Scheduling shared continuous resources on many-cores

This is a post-peer-review, pre-copyedit version of an article published in Journal of Scheduling. The final authenticated version is available online at: https://doi.org/10.1007/s10951-017-0518-0© 2017 Springer Science+Business Media New York We consider the problem of scheduling a number of jobs on m identical processors sharing a continuously divisible resource. Each job j comes with a resource requirement [InlineEquation not available: see fulltext.]. The job can be processed at full speed if granted its full resource requirement. If receiving only an x-portion of (Formula presented.), it is processed at an x-fraction of the full speed. Our goal is to find a resource assignment that minimizes the makespan (i.e., the latest completion time). Variants of such problems, relating the resource assignment of jobs to their processing speeds, have been studied under the term discrete–continuous scheduling. Known results are either very pessimistic or heuristic in nature. In this article, we suggest and analyze a slightly simplified model. It focuses on the assignment of shared continuous resources to the processors. The job assignment to processors and the ordering of the jobs have already been fixed. It is shown that, even for unit size jobs, finding an optimal solution is NP-hard if the number of processors is part of the input. Positive results for unit size jobs include a polynomial-time algorithm for any constant number of processors. Since the running time is infeasible for practical purposes, we also provide more efficient algorithm variants: an optimal algorithm for two processors and a [InlineEquation not available: see fulltext.] -approximation algorithm for m processors

Fast truck-packing of 3D boxes

We present formulation and heuristic solution of a container packing problem observed in a household equipment factory’s sales and logistics department. The main feature of the presented MIP model is combining several types of constraints following from the considered application field. The developed best-fit heuristic is tested on the basis of a computational experiment. The obtained results show that the heuristic is capable of constructing good solutions in a very short time. Moreover, the approach allows easy adjustment to additional loading constraints

Handling the description noise using an attribute value ontology

The quality of any classifier depends on a number of factors, including the quality of training data. In real-world scenarios, data are often noisy. One reason for noisy data (erroneous values) is in the representation language, insufficient to model different levels of knowledge granularity. In this paper, to address the problem of such description noise, we propose a novel extension of the na've Bayesian classifier by an attribute value ontology (AVO). In the proposed approach, every attribute is a hierarchy of concepts from the domain knowledge base. In this way an example is described either very precisely (using a concept from the low-level of the hierarchy) or, when it is not possible, in a more general way (using a concept from higher levels of the hierarchy). Our general strategy is to classify a new example using training examples described in the same way or more precisely at lower levels of knowledge granularity. Hence, the hierarchy introduces a bias which in effect can contribute to improvement of a classification