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This paper aims to propose a novel matrix-based systematic approach for vulnerability assessment.

The proposed method consists of two major steps. First, the power network is modeled as a topological combination of edges (transmission lines, transformers, etc.) and nodes (buses, substations, etc.). The second step is to use an axiomatic design-based index for topology analysis. This index is based on the systematic counting of possible routes from the start (generators) to destination (loads), considering load importance, before and after a disruption.

The effectiveness of the proposed method is demonstrated through an illustrative example and the Institute of Electrical and Electronics Engineers (IEEE) 14-bus power system. It was shown that the load’s importance influences the results of the vulnerability analysis. The proposed method has some advantages over traditional graph theory such as an explicit description of multiple transmission nodes and assets with multiple conversion processes. Furthermore, it would help the power grid operators and asset investment managers to be better to assess the vulnerable components.

The proposed method can be used in planning, optimization, robustness and hardening of power systems.

The paper presents a matrix-based systematic approach to evaluate and quantify the vulnerability of the power grid’s components.

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

The purpose of this paper is to investigate the knowledge gaps in the extant literature on the role of ambidexterity and coopetition in designing resilient fashion supply chains (RFSCs), and to develop a contextual framework for effective decision-making to enable practitioners to enhance their supply chain resilience.

The study adopts a novel multi-evidence-based approach comprising Denyer and Tranfield's (2009) systematic literature review (SLR) with context, intervention, mechanism and outcome (CIMO) logic, text mining and network analysis. The approach constitutes a rigorous methodology that cross-validates results and ensures the reliability and validity of findings.

The authors identified key knowledge gaps in the literature and explored the main contribution categories (e.g. conceptual understandings, operational impacts, use of theories and frameworks). Subsequently, we developed a contextual framework of ambidextrous coopetition to design RFSCs. Finally, an empirical research agenda is proposed with the five research directions to address the gap and take forward the notion of ambidextrous coopetition and RFSCs.

The multi-evidence-based approach is a structured and triangulated SLR approach and thus lacks empirical study.

This research proposes a contextual framework of ambidextrous coopetition that can be used by fashion companies to embed resilience into their structures and operations. This research also presents an agenda for the future empirical research.

This paper contributes by providing a combinatory synthesis on the role of ambidexterity and coopetition in designing RFSCs. This paper introduces a novel methodological triangulation for improving the quality and validity of SLRs. It identifies significant knowledge gaps and defines directions for future research.

The purpose of this paper is to investigate whether income smoothing does indeed improve the informativeness of stock prices about firms' future earnings and cash flows. Also an approach to studying the effects of income smoothing is presented.

This study uses data from 1992‐2006 and runs regressions on each of the 560 industry‐year cross‐sections. The data compiled from the financial statements of firms were collected for each year available from the Tehran Stock Exchange database. Income smoothing is defined as the management of accruals to reduce time‐series variation in income, and uses a cross‐sectional version of the Jones model, modified by Kothari, Leone and Wasley. Smoothing is measured as the variation of net income relative to the variation in CFO, or the correlation between changes in accruals and changes in CFO. Informativeness is measured as the coefficient on future earnings (cash flows) in a regression of current stock return against current and future earnings (cash flows and accruals).

The findings suggest that income smoothing enhances the information content of the effect of stock price on future earnings, thus improving the ability of market participants to make informed decisions about the allocation of capital resources.

Although previous research on the subject of income smoothing in an emerging market has been documented, its effect on stock prices efficiency is largely unknown. Thus, this paper presents an approach to studying the effects of income smoothing and the knowledge that the ability to manage earnings could improve stock prices efficiency could be useful for academics and policymakers in this market.

COVID-19’s attack on the system of education left the learning of many students in limbo. For minimizing this effect, the current study sought to probe how Mobile-Assisted Language Learning (MALL) can facilitate English as a Foreign Language (EFL) learners’ vocabulary knowledge. In so doing, a quasi-experimental pretest-posttest research design was adopted to check the effect of a newly developed application (i.e. visual vocabulary learning, hereafter VVL) in an EFL classroom.

To complying with the objective, a number of intermediate students (N = 40) were considered to participate in the study. They were assigned into a visual learning group (VLG) and a nonvisual learning group (NVLG) comprising 20 students in each. The VLG was directed to learn vocabulary by a VVL, and the NVG, a conventional method was employed for vocabulary instruction. An independent samples t-test was run to screen the efficacy of the intervention.

The finding revealed that participants in the VLG outperformed significantly in the posttest. The finding is beneficial for EFL teachers, syllabus designers, and material developers to take advantage of mobile applications in teaching vocabulary.

This study was conducted during COVID 19 between two groups in Iran.

Spurred by the high turnover in the pharmaceutical industry, locating pharmacies inside urban areas along with the high product perishability in this industry, the pharmaceutical supply chain management has recently gained increasing attention. Accordingly, this paper unveils an inventory-routing problem for designing a pharmaceutical supply chain with perishable products and time-dependent travel time in an uncertain environment.

In this study, mathematical programming is employed to formulate a multi-graph network affected by the traffic volume in order to adapt to real-world situations. Likewise, by transforming the travel speed function to the travel time function using a step-by-step algorithm, the first-in-first-out property is warranted. Moreover, the Box–Jenkins forecasting method is employed to diminish the demand uncertainty.

An appealing result is that the delivery horizon constraint in the under-study multi-graph network may eventuate in selecting a longer path. Our analysis also indicates that the customers located in the busy places in the city are not predominantly visited in the initial and last delivery horizon, which are the rush times. Moreover, it is concluded that integrating disruption management, routing planning and inventory management in the studied network leads to a reduction of costs in the long term.

Applying the time-dependent travel time with a heterogeneous fleet of vehicles on the multi-graph network, considering perishability in the products for reducing inventory costs, considering multiple trips of transfer fleet, considering disruption impacts on supply chain components and utilizing the Box–Jenkins method to reduce uncertainty are the contributions of the present study.

Human developments in constructing buildings and their subsequent infrastructures despite certain advantages have unfortunately considerable effects on the environment, such that construction could be named as the main exploiter of the natural, physical and biologic resources. Hence, the purpose of this paper is to evaluate the effect of communication capabilities of the suppliers and external green integration on the green performance and financial performance of engineering and construction companies.

The statistical population of the study comprises all 300 members of the Building Engineering System of South Khorasan Province. According to the Morgan table (1972), 167 participants were selected as the sample of the study using the stratified random sampling, and a standard questionnaire was distributed among them. Data were collected through library and filed methods. Data were analyzed using the factor analysis method (SPSS), and LISREL Software was used for evaluating the research hypotheses.

The obtained results indicated that information sharing has a significant effect on environmental coordination and the green balance of green cost reduction of coordinate competitive advantage.

Accordingly, given the results obtained from a developmental-practical research, the authors initially gathered and classified the required data on the construction industry using the library and field methods, then extracted the obstacles of the supply chain to help facilitate the implementation of efficiency in the so-called industry.

The purpose of this study is to investigate resource-constrained multi-project scheduling problems (RCMPSP) involving uncertainty in the form of time-dependent renewable resource reliability. A key focus is to minimize the makespan (completion time) of projects when resources can become unavailable or fail over time at non-constant rates. Accounting for realistic resource reliability seeks to provide scheduling solutions that better reflect potential delays in practical multi-project environments.

A new discrete-time binary integer programming formulation of RCMPSP is expanded to include time-dependent resource reliability and simultaneously evaluate the time-dependent failure rate and constant repair rate of a resource. A new hybrid immune genetic algorithm with local search (HIGALS) is developed to solve this NP-hard problem. HIGALS incorporates a new coding mechanism, initialization method and local search operator.

A case study tests the proposed HIGALS approach. The validity of the mathematical model is confirmed by solving small-sized problems with GAMS software. The proposed HIGALS algorithm is validated by solving small-sized problems and comparing its solutions with GAMS. The superiority of HIGALS is demonstrated by comparing its solutions with six basic algorithms on medium- and large-sized problems. Results show that HIGALS outperforms existing algorithms, achieving an average reduction in makespan of over 11.79%, while maintaining the advantages of genetic, immune and local search algorithms and avoiding their disadvantages.

Considering time-dependent resource reliability can help project managers plan for disruptions and delays in resource-critical projects. HIGALS provides decision support for robust multi-project scheduling.

This study contributes to the field by investigating RCMPSP with time-dependent renewable resource reliability, which reflects real-world uncertainty more accurately. HIGALS presents a novel approach to balance intensification and diversification for this challenging problem.