Using a group support system to aid input-output identification in DEA

In data envelopment analysis (DEA), operating units are compared on their outputs relative to their inputs. The identification of an appropriate input-output set is of decisive significance if assessment of the relative performance of the units is not to be biased. This paper reports on a novel approach used for identifying a suitable input-output set for assessing central administrative services at universities. A computer-supported group support system was used with an advisory board to enable the analysts to extract information pertaining to the boundaries of the unit of assessment and the corresponding input-output variables. The approach provides for a more comprehensive and less inhibited discussion of input-output variables to inform the DEA model. © 2005 Operational Research Society Ltd. All rights reserved.

Optimal input/output reduction in production processes

While conventional Data Envelopment Analysis (DEA) models set targets for each operational unit, this paper considers the problem of input/output reduction in a centralized decision making environment. The purpose of this paper is to develop an approach to input/output reduction problem that typically occurs in organizations with a centralized decision-making environment. This paper shows that DEA can make an important contribution to this problem and discusses how DEA-based model can be used to determine an optimal input/output reduction plan. An application in banking sector with limitation in IT investment shows the usefulness of the proposed method.

Glutamatergic input-output properties of thalamic astrocytes

Astrocytes in the somatosensory ventrobasal (VB) thalamus of rats respond to glutamatergic synaptic input with metabotropic glutamate receptor (mGluR) mediated intracellular calcium ([Ca²?](i)) elevations. Astrocytes in the VB thalamus also release the gliotransmitter (GT) glutamate in a Ca²?-dependent manner. The tripartite synapse hypothesis posits that astrocytic [Ca²?](i) elevations resulting from synaptic input releases gliotransmitters that then feedback to modify the synapse. Understanding the dynamics of this process and the conditions under which it occurs are therefore important steps in elucidating the potential roles and impact of GT release in particular brain activities. In this study, we investigated the relationship between VB thalamus afferent synaptic input and astrocytic glutamate release by recording N-methyl-D-aspartate (NMDA) receptor-mediated slow inward currents (SICs) elicited in neighboring neurons. We found that Lemniscal or cortical afferent stimulation, which can elicit astrocytic [Ca²?](i) elevations, do not typically result in the generation of SICs in thalamocortical (TC) neurons. Rather, we find that the spontaneous emergence of SICs is largely resistant to acute afferent input. The frequency of SICs, however, is correlated to long-lasting afferent activity. In contrast to short-term stimulus-evoked GT release effects reported in other brain areas, astrocytes in the VB thalamus do not express a straightforward input-output relationship for SIC generation but exhibit integrative characteristics.

Input/output deterioration in production processes

In many real applications of Data Envelopment Analysis (DEA), the decision makers have to deteriorate some inputs and some outputs. This could be because of limitation of funds available. This paper proposes a new DEA-based approach to determine highest possible reduction in the concern input variables and lowest possible deterioration in the concern output variables without reducing the efficiency in any DMU. A numerical example is used to illustrate the problem. An application in banking sector with limitation of IT investment shows the usefulness of the proposed method. © 2010 Elsevier Ltd. All rights reserved.

Novel input-output prediction approach for biomass pyrolysis

Biomass pyrolysis to bio-oil is one of the promising sustainable fuels. In this work, relation between biomass feedstock element characteristic and pyrolysis process outputs was explored. The element characteristics considered in this study include moisture, ash, fix carbon, volatile matter, carbon, hydrogen, nitrogen, oxygen, and sulphur. A semi-batch fixed bed reactor was used for biomass pyrolysis with heating rate of 30 °C/min from room temperature to 600 °C and the reactor was held at 600 °C for 1 h before cooling down. Constant nitrogen flow rate of 5 L/min was provided for anaerobic condition. Rice husk, Sago biomass and Napier grass were used in the study to form different element characteristic of feedstock by altering mixing ratio. Comparison between each element characteristic to total produced bio-oil yield, aqueous phase bio-oil yield, organic phase bio-oil yield, higher heating value of organic phase bio-oil, and organic bio-oil compounds was conducted. The results demonstrate that process performance is associated with feedstock properties, which can be used as a platform to access the process feedstock element acceptance range to estimate the process outputs. Ultimately, this work evaluated the element acceptance range for proposed biomass pyrolysis technology to integrate alternative biomass species feedstock based on element characteristic to enhance the flexibility of feedstock selection.

The use of expert manpower in health and safety in France

The thesis examines the system of occupational health and safety in France. It analyses the use of expert manpower in the field with a view to establishing the possibility of a profession in health and safety. An input-output model is developed to bring together the necessary elements of prevention of accidents and occupational diseases. The role of institutions concerned with health and safety is analysed with reference to this model. The research establishes the need for a health and safety specialist role. The recognition and status of this role are found to be subject to other criteria including the acceptance by institutions of such a specialist role. The model is also used to define the role of this specialist as expected by the various institutions intervening in the field.

A mathematical model for dynamic efficiency using data envelopment analysis

In this paper we propose a data envelopment analysis (DEA) based method for assessing the comparative efficiencies of units operating production processes where input-output levels are inter-temporally dependent. One cause of inter-temporal dependence between input and output levels is capital stock which influences output levels over many production periods. Such units cannot be assessed by traditional or 'static' DEA which assumes input-output correspondences are contemporaneous in the sense that the output levels observed in a time period are the product solely of the input levels observed during that same period. The method developed in the paper overcomes the problem of inter-temporal input-output dependence by using input-output 'paths' mapped out by operating units over time as the basis of assessing them. As an application we compare the results of the dynamic and static model for a set of UK universities. The paper is suggested that dynamic model capture the efficiency better than static model. © 2003 Elsevier Inc. All rights reserved.

Data envelopment analysis model for the appraisal and relative performance evaluation of nurses at an intensive care unit

The appraisal and relative performance evaluation of nurses are very important and beneficial for both nurses and employers in an era of clinical governance, increased accountability and high standards of health care services. They enhance and consolidate the knowledge and practical skills of nurses by identification of training and career development plans as well as improvement in health care quality services, increase in job satisfaction and use of cost-effective resources. In this paper, a data envelopment analysis (DEA) model is proposed for the appraisal and relative performance evaluation of nurses. The model is validated on thirty-two nurses working at an Intensive Care Unit (ICU) at one of the most recognized hospitals in Lebanon. The DEA was able to classify nurses into efficient and inefficient ones. The set of efficient nurses was used to establish an internal best practice benchmark to project career development plans for improving the performance of other inefficient nurses. The DEA result confirmed the ranking of some nurses and highlighted injustice in other cases that were produced by the currently practiced appraisal system. Further, the DEA model is shown to be an effective talent management and motivational tool as it can provide clear managerial plans related to promoting, training and development activities from the perspective of nurses, hence increasing their satisfaction, motivation and acceptance of appraisal results. Due to such features, the model is currently being considered for implementation at ICU. Finally, the ratio of the number DEA units to the number of input/output measures is revisited with new suggested values on its upper and lower limits depending on the type of DEA models and the desired number of efficient units from a managerial perspective.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Probability distribution modelling to improve stability in nonlinear MIMO control

We consider the direct adaptive inverse control of nonlinear multivariable systems with different delays between every input-output pair. In direct adaptive inverse control, the inverse mapping is learned from examples of input-output pairs. This makes the obtained controller sub optimal, since the network may have to learn the response of the plant over a larger operational range than necessary. Moreover, in certain applications, the control problem can be redundant, implying that the inverse problem is ill posed. In this paper we propose a new algorithm which allows estimating and exploiting uncertainty in nonlinear multivariable control systems. This approach allows us to model strongly non-Gaussian distribution of control signals as well as processes with hysteresis. The proposed algorithm circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider.

Feasibility studies of an on-line identification technique

This thesis is concerned with the study of a non-sequential identification technique, so that it may be applied to the identification of process plant mathematical models from process measurements with the greatest degree of accuracy and reliability. In order to study the accuracy of the technique under differing conditions, simple mathematical models were set up on a parallel hybrid. computer and these models identified from input/output measurements by a small on-line digital computer. Initially, the simulated models were identified on-line. However, this method of operation was found not suitable for a thorough study of the technique due to equipment limitations. Further analysis was carried out in a large off-line computer using data generated by the small on-line computer. Hence identification was not strictly on-line. Results of the work have shovm that the identification technique may be successfully applied in practice. An optimum sampling period is suggested, together with noise level limitations for maximum accuracy. A description of a double-effect evaporator is included in this thesis. It is proposed that the next stage in the work will be the identification of a mathematical model of this evaporator using the teclmique described.