3,951 research outputs found
Ant Colony Optimization for Multilevel Assembly Job Shop Scheduling
Job shop scheduling is one of the most explored areas in the last few decades. Although it is very commonly witnessed in real-life situations, very less investigation has been carried out in scheduling operations of multi-level jobs, which undergo serial, parallel, and assembly operations in an assembly job shop. In this work, some of the dispatch rules, which have best performances in scheduling multilevel jobs in dynamic assembly job shop, are tested in static assembly job shop environment. A new optimization heuristic based on Ant Colony Algorithm is proposed and its performance is compared with the dispatch rules
Translating Neuralese
Several approaches have recently been proposed for learning decentralized
deep multiagent policies that coordinate via a differentiable communication
channel. While these policies are effective for many tasks, interpretation of
their induced communication strategies has remained a challenge. Here we
propose to interpret agents' messages by translating them. Unlike in typical
machine translation problems, we have no parallel data to learn from. Instead
we develop a translation model based on the insight that agent messages and
natural language strings mean the same thing if they induce the same belief
about the world in a listener. We present theoretical guarantees and empirical
evidence that our approach preserves both the semantics and pragmatics of
messages by ensuring that players communicating through a translation layer do
not suffer a substantial loss in reward relative to players with a common
language.Comment: Fixes typos and cleans ups some model presentation detail
Multitask Diffusion Adaptation over Networks
Adaptive networks are suitable for decentralized inference tasks, e.g., to
monitor complex natural phenomena. Recent research works have intensively
studied distributed optimization problems in the case where the nodes have to
estimate a single optimum parameter vector collaboratively. However, there are
many important applications that are multitask-oriented in the sense that there
are multiple optimum parameter vectors to be inferred simultaneously, in a
collaborative manner, over the area covered by the network. In this paper, we
employ diffusion strategies to develop distributed algorithms that address
multitask problems by minimizing an appropriate mean-square error criterion
with -regularization. The stability and convergence of the algorithm in
the mean and in the mean-square sense is analyzed. Simulations are conducted to
verify the theoretical findings, and to illustrate how the distributed strategy
can be used in several useful applications related to spectral sensing, target
localization, and hyperspectral data unmixing.Comment: 29 pages, 11 figures, submitted for publicatio
Comparing the Performance of Expert User Heuristics and an Integer Linear Program in Aircraft Carrier Deck Operations
Planning operations across a number of domains can be considered as resource allocation problems with timing constraints. An unexplored instance of such a problem domain is the aircraft carrier flight deck, where, in current operations, replanning is done without the aid of any computerized decision
support. Rather, veteran operators employ a set of experience based
heuristics to quickly generate new operating schedules. These expert user heuristics are neither codified nor evaluated by the United States Navy; they have grown solely from the convergent experiences of supervisory staff. As unmanned aerial vehicles (UAVs) are introduced in the aircraft carrier domain,
these heuristics may require alterations due to differing capabilities. The inclusion of UAVs also allows for new opportunities for on-line planning and control, providing an alternative to the current heuristic-based replanning methodology. To investigate these issues formally, we have developed a decision support system for flight deck operations that utilizes a conventional
integer linear program-based planning algorithm. In this system, a human operator sets both the goals and constraints for the algorithm, which then returns a proposed schedule for operator approval. As a part of validating this system, the performance of this collaborative human–automation planner was compared with that of the expert user heuristics over a set of test scenarios. The resulting analysis shows that human heuristics often outperform the plans produced by an optimization algorithm, but are also
often more conservative
Ant colony optimization for the single model U-type assembly line balancing problem
Cataloged from PDF version of article.An assembly line is a production line in which units move continuously through a
sequence of stations. The assembly line balancing problem is defined as the allocation of
tasks to an ordered sequence of stations subject to precedence constraints with the
objective of optimizing a performance measure. In this paper, we propose ant colony
algorithms to solve the single-model U-type assembly line balancing problem. We conduct
an extensive experimental study in which the performance of the proposed algorithm is
compared against best known algorithms reported in the literature. The results indicate
that the proposed algorithms display very competitive performance against them.
& 2009 Elsevier B.V. All rights reserved
An integrated ACO approach for the joint production and preventive maintenance scheduling problem in the flowshop sequencing problem.
International audienceIn this paper, an integrated ACO approach to solve joint production and preventive maintenance scheduling problem in permutation flowshops is considered. A newly developed antcolony algorithm is proposed and analyzed for solving this problem, based on a common representation of production and maintenance data, to obtain a joint schedule that is, subsequently, improved by a new local search procedure. The goal is to optimize a common objective function which takes into account both maintenance and production criteria. We compare the results obtained with our algorithm to those of an integrated genetic algorithm developed in previous works. The results and experiments carried out indicate that the proposed ant-colony algorithm provide very effective solutions for this problem
Monitoring using Heterogeneous Autonomous Agents.
This dissertation studies problems involving different types of autonomous agents observing objects of interests in an area. Three types of agents are considered: mobile agents, stationary agents, and marsupial agents, i.e., agents capable of deploying other agents or being deployed themselves. Objects can be mobile or stationary.
The problem of a mobile agent without fuel constraints revisiting stationary objects is formulated. Visits to objects are dictated by revisit deadlines, i.e., the maximum time that can elapse between two visits to the same object. The problem is shown to be NP-complete and heuristics are provided to generate paths for the agent. Almost periodic paths are proven to exist. The efficacy of the heuristics is shown through simulation. A variant of the problem where the agent has a finite fuel capacity and purchases fuel is treated. Almost periodic solutions to this problem are also shown to exist and an algorithm to compute the minimal cost path is provided.
A problem where mobile and stationary agents cooperate to track a mobile object is formulated, shown to be NP-hard, and a heuristic is given to compute paths for the mobile agents. Optimal configurations for the stationary agents are then studied. Several methods are provided to optimally place the stationary agents; these methods are the maximization of Fisher information, the minimization of the probability of misclassification, and the minimization of the penalty incurred by the placement. A method to compute optimal revisit deadlines for the stationary agents is given. The placement methods are compared and their effectiveness shown using numerical results.
The problem of two marsupial agents, one carrier and one passenger, performing a general monitoring task using a constrained optimization formulation is stated. Necessary conditions for optimal paths are provided for cases accounting for constrained release of the passenger, termination conditions for the task, as well as retrieval and constrained retrieval of the passenger. A problem involving two marsupial agents collecting information about a stationary object while avoiding detection is then formulated. Necessary conditions for optimal paths are provided and rectilinear motion is demonstrated to be optimal for both agents.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111439/1/jfargeas_1.pd
Addressing robustness in time-critical, distributed, task allocation algorithms.
The aim of this work is to produce and test a robustness module (ROB-M) that can be generally applied to distributed, multi-agent task allocation algorithms, as robust versions of these are scarce and not well-documented in the literature. ROB-M is developed using the Performance Impact (PI) algorithm, as this has previously shown good results in deterministic trials. Different candidate versions of the module are thus bolted on to the PI algorithm and tested using two different task allocation problems under simulated uncertain conditions, and results are compared with baseline PI. It is shown that the baseline does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. However, when PI is run with one of the candidate robustness modules, the failure rate becomes very low for both problems, even under high simulated uncertainty, and so its architecture is adopted for ROB-M and also applied to MIT’s baseline Consensus Based Bundle Algorithm (CBBA) to demonstrate its flexibility. Strong evidence is provided to show that ROB-M can work effectively with CBBA to improve performance under simulated uncertain conditions, as long as the deterministic versions of the problems can be solved with baseline CBBA. Furthermore, the use of ROB-M does not appear to increase mean task completion time in either algorithm, and only 100 Monte Carlo samples are required compared to 10,000 in MIT’s robust version of the CBBA algorithm. PI with ROB-M is also tested directly against MIT’s robust algorithm and demonstrates clear superiority in terms of mean numbers of solved tasks.N/
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