An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards

In many domains such as transportation and logistics, search and rescue, or cooperative surveillance, tasks are pending to be allocated with the consideration of possible execution uncertainties. Existing task coordination algorithms either ignore the stochastic process or suffer from the computational intensity. Taking advantage of the weakly coupled feature of the problem and the opportunity for coordination in advance, we propose a decentralized auction-based coordination strategy using a newly formulated score function which is generated by forming the problem into task-constrained Markov decision processes (MDPs). The proposed method guarantees convergence and at least 50% optimality in the premise of a submodular reward function. Furthermore, for the implementation on large-scale applications, an approximate variant of the proposed method, namely Deep Auction, is also suggested with the use of neural networks, which is evasive of the troublesome for constructing MDPs. Inspired by the well-known actor-critic architecture, two Transformers are used to map observations to action probabilities and cumulative rewards respectively. Finally, we demonstrate the performance of the two proposed approaches in the context of drone deliveries, where the stochastic planning for the drone league is cast into a stochastic price-collecting Vehicle Routing Problem (VRP) with time windows. Simulation results are compared with state-of-the-art methods in terms of solution quality, planning efficiency and scalability.Comment: 17 pages, 5 figure

Revisiting the Evolution and Application of Assignment Problem: A Brief Overview

The assignment problem (AP) is incredibly challenging that can model many real-life problems. This paper provides a limited review of the recent developments that have appeared in the literature, meaning of assignment problem as well as solving techniques and will provide a review on   a lot of research studies on different types of assignment problem taking place in present day real life situation in order to capture the variations in different types of assignment techniques. Keywords: Assignment problem, Quadratic Assignment, Vehicle Routing, Exact Algorithm, Bound, Heuristic etc

Operative Planning with Exchangeable and Mandatory Tasks : Applications to Lot Size Planning and Transportation Planning

Lot-sizing problems of manufacturers and transportation planning problems of forwarders are presented and analyzed in this thesis. These problems represent crucial planning tasks in supply chain management. Due to high fluctuations and competitive markets, companies within supply chains use internal and external resources for the fulfillment of tasks. The thesis claims to contribute to the following topics: (1) introducing mandatory tasks for the DULR, IOTPP, CTPP, and CIOTPP as well as (2) presenting computational studies that demonstrate how much the costs of companies increase due to mandatory tasks. Mandatory tasks are tasks, which have to be fulfilled by appointed resources due to contractual obligations. A lack of research is identified in terms of this topic. It is usually assumed that a task can be fulfilled by any internal or external resources. The thesis describes how these planning tasks with mandatory tasks can be solved by using operations research. Therefore, existing mathematical models and solution approaches have to be extended. The thesis focuses on the determination of the impact of mandatory tasks based on computational studies

Consensus-based auctions for decentralized task assignment

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 137-147).This thesis addresses the decentralized task assignment problem in cooperative autonomous search and track missions by presenting the Consensus-Based class of assignment algorithms. These algorithm make use of information consensus routines to converge on the assignment rather than the situational awareness of the fleet. A market-based approach is used as the mechanism for task selection, while the novel consensus stage of the algorithms allow for fast distributed conflict resolution. Three separate algorithms belonging to the Consensus-Based class of assignment strategies will be presented. The first is the Consensus-Based Auction Algorithm (CBAA), which is a single assignment auction strategy that is shown to be bounded within 50% of the optimal solution, while an upper-bound on convergence is presented. Two multi-assignment algorithms are then presented as extensions of the CBAA. The iterative CBAA executes the single assignment algorithm multiple times in order to build an assignment with multiple tasks. The second algorithm is the more general Consensus-Based Bundle Algorithm (CBBA) in which agents build a candidate bundle of tasks and bid on each task individually based on the improvement in score achieved by adding it to the bundle. Both algorithms are shown to be lower bounded by 50% optimality, while convergence bounds are derived based on the network topology. Numerical results show that the bundle algorithm performs much better than the iterative approach while providing faster convergence times. It is also compared with the Prim Allocation (PA) auction algorithm where it is shown to exhibit much faster convergence times and give better assignments. The CBBA is also implemented in the CSAT simulation test-bed developed by Aurora Flight Sciences in conjunction with MIT, and shown to produce faster response times and better tracking performance than the currently used RDTA algorithm.by Luc Brunet.S.M

Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work. Tasks must be allocated to agents and sequenced according to temporal and spatial constraints. Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots. In this paper, we present a centralized algorithm, named 'Tercio,' that handles tightly intercoupled temporal and spatial constraints. Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure. We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art. Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed