Decentralized dynamic task allocation for UAVs with limited communication range

We present the Limited-range Online Routing Problem (LORP), which involves a team of Unmanned Aerial Vehicles (UAVs) with limited communication range that must autonomously coordinate to service task requests. We first show a general approach to cast this dynamic problem as a sequence of decentralized task allocation problems. Then we present two solutions both based on modeling the allocation task as a Markov Random Field to subsequently assess decisions by means of the decentralized Max-Sum algorithm. Our first solution assumes independence between requests, whereas our second solution also considers the UAVs' workloads. A thorough empirical evaluation shows that our workload-based solution consistently outperforms current state-of-the-art methods in a wide range of scenarios, lowering the average service time up to 16%. In the best-case scenario there is no gap between our decentralized solution and centralized techniques. In the worst-case scenario we manage to reduce by 25% the gap between current decentralized and centralized techniques. Thus, our solution becomes the method of choice for our problem

Recent Advances in the Empirics of Organizational Economics

We present a survey of recent contributions in the empirical organizational economics, focusing on management practices and decentralization. Productivity dispersion between firms and countries has motivated the improved measurement of firm organization across industries and countries. There appears to be substantial variation in management practices and decentralization between countries, but especially within countries. Much of the poorer average management quality in countries like Brazil and India seems due to a "long tail" of poorly managed firms, which barely exist in the US. Many basic economic theories are supported by this new data. Some stylized facts include: (1) competition seems to foster improved management and decentralization; (2) larger firms, skillintensive plants and foreign multinationals appear better managed and are more decentralized; (3) family owned and managed firms appear to have worse management; (4) firms facing an environment of lighter labor market regulations, and more human capital intensive organizations specialize relatively more in "people management". There is evidence for complementarities between ICT, decentralization and management, but the relationship is complex and identification of the productivity effects of organizational practices remain a challenge for future research.productivity, organization, management, decentralization

Allocating Resources and Creating Incentives to Improve Teaching and Learning

Offers insights from scholarly literature, related theory, and practical activities to inform the efforts of policymakers, researchers and practitioners to allocate resources and create incentives that result in powerful, equitable learning for all

Human Resource Management and Productivity

In this chapter we examine the relationship between Human Resource Management (HRM) and productivity. HRM includes incentive pay (individual and group) as well as many nonpay aspects of the employment relationship such as matching (hiring and firing) and work organization (e.g. teams, autonomy). We place HRM more generally within the literature on management practices and productivity. We start with some facts on levels and trends of both HRM and productivity and the main economic theories of HRM. We look at some of the determinants of HRM - risk, competition, ownership and regulation. The largest section analyses the impact of HRM on productivity emphasizing issues of methodology, data and results (from micro-econometric studies). We conclude briefly with suggestions of avenues for future frontier work.human resource management, productivity, personnel economics

ORGANIZATIONAL REFORM AND THE EXPANSION OF THE SOUTH’S VOICE AT THE FUND

What organizational reforms might increase the influence of developing member countries within the International Monetary Fund? In this paper we argue that a variety of organizational changes are both feasible and could substantially increase the ability of developing countries to articulate policy alternatives and advance change. We focus particularly on changes in the recruitment, training, career paths and deployment of the Fund’s staff. Our recommendations address two general issues. First, we explore ways to diversify the “intellectual portfolio” of the staff by drawing more effectively on hands-on knowledge of the concrete circumstances that shape policy outcomes in the South. More mid-career hiring of staff with practical experience inside developing country institutions could increase the degree to which the distinctive institutional circumstances of developing members are taken into account in formulating Fund policies and implementing them. Allocating a larger share of the Fund’s resources to research consulting contracts for researchers and institutions based in developing countries could also expand input of ideas that reflect the experience of member countries from the South. Second, large asymmetries in workload currently make it difficult for those working on the needs of developing members to formulate and advocate alternative policies. We suggest a number of ways in which even modest reallocation and addition of staff resources might create breathing space that would allow Executive Directors from developing countries to play a larger role in shaping the Fund’s policies.

Decentralized Multi-Subgroup Formation Control With Connectivity Preservation and Collision Avoidance

This paper proposes a formation control algorithm to create separated multiple formations for an undirected networked multi-agent system while preserving the network connectivity and avoiding collision among agents. Through the modified multi-consensus technique, the proposed algorithm can simultaneously divide a group of multiple agents into any arbitrary number of desired formations in a decentralized manner. Furthermore, the agents assigned to each formation group can be easily reallocated to other formation groups without network topological constraints as long as the entire network is initially connected; an operator can freely partition agents even if there is no spanning tree within each subgroup. Besides, the system can avoid collision without loosing the connectivity even during the transient period of formation by applying the existing potential function based on the network connectivity estimation. If the estimation is correct, the potential function not only guarantees the connectivity maintenance but also allows some extra edges to be broken if the network remains connected. Numerical simulations are performed to verify the feasibility and performance of the proposed multi-subgroup formation control

Multi-Robot Task Allocation: A Spatial Queuing Approach

Multi-Robot Task Allocation (MRTA) is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to match a set of robots to a set of tasks so that the tasks can be completed by the robots while optimizing a certain metric such as the time required to complete all tasks, distance traveled by the robots and energy expended by the robots. We consider a scenario where the tasks can appear dynamically and the location of tasks are not known a priori by the robots. Additionally, for a task to be completed, it needs to be performed by multiple robots. This setting is called the MR-ST-TA (multi-robot, single-task, time- extended assginment) category of MRTA; solving the MRTA problem for this category is a known NP-hard problem. In this thesis, we address this problem by proposing a new algorithm that uses a spatial queue-based model to allocate tasks between robots while comparing its performance to several other known methods. We have implemented these algorithms on an accurately simulated model of Corobot robots within the Webots simulator for different numbers of robots and tasks. The results show that our method is adept in all proffered environments, especially scenarios that benefit from path planning, whereas other methods display inherent weakness at one end of the spectrum: a decentralized greedy approach exhibits inefficient behavior as the robot to task ratio dips below one, whereas the Hungarian method (an offline algorithm) fails to keep pace as the robot count increases

An adaptive multi-agent system for task reallocation in a MapReduce job

International audienceWe study the problem of task reallocation for load-balancing of MapReduce jobs in applications that process large datasets. In this context, we propose a novel strategy based on cooperative agents used to optimise the task scheduling in a single MapReduce job. The novelty of our strategy lies in the ability of agents to identify opportunities within a current unbalanced allocation, which in turn trigger concurrent and one-to-many negotiations amongst agents to locally reallocate some of the tasks within a job. Our contribution is that tasks are reallocated according to the proximity of the resources and they are performed in accordance to the capabilities of the nodes in which agents are situated. To evaluate the adaptivity and responsiveness of our approach, we implement a prototype test-bed and conduct a vast panel of experiments in a heterogeneous environment and by exploring varying hardware configurations. This extensive experimentation reveals that our strategy significantly improves the overall runtime over the classical Hadoop data processing