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A stochastic technique for solving interval non-linear problems using generalized Hukuhara (GH)-difference is proposed. The non-linear programming problem in interval form is transformed into an equivalent non-linear programming problem with real coefficients by associating a Gaussian random variable to the interval and the six-sigma rule. The conceptualized idea eliminates the decision maker’s instinctive selection of weight functions and provides an alternative to the order relation method, max-min criteria-based methods and bi-level approaches for representing intervals as real numbers. To demonstrate a coherent understanding, numerical examples have been used.

A stochastic approach has been used to develop a solution technique for solving interval nonlinear programming problems which arise in the modeling of scientific and engineering problems under uncertain environments.

The proposed idea eliminates the decision maker’s instinctive selection of weight functions and provides an alternative to the order relation method, max-min criteria-based methods and bi-level approaches for representing intervals as real numbers. This method provides specific results rather than in the interval form, which are more practical and implementable by the decision maker.

This is to certify, that the research paper submitted is an outcome of original work. I have duly acknowledged all the sources from which the ideas and extracts have been taken. This article has not been submitted elsewhere for publication.

To provide a new semi-analytical solution for the nonlinear Benjamin–Bona–Mahony (BBM) equation in the form of a convergent series. The results obtained through HPTM for BBM are compared with those obtained using the Sine-Gordon Expansion Method (SGEM) and the exact solution. We consider the initial condition as uncertain, represented in terms of an interval then investigate the solution of the interval Benjamin–Bona–Mahony (iBBM).

We employ the Homotopy Perturbation Transform Method (HPTM) to derive the series solution for the BBM equation. Furthermore, the iBBM equation is solved using HPTM to the initial condition has been considered as an interval number as the coefficient of it depends on several parameters and provides lower and upper interval solutions for iBBM.

The obtained numerical results provide accurate solutions, as demonstrated in the figures. The numerical results are evaluated to the precise solutions and found to be in good agreement. Further, the initial condition has been considered as an interval number as the coefficient of it depends on several parameters. To enhance the clarity, we depict our solutions using 3D graphics and interval solution plots generated using MATLAB.

A new semi-analytical convergent series-type solution has been found for nonlinear BBM and interval BBM equations with the help of the semi-analytical technique HPTM.

The aim of this paper is to evaluate the intensity and success of the digitalization process, by measuring the efficiency of the use of information and communication technologies in business in the Republic of Serbia (RS) in the period from 2006 to 2019.

The data envelopment analysis method is applied and due to the sensitivity of the results to measurement errors, the robustness analysis of the obtained values of average efficiency is performed, using the bootstrapping method.

The results show an intensive, expansive and relatively efficient process of digital business transformation in the RS. The results indicate inefficient use of software packages, While the efficiency of e-commerce in companies in most years is over 80%.

The research is limited to the RS, so the conclusions cannot be generalized in a broader context.

The biggest problem in the implementation of digital business transformation in the RS is the understanding of management and employees in organizations that digital business transformation will take place only if software solutions are purchased and installed, with less attention paid to their proper application and low use of their maximum capabilities.

Digital transformation measurement is the subject of a very small number of studies. Through a review of the literature, the authors of this paper do not find the use of data envelopment analysis to measure the efficiency of digital business transformation in the way they present it in this paper.

This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R² = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.

This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R² = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.

The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R²” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.

The authors confirm the originality of this paper.

The main purpose of this study is to present a new approach to managing process changes in uncertain conditions. The proposed approach is applied in one of the largest production companies in Turkey to manage the changes in their warehouse processes which formed after the merger.

In the model, interval-valued hesitant fuzzy the decision-making trial and evaluation laboratory (IVHF-DEMATEL) methodology is integrated into one of the most used BPR tools, change matrix. The main focus of the proposed model is to increase both flexibility and applicability in uncertain conditions. Thus, while the change matrix enables companies to be agile and responsive to changes, IVHF-DEMATEL provides a better way to continuously evaluate and determine critical processes, and strategies to align with evolving conditions.

Initial analysis revealed two major problems, the slowness of shipments caused by the increase in costs and the confusion in the organizational structure. However, the conventional methods fall short of effectively determination of critical objectives in terms of dealing with uncertainty. Therefore, a comprehensive roadmap for managing the change is developed with the integration of IVHF-DEMATEL and change matrix so that a successful transition is achieved.

It is believed that the study will contribute to the existing literature by providing a novel approach in which the IVHF-DEMATEL methodology is integrated into the change matrix. Also, the study provides a guideline for practical applications by presenting a step-by-step implementation of the model.

Cybersecurity has received growing attention from academic researchers and industry practitioners as a strategy to accelerate performance gains and social sustainability. Meanwhile, firms are usually prone to cyber-risks that emanate from their supply chain partners especially third-party logistics providers (3PLs). Thus, it is crucial to implement cyber-risks management in 3PLs to achieve social sustainability in supply chains. However, these 3PLs are faced with critical difficulties which tend to hamper the consistent growth of cybersecurity. This paper aims to analyze these critical difficulties.

Data were sourced from 40 managers in Nigerian 3PLs with the aid of questionnaires. A novel quantitative methodology based on the synergetic combination of interval-valued neutrosophic analytic hierarchy process (IVN-AHP) and multi-objective optimization on the basis of a ratio analysis plus the full multiplicative form (MULTIMOORA) is applied. Sensitivity analysis and comparative analysis with other decision models were conducted.

Barriers were identified from published literature, finalized using experts’ inputs and classified under organizational, institutional and human (cultural values) dimensions. The results highlight the most critical dimension as human followed by organizational and institutional. Also, the results pinpointed indigenous beliefs (e.g. cyber-crime spiritualism), poor humane orientation, unavailable specific tools for managing cyber-risks and skilled workforce shortage as the most critical barriers that show the highest potential to elicit other barriers.

By illustrating the most significant barriers, this study will assist policy makers and industry practitioners in developing strategies in a coordinated and sequential manner to overcome these barriers and thus, achieve socially sustainable supply chains.

This research pioneers the use of IVN-AHP-MULTIMOORA to analyze cyber-risks management barriers in 3PLs for supply chain social sustainability in a developing nation.

Most construction projects are undertaken with the incentive of some form of profit which in business attracts disputes that key disputing parties intend to resolve through mechanisms such as arbitration as early as possible so that they may advance with other activities associated with the project. However, this intention is regularly unfulfilled, disappointing the parties with late arbitration resolution. This study, therefore, explored the duration influencing factors (DIFs) facing arbitration practice in the Tanzania construction industry.

A concurrent convergence mixed methods approach was used where a total of 12 DIFs were identified from the literature, and data were then collected from 39 construction arbitrators, 8 semi-structured interviews and 4 documentary reviews. Descriptive and inferential statistics were employed for quantitative data and directed content analysis for the qualitative data.

Results show that contrary to most people’s convictions, delays in arbitration are regularly caused by the disputing parties rather than the arbitrating party or the authority. The study identified cooperation amongst the tribunal parties, poor accuracy and submission of documents and material evidences, late payment of arbitration fees, and skills, experience, reputation and profession of the arbitrator to be the most critical factors that cause late arbitration resolution.

The use of mixed methods concurrent triangulation convergence approach provides a unique contribution to knowledge by highlighting how the efficacy of arbitration in time performance could be further developed through understanding the critical factors that drive the proceedings duration in the Tanzania construction industry.

Supplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.

To deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.

While assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.

This integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.

We examine the relationship between macroeconomic news and fund price jumps, using high-frequency 5-min intraday data for Exchange Traded Funds (ETFs) and Listed Open-end Funds (LOFs) from 2019 to 2020.

We utilize the non-parametric jump test known as the LM method to detect fund price jumps. In addition, we perform Logistic regression to analyze the relationship between macroeconomic news and fund price jumps. Moreover, we use multiple linear regression to explore the relationship between fund price jumps and subsequent returns.

The probability of price jumps increases by 22.56% when macroeconomic news is released. Moreover, the returns associated with news-driven price jumps display a reversal pattern, and there is an asymmetric relationship in subsequent returns following macroeconomic shocks. Specifically, funds tend to exhibit lower returns after news-driven price jumps compared to those that are not influenced by news events.

In today's digital age, investors have unprecedented access to a wealth of information through the Internet and various communication platforms. News and market data can be instantly accessed and disseminated, allowing for swift dissemination of information to investors worldwide. However, despite this enhanced accessibility, investors continue to exhibit overreactions or underreactions to new information.

Macroeconomic news release provide crucial insights into the overall health and performance of the economy. By monitoring and analyzing these indicators, investors can gain valuable information that can guide their investment decisions. Furthermore, by fostering a transparent and reliable information disclosure systems, governments can play a critical role in ensuring the stability and transparency of the funds market.

The paper utilizes 5-min high-frequency data from funds and incorporates a comprehensive macroeconomic news information database. These methodological choices enhance the precision and reliability of the analysis, allowing for a more nuanced understanding of the relationship between macroeconomic news releases and fund price jumps.