2,589 research outputs found
Generating effective project scheduling heuristics by abstraction and reconstitution
A project scheduling problem consists of a finite set of jobs, each with fixed integer duration, requiring one or more resources such as personnel or equipment, and each subject to a set of precedence relations, which specify allowable job orderings, and a set of mutual exclusion relations, which specify jobs that cannot overlap. No job can be interrupted once started. The objective is to minimize project duration. This objective arises in nearly every large construction project--from software to hardware to buildings. Because such project scheduling problems are NP-hard, they are typically solved by branch-and-bound algorithms. In these algorithms, lower-bound duration estimates (admissible heuristics) are used to improve efficiency. One way to obtain an admissible heuristic is to remove (abstract) all resources and mutual exclusion constraints and then obtain the minimal project duration for the abstracted problem; this minimal duration is the admissible heuristic. Although such abstracted problems can be solved efficiently, they yield inaccurate admissible heuristics precisely because those constraints that are central to solving the original problem are abstracted. This paper describes a method to reconstitute the abstracted constraints back into the solution to the abstracted problem while maintaining efficiency, thereby generating better admissible heuristics. Our results suggest that reconstitution can make good admissible heuristics even better
Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization
Functional brain imaging is a source of spatio-temporal data mining problems.
A new framework hybridizing multi-objective and multi-modal optimization is
proposed to formalize these data mining problems, and addressed through
Evolutionary Computation (EC). The merits of EC for spatio-temporal data mining
are demonstrated as the approach facilitates the modelling of the experts'
requirements, and flexibly accommodates their changing goals
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
The reliable fraction of information is an attractive score for quantifying
(functional) dependencies in high-dimensional data. In this paper, we
systematically explore the algorithmic implications of using this measure for
optimization. We show that the problem is NP-hard, which justifies the usage of
worst-case exponential-time as well as heuristic search methods. We then
substantially improve the practical performance for both optimization styles by
deriving a novel admissible bounding function that has an unbounded potential
for additional pruning over the previously proposed one. Finally, we
empirically investigate the approximation ratio of the greedy algorithm and
show that it produces highly competitive results in a fraction of time needed
for complete branch-and-bound style search.Comment: Accepted to Proceedings of the IEEE International Conference on Data
Mining (ICDM'18
MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
We present multi-agent A* (MAA*), the first complete and optimal heuristic
search algorithm for solving decentralized partially-observable Markov decision
problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for
computing optimal plans for a cooperative group of agents that operate in a
stochastic environment such as multirobot coordination, network traffic
control, `or distributed resource allocation. Solving such problems efiectively
is a major challenge in the area of planning under uncertainty. Our solution is
based on a synthesis of classical heuristic search and decentralized control
theory. Experimental results show that MAA* has significant advantages. We
introduce an anytime variant of MAA* and conclude with a discussion of
promising extensions such as an approach to solving infinite horizon problems.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
A comparison of two lot sizing-sequencing heuristics for the process industry
Production Models;produktie
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