Market-Based Task Allocation Mechanisms for Limited Capacity Suppliers

This paper reports on the design and comparison of two economically-inspired mechanisms for task allocation in environments where sellers have finite production capacities and a cost structure composed of a fixed overhead cost and a constant marginal cost. Such mechanisms are required when a system consists of multiple self-interested stakeholders that each possess private information that is relevant to solving a system-wide problem. Against this background, we first develop a computationally tractable centralised mechanism that finds the set of producers that have the lowest total cost in providing a certain demand (i.e. it is efficient). We achieve this by extending the standard Vickrey-Clarke-Groves mechanism to allow for multi-attribute bids and by introducing a novel penalty scheme such that producers are incentivised to truthfully report their capacities and their costs. Furthermore our extended mechanism is able to handle sellers' uncertainty about their production capacity and ensures that individual agents find it profitable to participate in the mechanism. However, since this first mechanism is centralised, we also develop a complementary decentralised mechanism based around the continuous double auction. Again because of the characteristics of our domain, we need to extend the standard form of this protocol by introducing a novel clearing rule based around an order book. With this modified protocol, we empirically demonstrate (with simple trading strategies) that the mechanism achieves high efficiency. In particular, despite this simplicity, the traders can still derive a profit from the market which makes our mechanism attractive since these results are a likely lower bound on their expected returns

Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art

Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP

Centralised Versus Market-Based Control Under Environment Uncertainty: Case of the Mobile Task Allocation Problem (MTAP)

This paper aims at comparing the centralised versus the market-based approach. This is done in the context of the mobile task allocation problem (MTAP) from the perspective of environmental uncertainty. MTAP is defined as an optimization problem for planning the assignment of service tasks to mobile workers. Environmental uncertainty is introduced through the injection of stochastic tasks and dynamic travel delays. A multi-agent simulator is employed to experiment the behaviour of each approach in reaction to different uncertainty levels. Preliminary results suggest a tentative conceptual model to evaluate the suitability of each approach to address MTAP in function of uncertainty. It is suggested that uncertainty’s effect on achieved performance is moderated by the timeliness of decision making, workers’ degree of local knowledge, and problem’s complexity and size

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

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

Agent-based transportation planning compared with scheduling heuristics

Here we consider the problem of dynamically assigning vehicles to transportation orders that have di¤erent time windows and should be handled in real time. We introduce a new agent-based system for the planning and scheduling of these transportation networks. Intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. We use simulation to compare the on-time delivery percentage and the vehicle utilization of an agent-based planning system to a traditional system based on OR heuristics (look-ahead rules, serial scheduling). Numerical experiments show that a properly designed multi-agent system may perform as good as or even better than traditional methods

Incentive-compatible route coordination of crowdsourced resources

Technical ReportWith the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen-ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1-approximation algorithm to solve the 2 problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments

What’s in it for me? Incentive-compatible route coordination of crowdsourced resources

With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresence-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1/2-approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798