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Stochastic programming model for production planning with stochastic aggregate demand and spreadsheet-based solution heuristics

Saadouli Nasreddine

Operations Research and Decisions

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2021

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Vol. 31, No. 4

117--127

EN

By discretising the stochastic demand, a deterministic nonlinear programming formulation is developed. Then, a hybrid simulation-optimisation heuristic that capitalises on the nature of the problem is designed. The outcome is an evaluation problem that is efficiently solved using a spreadsheet model. The main contribution of the paper is providing production managers with a tractable formulation of the production planning problem in a stochastic environment and an efficient solution scheme. A key benefit of this approach is that it provides quick near-optimal solutions without requiring in-depth knowledge or significant investments in optimisation techniques and software.

2

Object recognition algorithm for mobile devices

Kozik R., Marchewka A

Image Processing & Communications

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2013

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Vol. 18, no. 1

31--36

EN

In this paper an object recognition algorithm for mobile devices is presented. The algorithm is based on a hierarchical approach for visual information coding proposed by Riesenhuber and Poggio [1] and later extended by Serre et al. [2]. The proposed method adapts an efficient algorithm to extract the information about local gradients. This allows the algorithm to approximate the behaviour of simple cells layer of Riesenhuber and Poggio model. Moreover, it is also demonstrated that the proposed algorithm can be successfully deployed on a low-cost Android smartphone.

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Efficient Graph Kernels for Textual Entailment Recognition

Zanzotto F. M., Dell'Arciprete L., Moschitti A.

Fundamenta Informaticae

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2011

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Vol. 107, nr 2/3

199-222

EN

One of the most important research area in Natural Language Processing concerns the modeling of semantics expressed in text. Since foundational work in Natural Language Understanding has shown that a deep semantic approach is still not feasible, current research is focused on shallow methods combining linguistic models and machine learning techniques. The latter aim at learning semantic models, like those that can detect the entailment between the meaning of two text fragments, by means of training examples described by specific features. These are rather difficult to design since there is no linguistic model that can effectively encode the lexico-syntactic level of a sentence and its corresponding semantic models. Thus, the adopted solution consists in exhaustively describing training examples by means of all possible combinations of sentence words and syntactic information. The latter, typically expressed as parse trees of text fragments, is often encoded in the learning process using graph algorithms. In this paper, we propose a class of graphs, the tripartite directed acyclic graphs (tDAGs), which can be efficiently used to design algorithms for graph kernels for semantic natural language tasks involving sentence pairs. These model the matching between two pairs of syntactic trees in terms of all possible graph fragments. Interestingly, since tDAGs encode the association between identical or similar words (i.e. variables), it can be used to represent and learn first-order rules, i.e. rules describable by first-order logic. We prove that our matching function is a valid kernel and we empirically show that, although its evaluation is still exponential in the worst case, it is extremely efficient and more accurate than the previously proposed kernels.