Forecasting and maintenance problem under subcontracting constraint with transportation delay

In this paper, a forecasting production/maintenance optimization problem has been proposed with a random demand and single machine M₁ on a finite horizon. The function rate of the machine M₁ is depending on the production rate for each period of the forecasting horizon. In order to satisfy the customer, a subcontracting assures the rest of the production through machine M₂ with transportation delay. An analytic formulation of the problem has been proposed using a sequential computation of the optimal production plan for which an optimal preventive maintenance policy has been calculated based on minimal repair. Firstly, we find, the optimal production plans of principal and subcontracting machines, which minimises the total production and inventory cost for the cases without and with returned products under service level and subcontracting transportation delay. Secondly, we determine a joint effective maintenance policy with the optimal production plan, which integrates the various constraints for the production rates, the transportation delay and the returned production deadline. Numerical results are presented to highlight the application of the developed approach and sensitivity analysis shows the robustness of the model.     

Modelling and solution approaches for the interconnected city logistics

Achieving a sustainable delivery of goods in urban areas has become a challenging task for service providers and logistics managers. Under this context, the physical internet (PI) Manifesto offers through its emergent concept of interconnected city logistics (ICL) a solution toward a more sustainable transportation of PI containers within cities. In this article, we explore the operational urban transportation problem of PI containers under ICL considerations. For this variant, built on the multiplicity of urban logistics centres and their interconnection, a comprehensive modelling approach is proposed to include key features such as multiple time periods, multi-zone urban coverage, heterogeneous fleets, multi-trip and multi-hub pickups, and delivery constraints. In order to deal with solvability issues encountered with realistic instances of the problem, a heuristic solution approach is developed. This is done with the objective to come up with solutions offering the best trade-offs between economic and ecological attributes within a short computational time. To validate the approach, a realistic set of instances is built with data inspired from city freight movements in an urban area in France. Using these experiments, the solvability of the model and the performance of our heuristic approach are discussed and managerial insights are derived.     

Prioritized assertional-based removed sets revision of <Emphasis Type="Italic">DL</Emphasis>-<Emphasis Type="Italic">Lite</Emphasis> belief bases

In real world applications, information is often provided by multiple sources having different priority levels reflecting for instance their reliability. This paper investigates ”Prioritized Removed Sets Revision” (PRSR) for revising stratified DL-Lite knowledge bases when a new sure piece of information, called the input, is added. The strategy of revision is based on inconsistency minimization and consists in determining smallest subsets of assertions (prioritized removed sets) that should be dropped from the current stratified knowledge base in order to restore consistency and accept the input. We consider different forms of input: A membership assertion, a positive or a negative inclusion axiom. To characterize our revision approach, we first rephrase Hansson’s postulates for belief bases revision within a DL-Lite setting, we then give logical properties of PRSR operators. In some situations, the revision process leads to several possible revised knowledge bases where defining a selection function is required to keep results within DL-Lite fragment. The last part of the paper shows how to use the notion of hitting set in order to compute the PRSR outcome. We also study the complexity of PRSR operators, and show that, in some cases, the computational complexity of the result can be performed in polynomial time.     

Toward intrusion detection using belief decision trees for big data

Big data refers to datasets that we cannot manage with standard tools and within which lie valuable information previously hidden. New data mining techniques are needed to deal with the increasing size of such data, their complex structure as well as their veracity which is on covering questions of data imperfection and uncertainty. Even though big data veracity is often overlooked, it is very challenging and important for an accurate and reliable mining and knowledge discovery. This paper proposes MapReduce-based belief decision trees for big data as classifiers of uncertain large-scale datasets. The proposed averaging and conjunctive classification approaches are experimented for intrusion detection on KDD’99 massive intrusion dataset. Several granularity attacks’ levels have been considered depending on whether dealing with whole kind of attacks, or grouping them in categories or focusing on distinguishing normal and abnormal connections.     

A combined support vector machine-FCGS classification based on the wavelet transform for Helitrons recognition in C.elegans

Multimedia Tools and Applications - The Helitrons, an important sub-class of the transposable elements (TEs) class II, have been revealed in diverse eukaryotic genomes. They are mobile elements...     

A novel replication technique to attenuate churn effects

Dealing with the churn problem is an important requirement in distributed systems in general and more precisely in structured peer-to-peer overlay networks due to their dynamicity. This problem refers to the change in the set of participating nodes due to the frequent joins, leaves or failures. Such variations induce at worst the loss of objects and at least performance degradation. This is due to the reorganization of the set of replicas of the affected objects. Till now, this problem has been mostly addressed at the p2p routing level to ensure the reach ability of peers by maintaining the consistency of the logical neighbors. However, the main challenge for structured p2p systems is to protect the ability of the system to locate any present object. At the storage level, avoiding data losses is still an issue when a reconfiguration of the participant peers occurs. In this paper and after presenting first the existing replication techniques, we propose the zone replication technique used in both Chord and Kademlia protocols in order to attenuate churn effects. Finally, we point out some ways that can lead to better and maybe robust replication protocols.     

Enhancement of noise-suppressed speech by spectral processing implemented in a digital signal processor

This paper presents a noisy suppressed speech enhancement method by combining the basic spectral subtraction technique and spectral processing in the frequency domain to provide better noise suppression as well as better enhancement in the speech regions. In contrast to several previous approaches we do not try to achieve a complete removal of the noise, but instead our goal is to preserve a pre-defined amount of the original noise in the processed signal. This is accomplished by exploiting the masking properties of the human auditory system. The proposed algorithm is named PM “Proposed Method” which simulates properties of the human auditory system and applies it to the speech recognition system to enhance its robustness. The performance of the speech enhancement algorithm using the proposed masking model was compared with three other speech enhancement methods over 4 different noise types and five SNRs. The performances of the proposed approach are objectively and subjectively compared to the conventional approaches to highlight the aforementioned improvement. In this paper we discuss the design and development of a digital signal processor (DSP) implementation to achieve real-time performance of our filter. The target processor is a Texas Instruments TMS320C6713 floating point DSP.