Finding good nurse duty schedules: a case study

Constructing duty schedules for nurses at large hospitals is a difficult problem. The objective is usually to ensure that there is always sufficient staff on duty, while taking into account individual preferences with respect to work patterns, requests for leave and financial restrictions, in such a way that all employees are treated fairly. The problem is typically solved via mixed integer programming or heuristic (local) search methods in the operations research literature. In this paper the problem is solved using a tabu search approach as a case study at Stikland Hospital, a large psychiatric hospital in the South African Western Cape, for which a computerized decision support system with respect to nurse scheduling was developed. This decision support system, called NuRoDSS (short for Nurse Rostering Decision Support System) is described in some detail.     

An evolutionary approach for the nurse rerostering problem

The personnel scheduler constructs a deterministic personnel roster that determines the line-of-work for each personnel member. When unexpected events disrupt this roster, the feasibility needs to be restored by constructing a new workable roster. The scheduler must reassign the set of employees in order to cover the disrupted shift such that the staffing requirements and the time-related personnel constraints remain satisfied. In this paper, we propose an evolutionary meta-heuristic to solve the nurse rerostering problem. We show that the proposed procedure performs consistently well under many different circumstances. We test different optimisation strategies and compare our procedure with the existing literature on a dataset that is carefully designed in a controlled and varied way.     

A constraint programming based column generation approach to nurse rostering problems

This paper presents our investigations on a hybrid constraint programming based column generation (CP–CG) approach to nurse rostering problems. We present a complete model to formulate all the complex real-world constraints in several benchmark nurse rostering problems. The hybrid CP–CG approach is featured with not only the effective relaxation and optimality reasoning of linear programming but also the powerful expressiveness of constraint programming in modeling the complex logical constraints in nurse rostering problems. In solving the CP pricing subproblem, we propose two strategies to generate promising columns which contribute to the efficiency of the CG procedure. A Depth Bounded Discrepancy Search is employed to obtain diverse columns. A cost threshold is adaptively tightened based on the information collected during the search to generate columns of good quality. Computational experiments on a set of benchmark nurse rostering problems demonstrate a faster convergence by the two strategies and justify the effectiveness and efficiency of the hybrid CP–CG approach.     

Particle swarm-based olfactory guided search

This article presents a new algorithm for searching odour sources across large search spaces with groups of mobile robots. The proposed algorithm is inspired in the particle swarm optimization (PSO) method. In this method, the search space is sampled by dynamic particles that use their knowledge about the previous sampled space and share this knowledge with other neighbour searching particles allowing the emergence of efficient local searching behaviours. In this case, chemical searching cues about the potential existence of upwind odour sources are exchanged. By default, the agents tend to avoid each other, leading to the emergence of exploration behaviours when no chemical cue exists in the neighbourhood. This behaviour improves the global searching performance. The article explains the relevance of searching odour sources with autonomous agents and identifies the main difficulties for solving this problem. A major difficulty is related with the chaotic nature of the odour transport in the atmosphere due to turbulent phenomena. The characteristics of this problem are described in detail and a simulation framework for testing and analysing different odour searching algorithms was constructed. The proposed PSO-based searching algorithm and modified versions of gradient-based searching and biased random walk-based searching strategies were tested in different environmental conditions and the results, showing the effectiveness of the proposed strategy, were analysed and discussed. Lino Marques is an auxiliary professor in the Department of Electrical Engineering, University of Coimbra, and he is a researcher in the Institute for Systems and Robotics (ISR-UC). He received his Licenciatura, MSc. and Ph.D. degrees in Electrical Engineering from the University of Coimbra, Portugal. His main research interests include embedded systems, mechatronics, robotics for risky environments, optical range sensors, artificial olfaction systems and mobile robot olfaction. Urbano Nunes is an associate professor of the University of Coimbra and a researcher of the Institute for Systems and Robotics (ISR-UC), where he has been involved in research and teaching since 1983. He received his Licenciatura and Ph.D. degrees in Electrical Engineering from the University of Coimbra, Portugal, in 1983 and 1995, respectively. He is the coordinator of the Mechatronics Laboratory of ISR-UC, and had been responsible for several funded projects in the areas of mobile robotics and intelligent vehicles. His research interests include mobile robotics, intelligent vehicles, and mechatronics. Professor Urbano Nunes serves on the Editorial Board of the Journal on Machine Intelligence and Robotic Control, and currently he is co-chair of the IEEE RAS TC on Intelligent Transportation Systems. Currently he is the Program Chair of the IEEE ITSC2006. He has served as General Co-Chair of ICAR 2003 and as member of several program committees of international conferences. Aníbal T. De Almeida graduated in Electrical Engineering, University of Porto, 1972, and received a Ph.D. in Electrical Engineering, from Imperial College, University of London, 1977. Currently he is a Professor in the Department of Electrical Engineering, University of Coimbra, and he is the Director of the Institute of Systems and Robotics since 1993. Professor De Almeida is a consultant of the European Commission Framework Programmes. He is the co-author of five books and more than one hundred papers in international journals, meetings and conferences. He has coordinated several European and national research projects.     

Cooperative control for target-capturing task based on a cyclic pursuit strategy

This paper studies a methodology for group coordination and cooperative control of n agents to achieve a target-capturing task in 3D space. The proposed approach is based on a cyclic pursuit strategy, where agent i simply pursues agent i+1 modulo n. The distinctive features of the proposed method are as follows. First, no communication mechanism between agents is necessary and thus it is inherently a distributed control strategy. Also, it is a simple robust memoryless control scheme which has self-stability property. Finally, it guarantees a global convergence of all agents to the desired formation. Further, it is also guaranteed that no collision occurs. Simulation examples are given to illustrate the efficacy of the proposed method and the achievement of a desired pursuit pattern in 3D space.     

RoboCup仿真比赛中的Multi-Agent层次协作模型

本文针对RoboCup仿真比赛的多智能体协作问题,分析了目前在RoboCup中的几个典型多智能体协作模型,提出一种三层的Multi—Agent层次协作模型,它包括全局层、局部层和个体层。实战证明该模型是合理的、有效的。     

Cooperative semi-global robust output regulation for a class of nonlinear uncertain multi-agent systems

In this paper, we study the cooperative semi-global robust output regulation problem for a class of minimum phase nonlinear uncertain multi-agent systems. This problem is a generalization of the leader-following tracking problem in the sense that it further addresses such issues as disturbance rejection, robustness with respect to parameter uncertainties. To solve this problem, we first introduce a type of distributed internal model that converts the cooperative semi-global robust output regulation problem into a cooperative semi-global robust stabilization problem of the so-called augmented system. We then solve the semi-global stabilization problem via distributed dynamic output control law by utilizing and combining a block semi-global backstepping technique, a simultaneous high gain feedback control technique, and a distributed high gain observer technique.     

Design and evaluation of a multi-recommendation system for local code search

Searching for relevant code in the local code base is a common activity during software maintenance. However, previous research indicates that 88% of manually composed search queries retrieve no relevant results. One reason that many searches fail is existing search tools’ dependence on string matching algorithms, which cannot find semantically related code. To solve this problem by helping developers compose better queries, researchers have proposed numerous query recommendation techniques, relying on a variety of dictionaries and algorithms. However, few of these techniques are empirically evaluated by usage data from real-world developers. To fill this gap, we designed a multi-recommendation system that relies on the cooperation between several query recommendation techniques. We implemented and deployed this recommendation system within the Sando code search tool and conducted a longitudinal field study. Our study shows that over 34% of all queries were adopted from recommendation; and recommended queries retrieved results 11% more often than manual queries.     

Solving dimensioning tasks for proportionally fair networks carrying elastic traffic

The paper studies optimal dimensioning problems associated with the notion of “proportional fairness” (PF) considered for packet networks carrying elastic (greedy) traffic, i.e. the traffic streams that can use up, perhaps within certain assumed bounds, any bandwidth that is assigned to them. We give effective, simple and explicit solutions to the introduced optimisation tasks based on the dual optimisation theory, and illustrate them with numerical examples. The explicit solutions can guide a network operator how to proceed when introducing PF into his network.     

Cooperative target pursuit by multiple UAVs in an adversarial environment

This paper presents the development of a cooperation strategy for multiple UAVs to pursue a target moving in an adversarial environment where threat exposure should be minimized, and obstacles and restricted areas should be avoided. A probabilistic approach is used to model the adversarial environment. A cost function is defined to quantify placement of UAVs around the target in formation in terms of threat exposure level and distance to the target. The cost function is used to develop a cooperation strategy for a team of UAVs to follow the target such that the total threat exposure of the team and the average distance to the target throughout the pursuit are minimized according to the weighting coefficients specified. The cooperation strategy has the feature of collision avoidance as well as data-fusion-based estimation of the target trajectory based on noisy measurements. Simulation results have demonstrated that the cooperation reduces the risk of losing the target during the pursuit while avoiding obstacles and restricted areas. Further, the UAVs guided by the cooperation strategy can follow the target closer without increasing the total threat exposure level as compared to cases where the UAVs pursue the target without cooperation.     

Game-theoretic static load balancing for distributed systems

In this paper, we present a game theoretic approach to solve the static load balancing problem for single-class and multi-class (multi-user) jobs in a distributed system where the computers are connected by a communication network. The objective of our approach is to provide fairness to all the jobs (in a single-class system) and the users of the jobs (in a multi-user system). To provide fairness to all the jobs in the system, we use a cooperative game to model the load balancing problem. Our solution is based on the Nash Bargaining Solution (NBS) which provides a Pareto optimal solution for the distributed system and is also a fair solution. An algorithm for computing the NBS is derived for the proposed cooperative load balancing game. To provide fairness to all the users in the system, the load balancing problem is formulated as a non-cooperative game among the users who try to minimize the expected response time of their own jobs. We use the concept of Nash equilibrium as the solution of our non-cooperative game and derive a distributed algorithm for computing it. Our schemes are compared with other existing schemes using simulations with various system loads and configurations. We show that our schemes perform near the system optimal schemes and are superior to the other schemes in terms of fairness.     

Searching in Cooperative Patent Classification: Comparison between keyword and concept-based search

International patent corpus is a gigantic source containing today about 80 million of documents. Every patent is manually analyzed by patent officers and then classified by a specific code called Patent Class (PC). Cooperative Patent Classification CPC is the new classification system introduced since January 2013 in order to standardize the classification systems of all major patent offices. Like keywords for papers, PCs point to the core of the invention, describing concisely what they contain inside. Most of patents strategies are based on PC as filter for results therefore the selection of relevant PCs is often a primary and crucial activity. This task is considered particularly challenging and only few tools have been specially developed for this purpose. The most efficient tools are provided by patent offices of EPO and WIPO.This paper analyzes their PCs search strategy (mainly based on keyword-based engines) in order to identify main limitations in terms of missing relevant PCs (recall) and non-relevant results (precision). Patents have been processed by KOM, a semantic patent search tool developed by the authors. Unlike all other PC search tools, KOM uses semantic parser and many knowledge bases for carrying out a conceptual patent search. Its functioning is described step by step through a detailed analysis pointing out the benefits of a concept-based search vis-à-vis a keyword-based search. An exemplary case is proposed dealing with CPCs describing the sterilization of contact lenses. Comparison could be likewise conducted on other PCs such as International (IPC), European (ECLA) or United States (USPC) patent classification codes.     

基于线性规划算法的多机器人系统的协同定位

传统的多机器人协同定位研究大多采用主从式模型,它要求给主机器人配备许多昂贵且不易获得的高精度定位设备,这在实际应用中有很大的局限性。针对这一问题,通过对多机器人并行式协同定位模型的分析,建立了机器人群体协同定位的线性传感器约束模型。在满足传感器约束条件下,利用线性规划算法得到了系统内每一机器人的不确定区域。理论推导和仿真结果表明,该定位算法较高精度地实现了机器人群体的协同定位,且具有成本低、精度较高等工程实用价值。     

Combining information from multiple search engines—Preliminary comparison

The total number of popular search engines has decreased over time from its peak of the late 1990s. However, when combining the three remaining major ones (Yahoo!, Google, MS Live) with large repositories of information (e.g. BBC.com, NYT.com, Gazeta.pl, etc.), the total number of important information sources can be seen as slowly increasing. Focusing on utilization of search engines only, it is easy to observe that the same query issued to each one of them results in different “suggestions.” The question thus arrives, is it possible (and worthy) to combine responses obtained from each one of them into a single answer set. In this paper, we look into three approaches of achieving this goal, which are based on: Game theory, Auction and Consensus methods, while our focus is to study (and compare) their “performance.”     

Beam search algorithm for capacity allocation problem in flexible manufacturing systems

This study considers the operation assignment and tool allocation problem in flexible manufacturing systems. A set of operations together with their required tools are selected so as to maximize the total weight. The machines have limited time and tool magazine capacities and the tools are available in limited quantities. We develop a beam search algorithm and obtain near optimal solutions for large size problems very quickly.     

Study of the performance of multi-behaviour agents for supply chain planning

In today's industrial context, competitiveness is closely associated to supply chain performance. Coordination between business units is essential to increase this performance, in order to produce and deliver products on time to customers, at a competitive price. While planning systems usually follow a single straightforward production planning process, this paper proposes that partners adapt together their local planning process (i.e. planning behaviours) to the different situations met in the supply chain environment. Because each partner can choose different behaviour and all behaviours will have an impact on the overall performance, it is difficult to know which is preferable for each partner to increase their performance. Using agent-based technology, simulation experiments have been undertaken to verify if multi-behaviour planning agents who can change planning behaviours to adapt to their environment can increase supply chain performance. These agents have been implemented in an agent-based planning platform, using a case study illustrating a lumber supply chain. The performance analysis shows that advanced planning systems can take advantage of using multiple planning processes, because of the dynamic context of supply chains.     

Cooperative tracking control for linear multi-agent systems with external disturbances under a directed graph

This paper studiesthe cooperative tracking control problem for multi-agent systems with unknown external disturbances under a directed graph. The leader's input is nonzero and unavailable to the followers. Based on the relative output measurements of neighbouring agents, a novel observer-based cooperative tracking protocol is presented, where the a group of intermediate estimators are constructed to estimate the tracking errors and the combined signal of the disturbances and the leader's input simultaneously, then decentralised tracking protocols are designed based on the obtained estimates. The states of the global tracking error system are proved to be uniformly ultimately bounded. A numerical example of aircraft illustrates the effectiveness of the cooperative tracking protocol.     

分布式视频服务器系统中的协同缓存策略

在分布式视频服务器系统中,利用协同缓存(CooperativeCaching)技术,可以达到充分利用系统中各服务器上的空闲内存,以形成一个更大的、逻辑上完整的协同缓存,提升总体的缓存命中率,进而提升整个系统性能的目的。文章就来对这一背景下的协同缓存技术进行研究,分析采用协同缓存的动因,比较几种传统的协同缓存机制,并提出了一种适合于分布式视频服务器系统的协同缓存策略。     

协同产品开发集成环境的研究与实现

首先讨论了协同产品过程的特点,分析了能够支持协同产品开发的关键技术。文中建立了协同产品开发集成环境的体系结构,该体系结构能够满足产品开发人员在各个层次上的协同需要。在此基础上提出了协同产品开发集成环境的具体结构,它建立在Ｌｏｔｕｓ　Ｎｏｔｅｓ和Ｐｒｏ／Ｉｎｔｒａｌｉｎｋ的基础上,能够为产品开发人员提供一个高效的协同工作环境。     

Cooperative passers-by tracking with a mobile robot and external cameras

In this paper, we present a cooperative passers-by tracking system between fixed view wall mounted cameras and a mobile robot. The proposed system fuses visual detections from wall mounted cameras and detections from a mobile robot–in a centralized manner–employing a “tracking-by-detection” approach within a Particle Filtering strategy. This tracking information is then used to endow the robot with passers-by avoidance ability to facilitate its navigation in crowds during the execution of a person following mission. The multi-person tracker’s ability to track passers-by near the robot distinctively is demonstrated through qualitative and quantitative off-line experiments. Finally, the designed perceptual modalities are deployed on our robotic platform, controlling its actuators via visual servoing techniques and free space diagrams in the vicinity of the robot, to illustrate the robot’s ability to follow a given target person in human crowded areas.