A multi-agent approach for integrated emergency vehicle dispatching and covering problem

The most important decisions that should be made by emergency vehicle managers are related to the allocation and the covering problems. The allocation (or dispatching) problem consists of deciding which vehicle must be assigned to assist an emergency in the best times. The covering problem aims at keeping the region under surveillance well-covered by relocating available vehicles. As components are geographically distributed, decentralized solution approaches may present several advantages. This paper develops a decentralized distributed solution approach based on multi-agent systems (MAS) to manage the emergency vehicles. The proposed system integrates the dispatching of vehicles to calls with zone coverage issues. This integration means that allocation and covering decisions are considered jointly. The idea of MAS has been applied in many others real-world contexts, and has been proven to provide more flexibility, reliability, adaptability and reconfigurability. To our knowledge, there is no existing work that uses MAS for real-time emergency vehicle allocation problem while accounting for the coverage requirements for future demands. We propose a multi-agent architecture that fit the real emergency systems, and that aims at keeping good performance compared to the centralized solution. The objective is to coordinate agents to reach good quality solutions in a distributed way. For this purpose two approaches are examined. The first one is used to show the impact of distributing data and control on the solution quality, since the dispatching decisions are based only on local evaluations of the fitness. The second approach is based on implicit agents' coordination using a more refined and efficient auction mechanism. The performance of each approach is compared to the centralized solution obtained by solving the proposed model with ILOG CPLEX solver. The obtained results show the importance of the coordination method to keep a good quality of service while distributing data and decision making, and prove the performance of the second approach.     

Highly-scalable traffic management of autonomous industrial transportation systems

In this paper, we present a novel method for highly-scalable coordination of free-ranging automated guided vehicles in industrial logistics and manufacturing scenarios. The primary aim of this method is to enhance the current industrial state-of-the-art multi-vehicle transportation systems, which, despite their long presence on the factory floor and significant advances over the last decades, still rely on a centralized controller and predetermined network of paths. In order to eliminate the major drawbacks of such systems, including poor scalability, low flexibility, and the presence of a single point of failure, in the proposed control approach vehicles autonomously execute their assigned pick-up and delivery operations by running a fully decentralized control algorithm. The algorithm integrates path planning and motion coordination capabilities and relies on a two-layer control architecture with topological workspace representation on the top layer and state-lattice representation on the bottom layer. Each vehicle plans its own shortest feasible path toward the assigned goal location and resolves conflict situations with other vehicles as they arise along the way. The motion coordination strategy relies on the private-zone mechanism ensuring reliable collision avoidance, and local negotiations within the limited communication radius ensuring high scalability as the number of vehicles in the fleet increases. We present experimental validation results obtained on a system comprising six Pioneer 3DX robots in four different scenarios and simulation results with up to fifty vehicles. We also analyze the overall quality of the proposed traffic management method and compare its performance to other state-of-the-art multi-vehicle coordination approaches.     

Centralized fleet management system for cybernetic transportation

In this article, we present a centralized fleet management system (CFMS) for cybernetic vehicles called cybercars. Cybercars are automatically guided vehicles for passenger transport on dedicated networks like amusement parks, shopping centres etc. The users make reservations for the vehicles through phone, internet, kiosk etc and the CFMS schedules the cybercars to pick the users at their respective stations at desired time intervals. The CFMS has centralized control of the routing network and performs pooling of customer requests, scheduling and routing of cybercars to customers, empty cybercars to new services or parking stations and those running below their threshold battery levels to recharging stations. The challenges before CFMS are to assure conflict-free routing, accommodate immediate requests from customers, dynamic updation of vehicle paths and minimize congestion on the whole network. We present the approaches used by CFMS to ensure these functionalities and demonstrate a numerical illustration on a test network.     

电动汽车智能充电策略研究综述

发展电动汽车是减少对石油的依赖以及解决环境污染的有效途径。但是,大规模电动汽车无序充电会严重影响电网的稳定性,主要表现在电网负荷峰值升高、电网电压下降以及电网能量损失变大等方面。众所周知,对电网进行扩容改造将需巨大的成本,而智能充电无需对电网进行大幅度改造即可有效地解决这一难题。智能充电不仅能为电力运营商创造可观的经济效益,同时还能使电动汽车用户受益。智能充电主要分为集中式充电和分散式充电。集中式充电根据电网信息和电动汽车的充电需求规划电动汽车的充电策略。充电策略的制定主要以电网运行成本最小、电网能量损失最小、电网负荷方差最小等为目标。分散式充电主要在获取电网状态信息和电动汽车的充电需求后制定浮动电价以激励电动汽车用户避开用电高峰期。最后,文章简要介绍了在智能充电框架下电动汽车与可再生能源融合的潜力、智能充电对电池的要求以及相应的通讯网络构建等问题。     

Scheduling automated transport vehicles for material distribution systems

The rise of advanced manufacturing technology has led to enhanced, efficient material handling equipment in manufacturing and container terminal environments. In order to exploit the full potential of advanced material handling equipment in real-world industrial environments, novel scheduling approaches capable of ensuring integrated operations for multiple automated transport vehicles need to be developed. This research develops a methodology for scheduling automated transport vehicles to ensure the smooth flow of materials in production and container terminal environments. The procedure consists of a mixed-integer programming model and two meta-heuristic-based algorithms that are proposed to achieve quality schedules within a reasonable amount of time. The obtained results show a significant reduction in the earliness or lateness of material delivery tasks and an improvement in operational performance, demonstrating that the proposed approaches are capable of ensuring smooth material distribution by scheduling automated transport vehicles in an integrated manner.     

Distributed and centralised material handling scheduling: Comparison and results of a simulation study

Part of a larger research that employs decentralized holonic modelling techniques in manufacturing planning and control, this work proposes a holonic-based material handling system and contrasts the centralized and distributed scheduling approaches for the allocation of material handling operations to the available system resources. To justify the use of the decentralized holonic approach and assess its performance compared to conventional scheduling systems, a series of evaluation tests and a simulation study are carried out. As illustrated by the results obtained from the simulation study, the decentralized holonic approach is capable of delivering competitive feasible solutions in, practically, real-time.     

A holistic approach to decentralized structural damage localization using wireless sensor networks

Wireless sensor networks (WSNs) have become an increasingly compelling platform for Structural Health Monitoring (SHM) applications, since they can be installed relatively inexpensively onto existing infrastructure. Existing approaches to SHM in WSNs typically address computing system issues or structural engineering techniques, but not both in conjunction. In this paper, we propose a holistic approach to SHM that integrates a decentralized computing architecture with the Damage Localization Assurance Criterion algorithm. In contrast to centralized approaches that require transporting large amounts of sensor data to a base station, our system pushes the execution of portions of the damage localization algorithm onto the sensor nodes, reducing communication costs by two orders of magnitude in exchange for moderate additional processing on each sensor. We present a prototype implementation of this system built using the TinyOS operating system running on the Intel Imote2 sensor network platform. Experiments conducted using two different physical structures demonstrate our system’s ability to accurately localize structural damage. We also demonstrate that our decentralized approach reduces latency by 65.5% and energy consumption by 64.0% compared to a typical centralized solution.     

Nonlinear Model Predictive Formation Flight

This correspondence paper presents the validation of a formation-flight control technique with obstacle avoidance capability based on nonlinear model predictive algorithms. Control architectures for multiagent systems employed in this correspondence paper can be categorized as centralized, sequential-decentralized, and fully decentralized methods. Centralized methods generally have better performance than decentralized methods. However, it is well known that the performance of the centralized methods for formation flight degrades when there exists communication failure among the vehicles, and they require more computation time than the decentralized method. This correspondence paper evaluates the control performance and the computation time reduction of the sequential-decentralized and fully decentralized methods in comparison with the centralized method and shows that the fully decentralized method can be made effective against short-term communication failure. The control inputs for formation flight are computed by nonlinear model predictive control (NMPC). The control input saturation and state constraints are incorporated as inequality constraints using Karush–Kuhn–Tucker conditions in the NMPC framework, and the collision avoidance can be considered in real time. The proposed schemes are validated by numerical simulations, which include the process and measurement noise for more realistic situations.      

A P2P strategy for QoS discovery and SLA negotiation in Grid environment

In recent years, Grid systems and Peer to Peer networks are the most commonly-used solutions to achieve the same goal: the sharing of resources and services in heterogeneous, dynamic, distributed environments. Many studies have proposed hybrid approaches that try to conjugate the advantages of the two models. This paper proposes an architecture that integrates the P2P interaction model in Grid environments, so as to build an open cooperative model wherein Grid entities are composed in a decentralized way. In particular, this paper focuses on a QoS aware discovery algorithm for P2P Grid systems, analyzing protocol and explaining techniques used to improve its performance.     

高效的混合式查找方案*

通过整合基于服务器/客户机模式的集中式查找方案和基于对等计算模式的分散式查找方案,提出了一种混合式查找方案。该方案既有可扩展性、高容错性和自组织性等分散式查找方案的优点,又有利于集中管理和控制,安全性好等集中式查找方案的优势。仿真实验结果表明,该方案的RDP指标优于常规的分散式查找方案,实际查询时间开销更是远远少于常规的分散式查找方案。     

Intra-domain routing convergence with centralized control

The decentralized control scheme for routing in current IP networks has been questioned, and a centralized routing scheme has been proposed as an alternative. In this paper, we compare the convergence of centralized control scheme with decentralized link-state routing protocols. We first review the architectural advantages and challenges of centralized control. Thereafter, we identify and discuss the components of the convergence time in both schemes. We present how to achieve fast routing convergence in networks with centralized control. In particular, we analyze how to distribute forwarding information efficiently. Finally, we perform simulation studies on the convergence time for both real and synthetic network topologies and study the impact of control element location, link weights, and number of failures on the convergence time. The results show that the centralized control scheme can provide faster routing convergence than link-state routing protocols.     

Dynamic Web Service discovery architecture based on a novel peer based overlay network

Service Oriented Computing and its most famous implementation technology Web Services (WS) are becoming an important enabler of networked business models. Discovery mechanisms are a critical factor to the overall utility of Web Services. So far, discovery mechanisms based on the UDDI standard rely on many centralized and area-specific directories, which poses information stress problems such as performance bottlenecks and fault tolerance. In this context, decentralized approaches based on Peer to Peer overlay networks have been proposed by many researchers as a solution. In this paper, we propose a new structured P2P overlay network infrastructure designed for Web Services Discovery. We present theoretical analysis backed up by experimental results, showing that the proposed solution outperforms popular decentralized infrastructures for web discovery, Chord (and some of its successors), BATON (and it’s successor) and Skip-Graphs.     

Cooperative output regulation with application to multi-agent consensus under switching network

In this paper, we consider the cooperative output regulation of linear multi-agent systems under switching network. The problem can be viewed as a generalization of the leader-following consensus problem of multi-agent systems. Due to the limited information exchanges of different subsystems, the problem cannot be solved by the decentralized approach and is not allowed to be solved by the centralized control. By devising a distributed observer network, we can solve the problem by both dynamic state feedback control and dynamic measurement output feedback control. As an application of our main result, we show that a special case of our results leads to the solution of the leader-following consensus problem of linear multi-agent systems.     

A hayekian self-organization approach to service allocation in computing systems

Future ‘on-demand’ computing systems, often depicted as potentially large scale and complex Service-Oriented Architectures, will need innovative management approaches for controlling and matching services demand and supply. Centralized optimization approaches reach their bounds with increasing network size and number of nodes. The search for decentralized approaches has led to build on self-organization concepts like Autonomic Computing, which draw their inspiration from Biology. This article shows how an alternative self-organization concept from Economics, the Catallaxy concept of F.A. von Hayek, can be realized for allocating service supply and demand in a distributed ‘on-demand’ web services network. Its implementation using a network simulator allows evaluating the approach against a centralized resource broker, by dynamically varying connection reliability and node density in the network. Exhibiting Autonomic Computing properties, the Catallaxy realization outperforms a centralized broker in highly dynamic environments.     

A decentralized approach for mining event correlations in distributed system monitoring

Nowadays, there is an increasing demand to monitor, analyze, and control large scale distributed systems. Events detected during monitoring are temporally correlated, which is helpful to resource allocation, job scheduling, and failure prediction. To discover the correlations among detected events, many existing approaches concentrate detected events into an event database and perform data mining on it. We argue that these approaches are not scalable to large scale distributed systems as monitored events grow so fast that event correlation discovering can hardly be done with the power of a single computer. In this paper, we present a decentralized approach to efficiently detect events, filter irrelative events, and discover their temporal correlations. We propose a MapReduce-based algorithm, MapReduce-Apriori, to data mining event association rules, which utilizes the computational resource of multiple dedicated nodes of the system. Experimental results show that our decentralized event correlation mining algorithm achieves nearly ideal speedup compared to centralized mining approaches.     

Price-driven coordination method for solving plant-wide MPC problems

In large-scale model predictive control (MPC) applications, such as plant-wide control, two possible approaches to MPC implementation are centralized and decentralized MPC schemes. These represent the two extremes in the “trade-off” among the desired characteristics of an industrial MPC system, namely accuracy, reliability and maintainability. To achieve optimal plant operations, coordination of decentralized MPC controllers has been identified as both an opportunity and a challenge. Typically, plant-wide MPC problem can be formulated as a large-scale quadratic program (QP) with linking equality constraints. Such problems can be decomposed and solved with the price-driven coordination method and on-line solutions of these structured large-scale optimization problems require an efficient price-adjustment strategy to find an “equilibrium price”. This work develops an efficient price-adjustment algorithm based on Newton’s method, in which sensitivity analysis and active set change identification techniques are employed. With the off-diagonal element abstraction technique and the enhanced priced driven coordination algorithm, a coordinated, decentralized MPC framework is proposed. Several case studies show that the proposed coordination-based decentralized MPC scheme is an effective approach to plant-wide MPC applications, which provides a high degree of reliability and accuracy at a reasonable computational load.     

A centralized control system for ecological vehicle platooning using linear quadratic regulator theory

This paper presents an ecological vehicle platooning control system that aims in reducing overall fuel consumption of the vehicles in a platoon. A centralized linear quadratic regulator system for controlling the vehicles in the platoon has been developed considering the aerodynamic characteristics of the vehicle and the resistance due to the road slope. The proposed control system is simulated on a highway with up?Cdown slopes for high speed driving. Its fuel saving performance is compared with a conventional decentralized vehicle platooning control system. Computer simulation results reveal the significant improvement in fuel economy by the proposed control system.     

LMI solutions for H-two and H-infinity decentralized controllers applied to an aerothermic process

In this paper, we present a linear matrix inequality (LMI)-based solution to implement H-two and Hinfinity decentralized robust control strategies. Appropriate parametrization of optimal H-two and H-infinity controllers is used. The general formulation of the decentralized control design leads to the optimal determination of both the state feedback gains and the observer gains of the decentralized controllers. This formulation is two folds: first, a centralized controller is obtained, and then, a simplified decentralized solution is derived by optimizing only the observer gains. The mathematical determination of these gains is formulated as an LMI optimization problem that can be easily solved using LMI solvers. As an experimental evaluation of these controllers, a real time application to an aerothermic process is carried out. A continuous-time model of the process obtained with a suitable direct continuous-time identification approach is elaborated. Results illustrating the real performance obtained from the H-two and H-infinity decentralized controllers are discussed and compared with the centralized ones.     

Optimizing Environment Maps for Material Depiction

We present an automated system for optimizing and synthesizing environment maps that enhance the appearance of materials in a scene. We first identify a set of lighting design principles for material depiction. Each principle specifies the distinctive visual features of a material and describes how environment maps can emphasize those features. We express these principles as linear or quadratic image quality metrics, and present a general optimization framework to solve for the environment map that maximizes these metrics. We accelerate metric evaluation using an approach dual to precomputed radiance transfer (PRT). In contrast to standard PRT that integrates light transport over the lighting domain to generate an image, we pre‐integrate light transport over the image domain to optimize for lighting. Finally we present two techniques for transforming existing photographic environment maps to better emphasize materials. We demonstrate the effectiveness of our approach by generating environment maps that enhance the depiction of a variety of materials including glass, metal, plastic, marble and velvet.