Model-driven development platform selection: four industry case studies

Purpose In today's competitive industries, the selection of best suitable maintenance strategy is dependent on large number of quantitative and qualitative factors, and it becomes an extensively difficult problem for maintenance engineers. Over the years, a diverse range of solution methodologies have been developed for solving this multi-criteria decision-making (MCDM) problem. In this paper, the authors have presented a comprehensive review of latest maintenance strategy paradigms and solution approaches proposed for the selection of an appropriate strategy in various industries. It would provide a systematic mapping of developments in this field and identify some research gaps to explore further studies. Design/methodology/approach A systematic state-of-the-art comprehensive literature review on maintenance strategy paradigms and selection approaches is presented in this study. In this study, 87 research articles published in peer-reviewed journals, since year 2012, are reviewed. Findings For the selection of a suitable maintenance strategy, a variety of criteria are considered to better evaluate the alternatives. In this study, contemporary strategies are discussed, and their applications in different industries are also depicted. Moreover, through the analysis of extant literature, critical criteria are selected and classified in six major categories (namely, economic, technical, safety, environmental, feasibility and social) and further sub-categorized in quantitative and qualitative classes. These clusters of criteria can be helpful as an initial set of criteria for survey and then case- or industry-specific criteria can be shortlisted for further alternative evaluation. Practical implications From the perspective of maintenance managers, maintenance management can be a very difficult task, considering the numerous factors affecting the decision-making process. In order to help in the decision-making process, this study presents the contemporary maintenance strategies in a systematic manner. In a previous study (Kothamasu et al ., 2006), these strategies were classified into repair and prevent classes only. With the developments of autonomous maintenance and design out maintenance (DOM), it was fair to include continuous improvement class. It will help managers and practitioners to identify, according to organization policy, appropriate maintenance strategy alternatives for the asset. A benchmark set of state-of-the-art maintenance strategies are laid out with their applications. The industrial case studies discussed in this study summarizes the optimal maintenance strategies for respective industries. Also, most critical criteria are identified from the existing studies for various industries that can help maintenance practitioners in acknowledging the critical factors and making appropriate decisions. Evaluation parameters for the maintenance strategy selection (MSS) generally conflict with each other, and considering the difficulty of quantifying the qualitative measures, it is a challenging task to determine the optimal trade-off. In order to overcome these challenges, popular MCDM approaches, demonstrating effective results across different industries are discussed with their limitations and applications. Decision-makers can refer this study to identify best suitable decision-making technique for the MSS problem in the industry of their choice. Maintenance managers and engineers can refer the case studies illustrated in Tables 1 and 2 to analyse the MSS techniques proposed by previous studies with industry-specific applications. Social implications This study is an attempt to provide a reference point for research scholars interested in the field of maintenance management and/or development of maintenance strategy framework. This study provides a critical state-of-the-art review of efforts made in the field of MSS. The prominent maintenance strategies being implemented in contemporary industries are discussed with respective case studies. Interested researchers and academicians can familiarize themselves with these strategies and their distinct features in this study. In order to guide future studies and provide a reference point for academicians, MSS critical criteria used in extant literature are identified and classified into a comprehensive benchmark framework. Moreover, the industrial case studies are discussed with the most critical criteria of MSS for different industries and which strategy is most suitable for the respective industries based on these criteria. Table 1 presents different MCDM techniques and their hybrid applications for solving MSS problem that can help researchers in identifying research gaps. Future research can be directed at addressing the limitation of MCDM approach employed in existing studies and comparing the differences in results obtained by the proposed approach. Different industrial case studies with considered maintenance strategy alternatives are presented in Table 2, which can help researchers in identifying the industries that have not been studied yet. Moreover, not all of the existing studies are carried out by considering all the presented benchmark strategies, which can be addressed in future studies by interested researchers. More detailed discussion on research gaps is presented in the following section. Originality/value From the analysis of the extant literature, the authors could observe that the decision-making process adopted in numerous studies was limited to the classical maintenance strategies and not inclusive of aggressive maintenance strategy alternatives. To overcome these limitations and help maintenance managers in the decision-making, this study depicts the contemporary maintenance strategies, critical evaluation criteria and MCDM frameworks (employed to solve the MSS problem with industrial case studies) in a structured manner.

Microservices-based applications consist of loosely coupled, independently deployable services that encapsulate units of functionality. To implement larger application processes, these microservices must communicate and collaborate. Typically, this follows one of two patterns: (1) choreography, in which communication is done via asynchronous message-passing; or (2) orchestration, in which a controller is used to synchronously manage the process flow. Choosing the right pattern requires the resolution of some trade-offs concerning coupling, chattiness, visibility, and design. To address this problem, we propose a decision framework for microservices collaboration patterns that helps solution architects to crystallize their goals, compare the key factors, and then choose a pattern using a weighted scoring mechanism. In cases where there is no clear preference, a hybrid pattern is suggested which inherits some strengths of both choreography and orchestration. We demonstrate the framework by evaluating the needs of three industry case studies (Danske Bank, LGB Bank, Netflix), showing that it leads to appropriate patterns being suggested. We are not aware of any existing decision frameworks to guide solution architects in choosing a microservices collaboration pattern.

In many domains, such as avionics, oil and gas, and maritime, a common practice is to derive and execute test cases manually from requirements, where both requirements and test cases are specified in natural language (NL) by domain experts. The manual execution of test cases is largely dependent on the domain experts who wrote the test cases. The process of manual writing of requirements and test cases introduces ambiguity in their description and, in addition, test cases may not be effective since they may not be derived by systematically applying coverage criteria. In this paper, we report on a systematic approach to support automatic derivation of manually executable test cases from use cases. Both use cases and test cases are specified in restricted NLs along with carefully-defined templates implemented in a tool. We evaluate our approach with four case studies (in total having 30 use cases and 579 steps from flows of events), two of which are industrial case studies from the oil/gas and avionics domains. Results show that our tool was able to correctly process all the case studies and systematically (by following carefully-defined structure coverage criteria) generate 30 TCSs and 389 test cases. Moreover, our approach allows defining different test coverage criteria on requirements other than the one already implemented in our tool.

Evaluating transportation service providers is an applied and multi-criteria decision-making problem. To ensure supply chain sustainability, it is important to consider all sustainable criteria for assessing and evaluating transport service providers. This paper aims to develop a new decision-making framework to evaluate transport service providers considering sustainable criteria from economic, environmental, social and operational aspects. The decision-making framework integrates both qualitative expert opinion and quantitative best-worst method (BWM) and VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method. The developed framework contributes to the academic literature by expanding the knowledge in supply chain sustainability by considering all possible sustainable criteria and integrating both qualitative and quantitative methods for evaluating transport service providers. This study also contributes to practice by developing a decision support tool, by which decision-makers can make an accurate, systematic and prompt decision to identify and assess sustainable criteria and to evaluate the priority of different transport service providers.

Multi-response optimization problems investigation is a crucial element in initiatives designed to enhance quality and overall productivity for manufacturing processes. Since no particular algorithm can be employed for all multi-response problems, defining the method that is utilized as a problem-solving technique is a vital step in the process factors optimization. Identifying a formal procedure of implementing the improvement approach in a multi-criteria decision-making problem is a critical need to ensure the consistency and sustainability of the enhancement methods. In this study, a Plan–Do–Check–Act (PDCA) framework is implemented for a case study in the food industry under which a multi-response optimization problem is investigated. The design of experiment (DOE) is used to examine the effect of process parameters on the quality responses by using the Taguchi method to find the optimal setting for each parameter. An orthogonal array (OA) and signal-to-noise (SNR) ratio is employed to investigate the performance characteristics. Each performance characteristic is then converted into a signal-to-noise ratio, and all the ratios are then fed into a fuzzy model to produce a single comprehensive output measure (COM). The average COM values for various factor levels are calculated, and the level that maximizes the COM value for each factor is identified as the optimal level. Results indicated the effectiveness of the applied method to find the optimal factor levels for the multi-response optimization problem under study. The global optimal factor levels that are driven from the fuzzy logic for the studied parameters are 1250, 40, 7.5, and 1:2, for the speed, frying time, cooking time, and the coating ratio, respectively. Means of all the studied quality characteristics were closer to the target values when compared with the initial factors’ settings.

Supply chain sustainability, which takes environmental, economic, and social factors into account, was recently recognized as a critical component of the supply chain (SC) management evaluation process and known as a multi-criteria decision-making problem (MCDM) that is heavily influenced by the decision-makers. While some criteria can be analyzed numerically, a large number of qualitative criteria require expert review in linguistic terms. This study proposes an integration of Data Envelopment Analysis (DEA), spherical fuzzy analytic hierarchy process (SF-AHP), and spherical fuzzy weighted aggregated sum product assessment (SF-WASPAS) to identify a sustainable supplier for the steel manufacturing industry in Vietnam. In this study, both quantitative and qualitative factors are considered through a comprehensive literature review and expert interviews. The first step employs DEA to validate high-efficiency suppliers based on a variety of quantifiable criteria. The second step evaluates these suppliers further on qualitative criteria, such as economic, environmental, and social factors. The SF-AHP was applied to obtain the criteria’s significance, whereas the SF-WASPAS was adopted to identify sustainable suppliers. The sensitivity analysis and comparative results demonstrate that the decision framework is feasible and robust. The findings of this study can assist steel industry executives in resolving the macrolevel supplier selection problem. Moreover, the proposed method can assist managers in selecting and evaluating suppliers more successfully in other industries.

This paper introduces a novel hybrid framework called Interval-Valued Picture Fuzzy Soft Rough Sets (IVPFSRS) designed to address complex uncertainty in multi-criteria group decision-making (MCGDM) problems. The model achieves a synergistic integration of three powerful mathematical theories: interval-valued picture fuzzy sets (IVPFS) for representing nuanced, interval-valued degrees of membership, neutrality, and non-membership; soft sets for parameterized problem formulation; and rough sets for handling data granularity and approximation under incompleteness. We formally define the IVPFSRS framework, investigate its fundamental properties and algebraic operations, and develop a comprehensive MCGDM algorithm with explicit weight incorporation to address the critical role of criterion importance. The effectiveness and robustness of the proposed approach are demonstrated through a detailed illustrative example of administrative position selection and a systematic comparative analysis with existing models. Results show that the IVPFSRS framework provides a more powerful, flexible, and logically coherent tool for robust decision making in highly uncertain and information-deficient environments. The proposed framework complements recent advancements in cloud-rough integration for large group decision making while offering unique advantages in parameterized three-way uncertainty representation and structured multi-criteria evaluation.

Several environmental policies strive to restore impaired ecosystems and could benefit from a consistent and transparent process-codeveloped with key stakeholders-to prioritize impaired ecosystems for restoration activities. The Clean Water Act, for example, establishes reallocation mechanisms to transfer ecosystem services from sites of disturbance to compensation sites to offset aquatic resource functions that are unavoidably lost through land development. However, planning for the prioritization of compensatory mitigation areas is often hampered by decision-making processes that fall into a myopic decision frame because they are not coproduced with stakeholders. In this study, we partnered with domain experts from the North Carolina Division of Mitigation Services to codevelop a real-world decision framework to prioritize catchments by potential for the development of mitigation projects following principles of a structured decision-making process and knowledge coproduction. Following an iterative decision analysis cycle, domain experts revised foundational components of the decision framework and progressively added complexity and realism as they gained additional insights or more information became available. Through the course of facilitated in-person and remote interactions, the codevelopment of a decision framework produced three main "breakthroughs" from the perspective of the stakeholder group: (a) recognition of the problem as a multiobjective decision driven by several values in addition to biogeophysical goals (e.g., functional uplift, restoring or enhancing lost functionality of ecosystems); (b) that the decision comprises a linked and sequential planning-to-implementation process; and (c) future risk associated with land-use and climate change must be considered. We also present an interactive tool for "on-the-fly" assessment of alternatives and tradeoff analysis, allowing domain experts to quickly test, react to, and revise prioritization strategies. The decision framework described in this study is not limited to the prioritization of compensatory mitigation activities across North Carolina but rather serves as a framework to prioritize a wide range of restoration, conservation, and resource allocation activities in similar environmental contexts across the nation.

Many parties are involved in a construction project, so in deciding a method of implementing the box girder erection many criteria are taken into consideration. Consequently, decision making is not easy for decision makers. A simple, frequently-used, and popular method for decision making is Fuzzy-Analytical Hierarchy Process (F-AHP) which is designed to overcome the problem of multi criteria decision making (MCDM) in the subjective assessment process. This study aims to obtain the correct method of implementing the box girder erection to be used. Based on previous research, the criteria for determining box girder erection method are the criteria for job risk, work quality, implementation time, implementation process, work safety, and work costs. The alternatives compared are the tandem crane method and the launching gantry method. The data in this study were collected through questionnaires distributed to the expert parties involved in Jakarta - Cikampek II Elevated Toll Road Project. F-AHP analysis is based on the weighting of quantitative criteria as a result of normalized calculations and qualitative criteria from the results of the respondents questionnaires which were then made pairwise comparisons assisted by Microsoft Excel software. The results of the analysis show that the occupational safety criteria are the most influential criteria with a weight of 0.454, then followed by job risk criteria (0.169), implementation time (0.119), work quality (0.114), implementation process (0.084), and work costs (0.061). The most appropriate method used in the work of the box girder erection at Jakarta - Cikampek II Elevated Toll Road Project is a launching gantry method with a value of 0.597, while the tandem crane method has a value of 0.403.

Predicting Post-Heart Transplant Composite Renal Outcome Risk in Adults: A Machine Learning Decision Tool

Bottleneck Tree Analysis (BOTA) with green and lean index for process capacity debottlenecking in industrial refineries

One of the significant challenges in operating a mix-model assembly system is the feeding of parts to the productive units. In order to avoid production loss, assembly systems require uninterrupted availability of components to feed workstations. On the other hand, the feeding of assembly components has to be performed in a way that minimises the related costs. In the past, the feeding system most widely used was so-called ‘line storage’ in which the components were stored along the assembly stations in large quantities and were periodically refilled by the central warehouse. Following just-in-time principles, nowadays, assembly system feeding is undertaken by supermarkets, as in decentralised storage areas close to the assembly lines. From such kinds of warehousing, a growing number of manufacturers are adopting two other feeding strategies: the kanban system, which continuously refills the assembly stations through the pull kanban system, or the kitting system, in which kits of components are prepared and delivered following the product through the assembly stations. This paper aims to quantitatively analyse and compare these two recent feeding strategies, considering the production mix variation and the assembled models variety influence. Moreover, kanban-kitting feeding policy and the related optimization issues are considered as hybrid. The findings from an industrial case study and a simulation analysis are also reported. Finally, a decision-making tool that defines a series of ‘convenience areas’ for the different feeding policies is provided.

A documentation framework for architecture decisions

CodeCrawler is an extensible and language independent software visualization tool. It has been validated in several industrial case studies over the past years. CodeCrawler enables the fast exploration of new visualization ideas. It implements and visualizes polymetric views, visualizations of software enriched with information such as software metrics and other source code semantics. It provides a rich set of views that can be customized using a large set of metrics. While CodeCrawler is mainly targeted at visualizing object-oriented software, in its latest implementation it has become a general information visualization tool. 1.

To remain competitive in dynamic environment, enterprises need to make effective and efficient decisions in response to changes. Good modeling tools and analysis support is a necessity in this regard. Such modeling and analysis tools should to be able to visually model and programmatically analyze several descriptive and prescriptive modeling languages in concert. We recount our experience with visual modeling editor and ontological representation for both descriptive and prescriptive models for enterprise decision making. Starting with purposive modeling tools, we shifted to integrated modeling environment where all relevant models of enterprise coexist and are analyzed together. Our ongoing research suggests that apart from integrated modeling environment, scalable modeling facilities for better interaction between modelers and domain experts are also necessary to make modeling and analysis of enterprise models more streamlined.