بررسی شاخص های مدیریت تعمیر و نگهداری ناوگان وسیله نقلیه با به کارگیری DEMATEL و ANP
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|6174||2012||12 صفحه PDF||سفارش دهید||9460 کلمه|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 12, 15 September 2012, Pages 10552–10563
The paper refers to the importance of maintenance management to increase the vehicle fleet energy efficiency. The fleet maintenance management influences as the vehicle maintenance process itself as well as the primary transport process but also their environment. In order to increase fleet energy efficiency by means of a more efficient maintenance management, it is indispensable to observe maintenance process, transport process and the environment. Since the implementation effects of such measures can be measured by different indicators, this paper analyses the influence of indicators in all three mentioned areas on management decision-making. In this sense, appropriate indicators have been defined and subsequently used in fleet maintenance management. To determine levels and intensities of interdependence as well as relative weight of selected indicators two methods have been combined: Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). A model was proposed with indicators’ interdependence whose relative weights were calculated. The proposed model has been implemented in several companies with road vehicle fleets. Collected results show the perceived evaluation by company managers in view of maintenance management process influence onto their fleet energy efficiency. Besides, by proposed model implementation we have obtained equally managers’ evaluation upon effectiveness and efficiency of the maintenance management within studied companies.
Companies with own road vehicle fleets attain profit by performing transport services. The amount of profit is significantly influenced among other things, by the costs incurred by transport and vehicle maintenance processes. Considered companies seek to accomplish all the planned transport tasks while minimising transport and maintenance costs. More efficient fleet maintenance management could affect rational transport process realisation, i.e. reduction in incurred costs. Efficient maintenance management facilitates vehicles of best suited construction–operation (CO) groups in a state “ready for operation” for transport tasks realisation during the required time periods. This certainly influences the increase in energy efficiency of the fleet and transport and maintenance costs reduction, meanwhile company’s core performance is not jeopardised i.e. all planned transport tasks are to be accomplished. In order to attain an efficient maintenance management, it is necessary to coordinate the primary (core) process with maintenance, which has been researched mostly in the field of industrial production (Ashayeri et al., 1996, Brandolese et al., 1996, Nikolopoulos et al., 2003 and Waeyenbergh and Pintelon, 2002). In these papers different models and systems have been proposed, all with the objective of increasing efficiency and productivity of industrial machines. However, vehicle operation and maintenance processes differ from those related to industrial machines. The fact that vehicles are mobile assets, further affected by a large number of external environmental factors imposes the need for a different approach in their maintenance management, compared to other static machines. In this sense, for an efficient fleet maintenance management it is necessary to observe jointly: (1) the transport process as a primary (core) process that brings profit to the company; (2) the vehicle maintenance, as logistical support to the core transport process, which by means of maintenance interventions transforms vehicle condition from the state of “unready for operation” to the state “ready for operation”; (3) the environment, associated to safety and environmental protection from the maintenance impact, which is monitored via technical inspections. For an integrated approach to maintenance management an important concept is the “Process based maintenance” (Zhu, Gelders, & Pintelon, 2002) and (Zhu & Pintelon, 2001). This concept, among other things, involves the definition of necessary indicators which allow measuring the implementation effects of specific measures during maintenance management, monitoring the value of indicators in relation to the adopted thresholds and management decision making in the event of unauthorized indicator values deviation from threshold values. However, in case of several indicators for measuring the implemented measures effects and for management decision making, it is necessary to determine which of the indicators is more significant for achieving a defined objective. Moreover, large number of observed indicators have interdependent impact. Implementing certain measures within management, could improve an indicator value, but impact differently a number of other indicators’ values. The considered issue represents a classic example of Multiple Criteria Decision Making (MCDM). Therefore, the problem under consideration in this paper is to determine the level of interdependences of indicators and determine their significance and their relative weight in the maintenance management causing an increase in the fleet energy efficiency, provided that planned transport tasks are realised. As a solution, a model with ranked indicators upon their impact onto the fleet energy efficiency is obtained. The resulting model should point out to managers which indicators should be given more attention in measuring the implemented measures effects and in maintenance management decision-making. The proposed model can be used to evaluate the managers’ perception of the importance of maintenance management to increase the fleet energy efficiency. Also, managers can be evaluated by means of the model upon their effectiveness and efficiency in the fleet maintenance management. To calculate the level of interdependences and determine the level of significance of indicators in relation to the accomplishment of a defined objective, a combination of two methods DEMATEL and ANP will be used as tools for Multiple Criteria Decision Making (MCDM). DEMATEL method has been developed by “Science and Human Affairs Program of the Battelle Memorial Institute of Geneva” between 1972 and 1976 and used for research and solving several groups of complicated and interdependent problems (Fontela & Gabus, 1974) and (Fontela & Gabus, 1976). This method has been applied in various fields most recently (Li and Tzeng, 2009, Lin et al., 2009, Lin et al., 2011, Tzeng et al., 2007 and Wu and Lee, 2007). As a result, total direct and indirect influences of each factor (indicator) are obtained as each factor’s (indicator) influence given to other factors, but as well influence received from other factors. This interdependence is visually depicted by a Network Relation Map (NRM). ANP method represents a more developed version of Analytic Hierarchy Process (AHP) method. Proposed by Saaty in (Saaty, 1996) and (Saaty & Vargas, 1998), in order to avoid hierarchical constraints that exist in the AHP method (Saaty, 1980). It is a relatively new MCDM method used to calculate the interdependences of factors and determine their relative weights. This method has been applied in many areas (Chung et al., 2005, Coulter and Sarkis, 2005, Leung et al., 2006, Lin et al., 2008, Niemira and Saaty, 2004, Partovi, 2006 and Tesfamariam and Lindberg, 2005). However, treatment of factors’ interdependences in ANP method is not objectively addressed in relation to the actual system. This lack is covered by using the DEMATEL method, where interdependences between groups (sets) of factors are determined more objectively and based on the NRM form a structure of the observed system is created, which is subsequently used to calculate the relative weight of factors by using the ANP method (Yang et al., 2008). Combined use of these two methods has been recently implemented for solving MCDM problems in different fields (Yang and Tzeng, 2011, Wu, 2008 and Lin et al., 2010). In the paper (Lee, Huang, Chang, & Cheng, 2011), the authors go a step further and propose a new hybrid method, which is a developed version of the method compared to the paper by (Ou Yang et al., 2008). According to (Lee et al., 2011), DEMATEL method is used not only as a more objective view of interdependences of groups (sets) of factors, but its total-influence matrix – T is normalised and incorporated into an unweighted supermatrix by the AHP method. A combination of DEMATEL and ANP methods has been used in this research with same approach as in the paper by (Lee et al., 2011). By literature review and based on authors’ personal experience, appropriate indicators were defined in the fleet maintenance management. A model which contains three interdependent groups or fields: transport and maintenance processes and their environment has been developed. In each field there are interdependent factors (indicators). Based on a conducted research of perceptions of field-related professors and other relevant experts from the Faculty of Transport and Traffic engineering in Belgrade, the interdependences among indicators, as well as interdependences between observed fields have been established. Afterwards, the relative weights of indicators and of each observed field are calculated within the developed model using the above mentioned methods. By surveys of managers in several transport companies and by the proposed model implementation, an evaluation of perception on maintenance management impact on the enhancement of the fleet energy efficiency was made, as well as evaluation of managers’ efficiency in the maintenance management within each studied company. In the following Section 2, the concept of fleet maintenance management has been presented in detail, together with a description of selected indicators. Section 3 describes the DEMATEL and ANP methods. Based on survey results, relative weights of indicators and observed fields were obtained in the developed model. In the Section 4 the results of proposed model implementation in several companies with road fleets were addressed. In Section 5 the results were thoroughly analysed, while in the last section the main conclusions and future research topics were drawn u
نتیجه گیری انگلیسی
This paper analyses the impact of interdependent indicators used to measure the implementation effects of specific measures within the maintenance management in order to increase the vehicle fleet energy efficiency. Since an efficient fleet maintenance management needs to be observed altogether with the core (transport) process and its environment, it is essential to evaluate all these indicators in these specific fields. Upon literature review and our expert deliberation we have selected nine provisional indicators for evaluation. A combination of DEMATEL and ANP methods was used for determining the level of interdependence of indicators and to calculate their relative weights. A model was obtained with the relative weights of maintenance management indicators with the primary objective to increase fleet energy efficiency. Some conclusions drawn from the developed model could be that the indicators are interdependent in three observed groups/fields, with varied levels of interdependence and no apparent trade-off. The indicator of Maintenance Plan Realisation (M3) has an utmost importance for the efficient fleet maintenance management. If all maintenance work orders were realised at the planned timeframe according to the maintenance plan (MP), it will almost always facilitate the most appropriate vehicle to the transport task during the required time periods as required by the Operation Plan (OP), which will reduce transport and maintenance costs. Improving the value of this indicator is largely affected by the improvement in value of other interdependent indicators. The second in rank of significance for the maintenance management is the Operational Plan Realisation Percentage (T1) while the third in ranking is for the Vehicle Payload Utilisation (T2) indicator. Other significant indicators are Mean Vehicle Downtime (M2), Vehicle Fleet Utilisation Rate (T3), Percentage of Fleet Roadworthiness (E1), Percentage of Vehicle Roadworthiness in Accidents (E2), Mean Time Between Failures (M1), and Planned Maintenance Percentage (M4). The proposed model with the relative weights of indicators has been implemented for the evaluation of managers in several companies with road vehicle fleets in the Republic of Serbia. Executives at “Delmax” Ltd. have achieved the best overall score in terms of developed awareness of the importance of maintenance management to increase vehicle fleet energy efficiency (S’), as well as the best evaluation of the maintenance management efficiency (S″) compared to managers in other companies. Managers have achieved higher scores regarding the importance of maintenance management to attain an increase in fleet energy efficiency (S’) than the scores in terms of actual efficient maintenance management in their companies (S″). Based on overall evaluation of managers in terms of efficient maintenance management, it is concluded that in the Public Utility Companies, as PUC “Sanitation,” GSP “Belgrade” PUC “Belgrade water supply and sewerage” there is a significant potential to improve a value of indicators with more important relative weights in the developed model, which will help them manage their maintenance to become more effective and efficient.