s society can be tackled by distributed open systems. This is particularly true for domains that are commonly perceived under the umbrella of smart cities, such as intelligent transportation, smart energy grids, or participative governance. When designing computer applications for these domains, it is necessary to account for the fact that the elements of such systems, often called software agents, are usually made by different designers and act on behalf of particular stakeholders. Furthermore, it is unknown at design time when such agents will enter or leave the system, and what interests new agents will represent. To instil coordination in such systems is particularly demanding, as usually only part of them can be directly controlled at runtime. Agreement technologies refer to a sandbox of tools and mechanisms for the development of such open multiagent systems, which are based on the notion of agreement. In this paper, we argue that agreement technologies are a suitable means for achieving coordination in smart city domains, and back our claim through examples of several real-world applications.
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.
Distributed Multi-Robot Coordination Combining Semantics and Real-Time Scheduling
(IEEE Explore, 2015-06) Lujak, Marin; Fernández, Alberto
In this paper, we study a distributed intelligent multi-robot system (MRS) in assembly setting where robots have partially overlapping capabilities. We treat the problem of the system’s real-time self-(re)configurability and self-optimization. In this light, we propose an optimized distributed coordination system ORCAS that integrates the MRS configuration based on semantic descriptions and process scheduling. In more detail, initially, robots and the corresponding devices get matched semantically to respond to the assembly requests. Then, the best configurations are chosen by dynamically minimizing total assembly costs and off-line times. During the execution, the performance is controlled for contingencies in case of which the robots, if necessary, self-reconfigure or reschedule the tasks.
Dynamic Coordination in Fleet Management Systems: Toward Smart Cyber Fleets
(IEEE Intelligent Systems, 2014) Billhardt, Holger; Fernández, Alberto; Lemus, Lissette; Lujak, Marin; Osman, Nardine; Ossowski, Sascha; Sierra, Carles
Fleet Management Systems are commonly used to coordinate mobility and delivery services in a broad variety of domains. However, their traditional top-down control architecture becomes a bottleneck in open and dynamic environments, where scalability, proactiveness, and autonomy are becoming key factors for their success. In this paper, we first present an abstract event-based architecture for Fleet Management Systems that supports tailoring dynamic control regimes for coordinating fleet vehicles, and illustrate it for the case of medical emergency management. Then, we go one step ahead in the transition towards automatic or driverless fleets, by conceiving Fleet Management Systems in terms of Cyber-Physical Systems, and putting forward the notion of Cyber Fleets. We illustrate the idea in the field of electro mobility, where we expect drivers of smart e-motorbikes (Cyber Vehicles), equipped with an intelligent communication device (Cyber Helmet), to coordinate in a context-aware manner as part of a decentralised Fleet Management System.
Dynamic Coordination of Ambulances for Emergency Medical Assistance Services
(Elsevier, 2014) Billhardt, Holger; Lujak, Marin; Sánchez-Brunete, Vicente; Fernández, Alberto; Ossowski, Sascha
The main objective of emergency medical assistance (EMA) services is to attend patients with sudden dis- eases at any possible location within an area of influence. This usually consists in providing ``in situ¿¿ assistance and, if necessary, the transport of the patient to a medical center. The potential of such systems to reduce mortality is directly related to the travel times of ambulances to emergency patients. An effi- cient coordination of the ambulance fleet of an EMA service is crucial for reducing the average travel times. In this paper we propose mechanisms that dynamically improve the allocation of ambulances to patients as well as the redeployment of available ambulances in the region under consideration. We test these mechanisms in different experiments using historical data from the EMA service of the Auton- omous Region of Madrid in Spain: SUMMA112. The results empirically confirm that our proposal reduces the average response times of EMA services significantly.
Dynamic, fair, and efficient routing for cooperative autonomous vehicle fleets
(Elsevier, 2024-10-01) López Sánchez, Aitor; Lujak, Marin; Semet, Frédérik; Billhardt, Holger
This paper addresses challenges in agricultural cooperative autonomous fleet routing through the proposition, modeling, and resolution of the Dynamic Vehicle Routing Problem with Fair Profits and Time Windows (DVRP-FPTW). The aim is to dynamically optimize routes for a vehicle fleet serving tasks within assigned time windows, emphasizing fair and efficient solutions. Our DVRP-FPTW accommodates unforeseen events like task modifications or vehicle breakdowns, ensuring adherence to task demand, vehicle capacities, and autonomies. The proposed model incorporates mandatory and optional tasks, including optional ones in operational vehicle routes if not compromising the vehicles’ profits. Including asynchronous and distributed column generation heuristics, the proposed Multi-Agent-based architecture DIMASA for the DVRP-FPTW dynamically adapts to unforeseen events. Systematic Egalitarian social welfare optimization is used to iteratively maximize the profit of the least profitable vehicle, prioritizing fairness across the fleet in light of unforeseen events. This improves upon existing dynamic and multi-period VRP models that rely on prior knowledge of demand changes. Our approach allows vehicle agents to maintain privacy while sharing minimal local data with a fleet coordinator agent. We propose publicly available benchmark instances for both static and dynamic VRP-FPTW. Simulation results demonstrate the effectiveness of our DVRP-FPTW model and our multi-agent system solution approach in coordinating large, dynamically evolving cooperative autonomous fleets fairly and efficiently in close to real-time
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.
SBEO: Smart Building Evacuation Ontology
(ComSIS Consortium, 2023) Khalid, Qasim; Fernández, Alberto; Lujak, Marin; Doniec, Arnaud
Semantically rich depiction of the concepts for context-aware indoor routing brings appealing benefits for the safety of occupants of smart spaces in emergency evacuation. In this paper, we propose Smart Building Evacuation Ontol- ogy (SBEO3), a reusable ontology for indoor spaces, based on three different data models: user, building, and context. We provide a common representation of indoor routing and navigation, describe users’ characteristics and preferences, grouping of individuals and their role in a specific context, hazards, and emergency evacuation. Among other characteristics, we consider abilities of individuals, safety and accessibility of spaces related to each person, intensity, impact, and severity of an emergency event or activity. SBEO is flexible and compatible with other ontologies of its domain, including SEAS, SSN/SOSA, SEMA4A, and empathi. We evaluate SBEO based on several metrics demonstrating that it addresses the information needs for the context-aware route recommendation system for emergency evacuation in indoor spaces. In the end, a simulation-based application example exploits SBEO using Context-Aware Emergency Evacuation Software (CAREE).
Spillover Algorithm: A decentralised coordination approach for multi-robot production planning in open shared factories
(Elsevier, 2021-08) Lujak, Marin; Fernández, Alberto; Onaindia, Eva
Open and shared manufacturing factories typically dispose of a limited number of industrial robots and/or other production resources that should be properly allocated to tasks in time for an effective and efficient system performance. In particular, we deal with the dynamic capacitated production planning problem with sequence independent setup costs where quantities of products to manufacture need to be determined at consecutive periods within a given time horizon and products can be anticipated or back-ordered related to the demand period. We consider a decentralised multi-agent variant of this problem in an open factory setting with multiple owners of robots as well as different owners of the items to be produced, both considered self-interested and individually rational. Existing solution approaches to the classic constrained lot-sizing problem are centralised exact methods that require sharing of global knowledge of all the participants’ private and sensitive information and are not applicable in the described multi-agent context. Therefore, we propose a computationally efficient decentralised approach based on the spillover effect that solves this NP-hard problem by distributing decisions in an intrinsically decentralised multi-agent system environment while protecting private and sensitive information. To the best of our knowledge, this is the first decentralised algorithm for the solution of the studied problem in intrinsically decentralised environments where production resources and/or products are owned by multiple stakeholders with possibly conflicting objectives. To show its efficiency, the performance of the Spillover Algorithm is benchmarked against state-of-the-art commercial solver CPLEX 12.8.