27,073 research outputs found
Multi crteria decision making and its applications : a literature review
This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM
Decision support for build-to-order supply chain management through multiobjective optimization
This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF
Spatial-temporal data modelling and processing for personalised decision support
The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less
Keywords
Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio
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Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
Spatial-temporal data modelling and processing for personalised decision support
The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less
Keywords
Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio
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The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems
Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three âpillarsâ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a âboomerangâ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems
Information technology and performance management for build-to-order supply chains
En las siguientes lĂneas se plantea un artĂculo de reflexiĂłn que tiene en cuenta parte del marco teĂłrico que sustenta la investigaciĂłn titulada âPrĂĄcticas pedagĂłgicas que promueven la competencia argumentativa escrita (CAE) en niños campesinos de los grados
4° y 5° del Centro Educativo Municipal La Caldera, Sede Principal de Pastoâ, desarrollada en el año 2012. En Ă©l se contemplan los aportes de las ciencias del lenguaje y la comunicaciĂłn, la teorĂa de la argumentaciĂłn, la didĂĄctica de la lengua escrita y los gĂ©neros discursivos, que dan cuenta de la necesidad de desarrollar la
capacidad crĂtica en los estudiantes a travĂ©s de la argumentaciĂłn, lo cual implica transformar las prĂĄcticas pedagĂłgicas para que se alejen de la transmisiĂłn de conocimientos y den paso a la comunicaciĂłn, para que la palabra escrita sea apropiada de manera significativa
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Information technology and performance management for build-to-order supply chains
Life cycle assessment of mechanical recycling of post-consumer polyethylene flexible films based on a real case in Spain
Mechanical recycling of plastic waste is a common practice in industry and is an environmental solution to the problem of plastics disposal. In this article, a case study of mechanical recycling of post-consumer polyethylene flexible films in Granada (Spain) was analyzed from an environmental point of view by the Life-Cycle Assessment methodology. The industrial process is divided into four large areas of operation: sorting, washing, extrusion and wastewater treatment. The results show that the washing area has the largest environmental impacts, mostly due to the electricity consumption, followed by sorting. Also, the overall mechanical recycling process causes damage, mainly, on human health, which dominates over ecosystems and resources with 93.4% of the total impact of the process. Two different scenarios have also been considered for the generated waste, and they critically affect the overall environmental performance of the entire process. The first scenario considers the impacts of the landfill disposal of the humid organic matter generated and the losses of PE. In this scenario, all the CH4 resulting from the anaerobic degradation of organic matter was emitted into the atmosphere. In this case, human health impact was high. In the second end-of-life scenario, all the CH4 generated would be captured and burned in a gas turbine for energy generation. Lower impacts were found in human health and ecosystems categories, as well as the total value, in the second scenario.This work has received funds from the European Unionâ LIFE Programme, under Grant Agreement LIFE17ENV/ES/000229. Funding for open access charge: Universidad de Granada / CBUA
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