In this paper we address the integration of two-level supply chain with multiple items, production facility and retailers’ demand over a considered discrete time horizon. This two-level production distribution system features capacitated production facility supplying several retailers located in the same region. If production does take place, this process incurs a fixed setup cost as well as unit production costs. In addition, deliveries are made from the plant to the retailers by a limited number of capacitated vehicles and routing costs are incurred. This work aims at implementing a solution to minimize the sum of the costs at the production facility and the retailers. The methodology adopted to tackle this issue is based on a hybrid heuristic, greedy and genetic algorithms that uses strong formulation to provide a good solution of a guaranteed quality that are as good or better than those provided by the MIP optimizer with a considerably larger run time. The results demonstrate that the proposed heuristics are effective and performs impressively in terms of computational efficiency and solution quality.

In this paper, we address the integration of a two-level supply chain with multiple items. This two-level production-distribution system features a capacitated production facility supplying several retailers located in the same region. If production does occur, this process incurs a fixed setup cost and unit production costs. Besides, deliveries are made from the plant to the retailers by a limited number of capacitated vehicles, routing costs incurred. This work aims to implement a minimization solution that reduces the total costs in both the production facility and retailers. The methodology adopted based on a hybrid heuristic, greedy and genetic algorithm uses strong formulation to provide a suitable solution of a guaranteed quality that is as good or better than those provided by the MIP optimizer. The results demonstrate that the proposed heuristics are effective and performs impressively in terms of computational efficiency and solution quality.

Nowadays, the real life constraints necessitatescontrolling modern machines using human interventionby means of sensorial organs. The voice is one of the hu-man senses that can control/monitor modern interfaces.In this context, Automatic Speech Recognition is princi-pally used to convert natural voice into computer text aswell as to perform an action based on the instructionsgiven by the human. In this paper, we propose a generalframework for Arabic speech recognition that uses LongShort-Term Memory (LSTM) and Neural Network (Multi-Layer Perceptron: MLP) classifier to cope with the non-uniform sequence length of the speech utterances issuedfrom both feature extraction techniques, (1) Mel FrequencyCepstral Coefficients MFCC (static and dynamic features),(2) the Filter Banks (FB) coefficients. The neural architec-ture can recognize the isolated Arabic speech via classifi-cation technique. The proposed system involves, first, ex-tracting pertinent features from the natural speech signalusing MFCC (static and dynamic features) and FB. Next,the extracted features are padded in order to deal with thenon-uniformity of the sequences length. Then, a deep ar-chitecture represented by a recurrent LSTM or GRU (GatedRecurrent Unit) architectures are used to encode the se-quences of MFCC/FB features as a fixed size vector that willbe introduced to a Multi-Layer Perceptron network (MLP)to perform the classification (recognition). The proposedsystem is assessed using two different databases, the firstone concerns the spoken digit recognition where a com-parison with other related works in the literature is per-formed, whereas the second one contains the spoken TVcommands. The obtained results show the superiority ofthe proposed approach.

Nowadays, the real life constraints necessitatescontrolling modern machines using human interventionby means of sensorial organs. The voice is one of the hu-man senses that can control/monitor modern interfaces.In this context, Automatic Speech Recognition is princi-pally used to convert natural voice into computer text aswell as to perform an action based on the instructionsgiven by the human. In this paper, we propose a generalframework for Arabic speech recognition that uses LongShort-Term Memory (LSTM) and Neural Network (Multi-Layer Perceptron: MLP) classifier to cope with the non-uniform sequence length of the speech utterances issuedfrom both feature extraction techniques, (1) Mel FrequencyCepstral Coefficients MFCC (static and dynamic features),(2) the Filter Banks (FB) coefficients. The neural architec-ture can recognize the isolated Arabic speech via classifi-cation technique. The proposed system involves, first, ex-tracting pertinent features from the natural speech signalusing MFCC (static and dynamic features) and FB. Next,the extracted features are padded in order to deal with thenon-uniformity of the sequences length. Then, a deep ar-chitecture represented by a recurrent LSTM or GRU (GatedRecurrent Unit) architectures are used to encode the se-quences of MFCC/FB features as a fixed size vector that willbe introduced to a Multi-Layer Perceptron network (MLP)to perform the classification (recognition). The proposedsystem is assessed using two different databases, the firstone concerns the spoken digit recognition where a com-parison with other related works in the literature is per-formed, whereas the second one contains the spoken TVcommands. The obtained results show the superiority ofthe proposed approach.