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The purpose of this paper is to evaluate and analyze lube oil performance. The plant experience shows that the oil, even after completion of a number of hours of recommended operation, has some residual life. It is desirable to maximize its use to conserve this scarce resource but avoid failures. At present, continuation or change of the oil is decided, based on the manufacturer's recommendation and experience in the plant. The suggested oil change period is conservative and results in non‐efficient usage of oil. This practice needs refinement to include all possible properties/attributes of oil and use of appropriate procedure to assess its realistic performance.

Parameter profile approach (PPA) is applied to evaluate and analyze the lubricating oil performance parameters.

Physical and chemical properties related to degradation of the lube oil are considered as lubricating oil performance parameters. The value of these performance parameters from time to time, obtained by analyzing samples drawn from the system, are analyzed through PPA. In this approach, mean, median, performance parameter index and time performance index are defined and evaluated at different time intervals. The suggested procedure is illustrated by means of an example.

The suggested procedure will be helpful for the maintenance personnel in planned oil replacement, and in identifying the weak oil properties in respect of the identified lube oil performance parameters.

The purpose of this paper is to study the regions of parameter space of engineering design in which performance is sensitive to design parameters. Some of these parameters (for example, the dimensions and compositions of components) constitute the design, but others are intrinsic properties of materials or Nature. The paper is concerned with narrow regions of parameter space, “cliffs”, in which performance (some measure of the final state of a system, such as ignition or nonignition of a flammable gas, or failure or nonfailure of a ductile material subject to tension) is a sensitive function of the parameters. In these regions, performance is also sensitive to uncertainties in the parameters. This is particularly important for intrinsically indeterminate systems, those whose performance is not predictable from measured initial conditions and is not reproducible.

We develop models of ignition of a flammable mixture and of failure in plastic flow under tension. We identify and quantify cliffs in performance as functions of the design parameters. These cliffs are characterized by large partial derivatives of performance parameters with respect to the design parameters and with respect to the uncertainties in the model. We calculate and quantify the consequences of small random variations in the parameters of indeterminate systems.

We find two qualitatively different classes of performance cliffs. In one class, performance is a sensitive function of the parameters in a narrow range that separates wider ranges in which it is insensitive. In the other class, the final state is not defined for parameter values outside some range, and performance is a sensitive function of the parameters as they approach their limiting values. We find that sensitivity of performance to control (design) parameters implies that it is also sensitive to other parameters, some of which may not be known, and to uncertainties of the initial state that are not under the control of the designer. Near or on a cliff performance is degraded. It is also less predictable and less reproducible.

Frequently, design optimization or cost minimization leads to choices of engineering design parameters near cliffs. The sensitivity of performance to uncertainty that we find in those regimes implies that caution and extensive empirical experience are required to assure reliable functioning. Because cliffs are defined as behavior on the threshold of failure, this is a reflection of the trade-off between optimization and margin of safety, and implies the importance of ensuring that margins and uncertainties are quantified. The implications extend far beyond the model systems we consider to engineering systems in general.

Many of these considerations have been part of the informal culture of engineering design, but they were not formalized until the methodology of “Quantification of Margins and Uncertainty” was developed in recent years. Although this methodology has been widely used and discussed, it has only been published in a small number of reports (cited here), and never in a journal article or book. This paper may be its first formal publication, and also its first quantitative application to and illustration with explicit model problems.

This study aimed to design a user-participatory methodology to investigate the post-occupancy sustainability of reused historical buildings and to apply it to a case study.

This study was designed in four stages. In the first stage, the sustainability parameters and sub-parameters were determined in the reused historical buildings based on the literature. The second stage included a field study in which the current situation of the study area was analysed, and the users were reached using the survey technique. In the third stage, the data obtained from the user participation were analysed with importance performance analysis (IPA) and an IPA matrix was created. The fourth stage included an evaluation of the results of the analysis and the development of recommendations.

IPA is a supportive method for ensuring the sustainable use of historic buildings. According to the data obtained from the IPA, it was seen that the functional sustainability of the building was achieved to a great extent. At the same time, there were deficiencies in technical and environmental sustainability. In terms of aesthetic sustainability, it was observed that the importance and performance values given by the users were generally consistent with each other.

The originality of this study is that the performance of the reused historical buildings in the process of use was monitored with appropriate parameters, and a user-participated method was proposed that allows improvement suggestions to be developed in line with the results obtained.

This paper aims to identify and classify the parameters that construct the input, processes, output, productivity and outcome variables that affect performance. These parameters are used in the evaluation model to measure research performance in universities so that they can be used as the basis for making leadership policies both at the national and institutional levels.

The design of this research is a quantitative research method using a survey questionnaire that was sent to the heads of research institutions at universities in Indonesia. To obtain these parameters, a test for determining the value of the loading factor was used.

The authors found that input variable parameters consisted of 10 parameters; process variable consisted of 22 parameters; output variable parameters consisted of 8 parameters; productivity variable consisted of 4 parameters; and outcome variable parameters consisted of 10 parameters.

One approach to obtain parameters is through systems theory, where every element that makes up the organization contributes to the achievement of goals. This study attempted to develop parameters in the performance appraisal model of systems theory-based research institutions that are adapted to trends in the direction of research in universities. These parameters are based on aspects of input, process, output, productivity and outcome.

The purpose of this paper is to develop a qualitative framework for the selection of the most appropriate optimization algorithm for the multi-objective trade-off problem (MOTP) in construction projects based on the predefined performance parameters.

A total of 6 optimization algorithms and 13 performance parameters were identified through literature review. The experts were asked to indicate their preferences between each pair of optimization algorithms and performance parameters. A multi-criteria decision-making tool, namely, consistent fuzzy preference relation was applied to analyze the responses of the experts. The results from the analysis were applied to evaluate their relative weights which were used to provide a ranking to the algorithms.

This study provided a qualitative framework which can be used to identify the most appropriate optimization algorithm for the MOTP beforehand. The outcome suggested that non-dominated sorting genetic algorithm (NSGA) was the most appropriate algorithm whereas linear programming was found to be the least appropriate for MOTPs.

The devised framework may provide a useful insight for the construction practitioners to choose an effective optimization algorithm tool for preparing an efficient project schedule aiming toward the desired optimal improvement in achieving the various objectives. Identification of the absolute best optimization algorithm is very difficult to attain due to various problems such as the inherent complexities and intricacies of the algorithm and different class of problems. However, the devised framework offers a primary insight into the selection of the most appropriate alternative among the available algorithms.

The purpose of this study is to examine how access and mobilisation of network resources influence a firm's performance. It has been established that alliance portfolio (AP) network parameters shape the access to network resources; however, resource access understood as value creation differs from resource mobilisation understood as value capture. Hence, the paper contributes towards the comprehension of AP performance by examining the extent to which a firm's level of network resource mobilisation (NRM) plays a role in improving financial performance and how this strategy conditions the benefits obtained from a firm's AP.

This study employs an interorganisational network approach to describe the APs of firms; subsequently, it examines how AP network parameters and resource mobilisation determine financial performance. To this end, sequential multiple regression models are applied to a sample from the Top International Airlines database, covering 135 portfolios that correspond to 1117 codeshare partnerships.

The analyses show that the NRM level has an inverted U-shaped relationship with revenue performance, thereby revealing the limitations and considerations in the strategic alliance strategy. In addition, the authors show how the resource mobilisation decision moderates the faculty of AP parameters to influence a firm's financial performance, thereby exposing the nuanced relationship between AP size, diversity and redundancy. The findings convey strategic and practical implications for managers regarding how to capture value from their APs.

The findings suggest the need for NRM to form part of a firm's AP management capability, so that, by acquiring superior strategic knowledge in NRM, the firm is able to extract value from its AP through the optimal exploitation of complementary assets.

Previous research has highlighted the multidimensional nature of APs at the theoretical level; however, no simultaneous empirical analysis of various AP parameters has yet been produced. The research empirically analyses an AP network and how its parameters affect financial performance in the presence of a resource mobilisation strategy. Not only do the authors introduce the analysis of the curvilinear relationship between the level of NRM and a firm's performance, but also of its role in advancing the AP literature.

Purpose — To develop methodologies to evaluate search engines according to an individual's preference in an easy and reliable manner, and to formulate user-oriented metrics to compare freshness and duplication in search results.

Design/methodology/approach — A personalised evaluation model for comparing search engines is designed as a hierarchy of weighted parameters. These commonly found search engine features and performance measures are given quantitative and qualitative ratings by an individual user. Furthermore, three performance measurement metrics are formulated and presented as histograms for visual inspection. A methodology is introduced to quantitatively compare and recognise the different histogram patterns within the context of search engine performance.

Findings — Precision and recall are the fundamental measures used in many search engine evaluations due to their simplicity, fairness and reliability. Most recent evaluation models are user oriented and focus on relevance issues. Identifiable statistical patterns are found in performance measures of search engines.

Research limitations/implications — The specific parameters used in the evaluation model could be further refined. A larger scale user study would confirm the validity and usefulness of the model. The three performance measures presented give a reasonably informative overview of the characteristics of a search engine. However, additional performance parameters and their resulting statistical patterns would make the methodology more valuable to the users.

Practical implications — The easy-to-use personalised search engine evaluation model can be tailored to an individual's preference and needs simply by changing the weights and modifying the features considered. A user is able to get an idea of the characteristics of a search engine quickly using the quantitative measure of histogram patterns that represent the search performance metrics introduced.

Originality/value — The presented work is considered original as one of the first search engine evaluation models that can be personalised. This enables a Web searcher to choose an appropriate search engine for his/her needs and hence finding the right information in the shortest time with the least effort.

This research presents a study on the supply chain process of an Indian apparel industry considering various parameters involved. The study aims to identify the main parameters to improve the supply chain process and develop a comprehensive structural relationship to rank them to streamline the apparel supply chain process and business environment.

The team of five experts from this apparel industry was made to give scores to multiple parameters. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) technique is used to develop the model for eleven key parameters and then rank them.

Based on the data analysis the planning, customer and warehouse storage have emerged as top three key parameters while the non-replenishment approach, push and pull strategy and manufacturing of the product are identified as the bottom three parameters from a hierarchy level. These parameters have been ranked based on their contributing attributes in this apparel supply chain process.

The study provides an overall ranking of parameters and the implications are in the direction of helping the industry to improve its supply chain performances rather than focus only on productivity. Further, the key parameters are identified as critical inputs and show that the firms are being more proactive and well prepared comprised of the industry.

The study indicates that the key parameters are identified by this apparel brand to improve its supply chain process. The key supply chain process involves planning, manufacturing, distribution, end customer and returns logistics of the goods, etc. So, this research also provides the focused parameters on the supply chain performance received by end customer from the supplier and rank them for effectiveness and improve their overall organizational performance. It also provides a critical observation of their supply chain process improvement which includes different brand uses, strategies and approaches.

As newer high performance polymers in mechanical properties become available for material extrusion-based additive manufacturing, determining infill parameter settings becomes more important to achieve both operational and mechanical performance of printed outputs. For the material extrusion of carbon fiber reinforced poly-ether-ether-ketone (CFR-PEEK), this study aims not only to identify the effects of infill parameters on both operational and mechanical performance but also to derive appropriate infill settings through a multicriteria decision-making process considering the conflicting effects.

A full-factorial experimental design to investigate the effects of two major infill parameters (i.e. infill pattern and density) on each performance measure (i.e. printing time, sample mass, energy consumption and maximum tensile load) is separately performed to derive the best infill settings for each measure. Focusing on energy consumption for operational performance and maximum tensile load for mechanical performance, the technique for order preference by similarity to ideal solution is further used to identify the most appropriate infill settings given relative preferences on the conflicting performance measures.

The results show that the honeycomb pattern type with 25% density is consistently identified as the best for the operational performance measures, while the triangular pattern with 100% density is the best for the mechanical performance measure. Moreover, it is suggested that certain ranges of preference weights on operational and mechanical performance can guide the best parameter settings for the overall material extrusion performance of CFR-PEEK.

The findings from this study can help practitioners selectively decide on infill parameters by considering both operational and mechanical aspects and their possible trade-offs.

The purpose of this study is to investigate the best fleet for a new purchase based on multi-objective optimization on the basis of ratio (MOORA), reference point and multi-MOORA methods. This study further identifies critical parameters for fleet performance monitoring and exploring optimum range of critical parameters using Monte Carlo simulation. At the end of this study, fleet maintenance management and operations have been discussed in the perspectives of risk management.

Fleet categories and fleet performance monitoring parameters have been identified using the literature survey and Delphi method. Further, real-time data has been analyzed using MOORA, reference point and multi-MOORA methods. Taguchi and full factorial design of experiment (DOE) are used to investigate critical parameters for fleet performance monitoring.

Fleet performance monitoring is done based on fuel consumption (FC), CO₂ emission (CE), coolant temperature (CT), fleet rating, revenue generation (RG), fleet utilization, total weight and ambient temperature. MOORA, reference point and multi-MOORA methods suggested the common best alternative for a particular category of the fleet (compact, hatchback and sedan). FC and RG are the critical parameters for monitoring the fleet performance.

The geographical aspects have not been considered for this study.

A pilot run of 300 fleets shows saving of Rs. 2,611,013/- (US$36,264.065), which comprises total maintenance cost [Rs. 1,749,033/- (US$24,292.125)] and FC cost [Rs. 861,980/- (US$11,971.94)] annually.

Reduction in CE (4.83%) creates a positive impact on human health. The reduction in the breakdown maintenance of fleet improves the reliability of fleet services.

This study investigates the most useful parameters for fleet management are FC, CE, CT. Taguchi DOE and full factorial DOE have identified FC and RG as a most critical parameters for fleet health/performance monitoring.