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This study presents a hybrid meta-modeling algorithm for optimum carbon dioxide (CO₂) emission estimation. It is composed of artificial neural network (ANN), fuzzy linear regression (FLR), and conventional regression (CR). Different FLR models are considered to cover the latest algorithms and viewpoints. ANN with different training algorithms and transfer functions is also applied to data sets. The proposed hybrid algorithms uses analysis of variance (ANOVA), and mean absolute percentage error (MAPE) to select between ANN, FLR or conventional regression for future CO₂ emission estimation. The intelligent algorithm of this study is then applied to estimate CO₂ emission in seven countries including India, Canada, Brazil, France, Japan, United Kingdom and United States. Different models are selected as preferred model for annual CO₂ emission estimation in these countries. The preferred model for India, Brazil, United Kingdom and United States is selected as FLR whereas the preferred model for CO₂ emission estimation in Japan, Canada and France is ANN. This shows how adopting the proposed hybrid algorithm could help in selecting the preferred model between FLR, ANN and CR in order to cover possible noise, complexity and ambiguity. This is the first study that utilizes a hybrid algorithm based on ANN, FLR and CR for accurate and optimum long term CO₂ emission estimation.

This study aims to identify the relationship between different maintenance practices and productive efficiency in continuous process productive plants as well as the moderating effect of good training practices.

The empirical data were drawn from a database containing 609 observations of 29 productive units. Scales were validated using the Q-sort method. The panel data technique was used as the analysis methodology, with the inclusion of fixed effects for each productive plant.

Maintenance practices can effectively contribute to increasing the overall equipment effectiveness (OEE) of firms. Application of predictive maintenance practices should be considered as the primary training tool.

This study used a secondary database, limiting the research design and data manipulation.

The article provides practitioners with an analysis of maintenance practices by category (predictive, preventive and corrective), and the impact of each practice on the OEE of continuous process productive plants. Moreover, it explores the importance of training for extracting more results from maintenance practices.

Companies are investing in new technologies, but it is also essential to invest in training people. There is a demand for Industry 4.0 through the introduction of upskilling and reskilling programs.

This study used practice-based view (PBV) theory to explain how maintenance practices help firms achieve greater OEE. Furthermore, it introduced training practice as a moderating variable in the relationship between maintenance practices and OEE.

The purpose of this paper is to promote a system dynamics-discrete event simulation (SD-DES) hybrid modelling framework, one that is useful for investigating problems comprising multifaceted elements which interact and evolve over time, such as is found in TPM.

The hybrid modelling framework commences with system observation using field notes which culminate in model conceptualization to structure the problem. Thereafter, an SD-DEShybrid model is designed for the system, and simulated to proffer improvement programmes. The hybrid model emphasises the interactions between key constructs relating to the system, feedback structures and process flow concepts that are the hallmarks of many problems in production. The modelling framework is applied to the TPM operations of a bottling plant where sub-optimal TPM performance was affecting throughput performance.

Simulation results for the case study show that intangible human factors such as worker motivation do not significantly affect TPM performance. What is most critical is ensuring full compliance to routine and scheduled maintenance tasks and coordinating the latter to align with rate of machine defect creation.

The framework was developed with completeness, generality and reuse in view. It remains to be applied to a wide variety of TPM and non-TPM-related problems.

The developed hybrid model is scalable and can fit into an existing discrete event simulation model of a production system. The case study findings indicate where TPM managers should focus their efforts.

The investigation of TPM using SD-DES hybrid modelling is a novelty.

To investigate safety at truck drivers' work outside the cab with a special focus on work environment and equipment maintenance to prevent occupational accidents.

Two statistical databases on Finnish occupational accidents for truck drivers are analysed. Firstly, the national accident statistics database is examined to understand the broad context, and secondly a database on fatal accidents is analysed to better understand the root causes of the accidents and their relationship to human factors in maintenance. Based on the root cause analysis, four accident scenarios are created, and risk management measures are prioritized by occupational safety and health specialists (n = 7).

The study shows that there are a variety of accidents in truck drivers' work. Most of the accidents occur outside the cab while performing tasks other than driving. Further, in-depth analysis of the fatal accidents increases understanding of the possibilities of different risk management and maintenance actions in preventing such accidents.

Databases contain different limitations concerning the data.

Truck drivers' work environments are wide in nature. Efficient safety management requires broad participation from different stakeholders. In addition to safe work activities, work environment and equipment maintenance is highlighted as a key component for safe and fluent delivery transportation.

Road transportation forms a backbone of modern society. Accidents affect the efficiency of transportation and cause manifold costs reaching all the way to the societal level.

This study adds an important dimension of delivery transportation to a current scientific discussion on human factors and maintenance.

Pumped-storage hydroelectricity (PSH) is considered as an effective method to moderate the difference in demand and supply of electricity. This study aims to understanding of the high capacity of energy production, storage and permanent exploitation has been the prominent feature of pumped-storage hydroelectricity.

In this paper, the optimization of energy production and maintenance costs in one of the large Iranian PSH has been discussed. Hence, a mathematical model mixed integer nonlinear programming developed in this area. Minimizing the difference in supply and demand in the energy production network to multiple energies has been exploited to optimal attainment scheme. To evaluate the model, exact solution CPLEX and to solve the proposed programming model, the efficient metaheuristics are utilized by the tuned parameters achieved from the Taguchi approach. Further analysis of the parameters of the problem is conducted to verify the model behavior in various test problems.

The results of this paper have shown that the meta-heuristic algorithm has been done in a suitable time, despite the approximation of the optimal answer, and the consequences of research indicate that the model proposed in the studied power plant is applicable.

In pumped-storage hydroelectricity plants, one of the main challenges in energy production issues is the development of production, maintenance and repair scheduling concepts that improves plant efficiency. To evaluate the mathematical model presented, exact solution CPLEX and to solve the proposed bi-objective mixed-integer linear programming model, set of efficient metaheuristics are used. Therefore, according to the level of optimization performed in the case study, it has caused the improvement of planning by 7%–12% and effective optimization processes.

This paper proposes a new simultaneous optimization model of the industrial systems design and maintenance. This model aims to help the designer in searching for technical solutions and the product architecture by integrating the maintenance issues from the design stage. The goal is to reduce the life-cycle cost (LCC) of the studied system.

Literature indicates that the different approaches used in the design for maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and the maintainability of a multicomponent system as well as the modeling of the dynamic maintenance. This article proposes to go further in the optimization of the product, by simultaneously characterizing the design, in terms of reliability and maintainability, as well as the dynamic planning of the maintenance operations. This combinatorial characterization is performed by a two-level hybrid algorithm based on the genetic algorithms.

The proposed tool offers, depending on the life-cycle expectation, the desired availability, the desired business model (sales or rental), simulations in terms of the LCCs, and so an optimal product architecture.

In this article, the term “design” is limited to reliability properties, possible redundancies, component accessibility (maintainability), and levels of monitoring information.

This work is distinguished by the use of a hybrid optimization algorithm (two-level computation) using genetic algorithms. The first level is to identify an optimal design configuration that takes into account the LCC criterion. The second level consists in proposing a dynamic and optimal maintenance plan based on the maintenance-free operating period (MFOP) concept that takes into account certain criteria, such as replacement costs or the reliability of the system.

Asset-intensive organizations rely heavily on physical assets that are often expensive, complex and have a significant impact on organizational performance. Past introductions of critical assets in various industries showed that despite many preparations in maintenance and operations, shortcomings were identified after deployment resulting in unreliable performance. The main purpose of this qualitative study is to explore the factors that determine how asset-intensive organizations can achieve reliable outcomes in critical asset introductions despite random failures as a result of increasing complexity and infant mortalities.

To gain a detailed understanding of the issues and challenges of critical asset introductions, a case study in railways (rolling stock introductions) was conducted and analyzed using qualitative analysis.

The case showed that organizational factors were perceived as decisive factors for a reliable performance of the introduction, while the main focus of the introduction was on the asset and its technical systems. This suggests that more consideration toward organizational factors is needed. Therefore, a critical asset introduction framework was proposed based on 15 identified factors.

Reliable performance is often associated with technical systems only. This empirical study emphasizes the need for a more holistic perspective and the inclusion of organizational factors when introducing critical assets seeking reliable performance. This study demonstrated the application of the affinity diagramming technique in collectively analyzing the data adopting a multidisciplinary orientation.

The purpose of this paper is to analyze issues pertaining to maintenance performance and to develop a framework that addresses challenges of maintenance management of an international lube blending plant in Malaysia. This study capitalizes on the contribution of selected maintenance department stakeholders from within the plant to develop “tailor-made” intervention strategies for maintenance performance improvement.

The study employed two focus group workshops to ascertain issues facing the maintenance department and identify intervention strategies. The final phase of the study employed fuzzy Delphi method (FDM) to prioritize and rank the intervention strategies for performance improvement.

The first focus group workshop identified 106 issues which could be classified under aspects of spare parts (n=8), equipment (n=14), communication (n=12), non-technical resource (n=8), health, safety and environment (n=4), technical skills and recruitment (n=27), and handling and procedures (n=33). Based on these findings, the second focus group revealed 28 significant performance initiatives to improve the issues identified for maintenance performance improvement. Through the FDM, 18 performance initiatives were ranked and prioritized. Performance improvement through leadership category leads the overall initiatives followed by equipment maintenance management, talent management, work environment and vendor management.

Interesting implications for maintenance management theory would be realized if future research were able to demonstrate that certain aspects or dimensions were related to high performing plant maintenance, and not with low performing ones. Apparently, the present study is not able to provide this clue because it is merely a case study of a single company.

As ILBP attempts to implement maintenance performance improvement, it is pertinent for the management to understand the relevant performance issues and concerns. The appropriate enablers have been identified and must be initiated to chart the strategic role of their maintenance organization.

This study contributes toward further understanding of the maintenance performance management. It has demonstrated the need of organizations to make infrastructural investments such as quality leadership, employee training and empowerment, to name a few, to strategically enhance their maintenance capabilities.

This study uses the FDM for the decision-making process of improving plant maintenance performance. It adds value to the body of knowledge of plant operation management.

This paper aims to present a dual-perspective framework for maintenance service delivery that should be used by manufacturing companies to structure and manage their maintenance service delivery process, using aggregated historical and real-time data to improve operational decision-making. The framework, built for continuous improvement, allows the exploitation of maintenance data to improve the knowledge of service processes and machines.

The Dual-perspective, data-based decision-making process for maintenance delivery (D3M) framework development and test followed a qualitative approach based on literature reviews and semi-structured interviews. The pool of companies interviewed was expanded from the development to the test stage to increase its applicability and present additional perspectives.

The interviews confirmed that manufacturing companies are interested in exploiting the data generated in the use phase to improve operational decision-making in maintenance service delivery. Feedback to improve the framework methods and tools was collected, as well as suggestions for the introduction of new ones according to the companies' necessities.

The paper presents a novel framework addressing the data-based decision-making process for maintenance service delivery. The D3M framework can be used by manufacturing companies to structure their maintenance service delivery process and improve their knowledge of machines and service processes.

Energy and environmental concerns have gained a significant role in public policy agenda as well as in energy economics literature. As policies often rely on imprecise information on data and goals, fuzzy goal programming (FGP) modeling is a relevant choice to evaluate multi-criteria sustainability. This technique is suitable for the analysis of the Europe 2020 strategy plan dealing with several possibly conflicting objectives in economy, environment, energy and employment. The paper aims to discuss these issues.

The paper presents a FGP model for sustainable implementations for all European Union (EU) countries with respect to Europe 2020 policy goals and provides insights for decision makers to better satisfy conflicting criteria by suggesting optimal allocations of workers in several economic sectors.

The analysis shows an overall great performance of European Union countries in the environmental and social criteria and outlines the needs for significant additional policy measures to reduce energy consumption while increasing the economic output. Comparing the performance of countries within the European Union between those who adopted the euro and those who maintained national currencies shows that Euro countries tend to perform worse in terms of Europe 2020 sustainability, opening opportunities for further research to better investigate on the causes and determinants of these differences.

The paper presents a conceptual model of sustainable development that improves understanding of the concept and reconciles highly competing policy objectives in a common framework. Applying this model to all European Union countries offers both comparison and policy recommendations at a large new scale.