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The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

This study aims to investigate the antecedents of behavioral intention and actual usage of mobile payment (m-payment) services during the COVID-19 pandemic among Indian consumers.

The proposed research model of this study is based on the extended framework of the Unified Theory of Acceptance and Use of Technology (UTAUT2) by using two additional variables, namely, perceived security (PS) and perceived trust (PT). In total, 436 sample respondents are chosen from Indian consumers with experience using m-payment services through the online survey method. The data analysis and proposed hypothetical relationships were tested using confirmatory factor analysis and structural equation modeling techniques.

The results confirm that performance expectancy, effort expectancy, facilitating conditions, PS, PT, habit and price value are antecedents of consumer intention toward adopting m-payment services. Furthermore, behavioral intention significantly influences the actual usage of m-payment services during the COVID-19 pandemic.

Though the impact of COVID-19 has been observed during the research period in getting responses from m-payment service users, the constructs used in the study are confined to the UTAUT2 model, and dimensions related to COVID-19 are not directly included in the measurement scale. The study’s findings propose valuable insights for service providers and policymakers.

This study’s results offer valuable insights to the service providers and policymakers to achieve the Government of India digital India objective of “Faceless, Paperless and Cashless” transactions.

This study’s results contribute to extending the empirical research literature on m-payment as antecedents of behavioral intention toward the adoption of m-payment services during the COVID-19 pandemic. Furthermore, this study assumes important interrelationships among UTAUT2 constructs with the additional incorporation of PS and PT.

This study investigates the impact of bank CEOs’ inside debt on shareholder benefits in the context of bank mergers and acquisitions (M&A) before the 2008–2009 financial crisis.

Employing an event-study methodology, this analysis delves into market reactions to bank M&A announcements during 2006–2007, encompassing 105 M&As by 79 public commercial banks. This era witnessed heightened risk-taking behavior on the verge of the financial crisis. We explore the relation between relative inside debt and market abnormal returns at M&A announcements and the association between relative inside debt and cash payment preferences in M&As.

Evidence suggests that M&A announcements from banks where acquiring CEOs hold a substantial inside debt experience favorable stock market reaction, particularly for smaller banks. Additionally, banks with elevated CEO inside debt tend to favor cash as a payment mode for M&As.

One limitation of this study is the short period of data availability. The data used in this study covers only 2006 and 2007, the periods marked by notable risk-taking activities on the verge of the financial crisis. Although this period is perfectly suitable for our investigation, given the prevalence of conflicts between equity and debt holders, it is essential to acknowledge that our findings may not capture changes or trends over time. Nevertheless, the results offer valuable insights into the factors that influence the behavior of the studied population. Future research could employ a longitudinal design to address this limitation and gain a more comprehensive understanding of the dynamics over extended periods.

Our study has significant implications for businesses and policymakers as it provides insights into the factors contributing to financial crises and how compensation mechanisms can be used to moderate bank risk-taking. We propose that CEO inside debt compensation presents a plausible mechanism that boards of directors can incorporate into bank executive compensation contracts. By doing so, they can promote value-enhancing investments and moderate excessive risk-taking, thereby safeguarding the financial stability of individual banks and overall financial system.

Our study sheds light on the beneficial role of bank CEO inside debt for shareholders, contributing empirical backing to the conflict resolution viewpoint in the discourse on wealth appropriation. From a regulatory stance, our findings advocate for the inclusion of bank CEO inside debt in executive remuneration agreements. Such a strategy can empower boards of directors to mitigate undue risk and enhance shareholder value in M&As, safeguarding both individual bank and broader financial system stability.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

This study aims to address three research questions pertaining to climate neutrality within the supply chain of metal and mining industry: (1) How can an organization implement practices related to climate neutrality in the supply chain? (2) How do members of the supply chain adopt different measures and essential processes to assist an organization in responding to climate change-related concerns? (3) How can the SAP-LAP framework assist in analyzing and proposing solutions to attain climate neutrality?

To address the proposed research questions concerning climate neutrality, this study employs a case study approach utilizing the SAP-LAP (situation, actor, process–learning, action, performance) framework. Within the SAP-LAP framework, adopting a natural resource-based perspective, the study thoroughly examines the intricacies and interactions among existing situations, pertinent actors and processes that impact climate initiatives within a metal and mining company.

The study's findings suggest that organizations can achieve the objective of climate neutrality by prioritizing resources and capabilities that lead to reduced GHG emissions, lower energy consumption and optimal resource utilization. The study further proposes key elements that significantly influence the pursuit of climate neutrality within enterprises.

This study is one of the earliest contributions to the development of a holistic understanding of climate neutrality in the supply chain of the metal and mining industry.

The study will assist practitioners and policymakers in comprehending the present circumstances, actors and processes involved in enterprises' supply networks in order to attain climate neutrality in supply chains, as well as in taking the right steps to enhance performance.

This study presents a climate neutrality model and provides valuable insights into emission management, contributing to the achievement of the climate neutrality objective.

In recent years, additive manufacturing (AM) has started to be used for manufacturing real functional parts and assemblies for critical applications in aerospace, automotive, and machinery industries. Most complex or assembled parts require internal features (IF) such as holes, channels, slots, or guides for locational and mating requirements. Therefore, it is critical to understand and compare the structural and mechanical properties of additively manufactured and conventionally machined IFs.

In this study, mechanical and microstructural properties of Inconel 718 (Inc718) alloy internal features, manufactured either as-built with AM or machining of additively manufactured (AMed) part thereafter were investigated.

The results showed that the average ultimate tensile strength (UTS) of additively manufactured center internal feature (AM-IF) is almost analogous to the machined internal feature (M-IF). However, the yield strength of M-IF is greater than that of AM-IF due the greater surface roughness of the internal feature in AM-IF, which is deemed to surpass the effect of microstructure on the mechanical performance. The results of digital image correlation (DIC) analysis suggest that AM-IF and M-IF conditions have similar strain values under the same stress levels but the specimens with as built IF have a more locally ductile region around their IF, which is confirmed by hardness test results. But this does not change global elongation behavior. The microstructural evolution starting from as-built (AB) and heat-treated (HT) samples to specimens with IF are examined. The microstructure of HT specimens has bimodal grain structure with d phase while the AB specimens display a very fine dendritic microstructure with the presence of carbides. Although they both have close values, machined specimens have a higher frequency of finer grains based on SEM images.

It was shown that the concurrent creation of the IF during AM can provide a final part with a preserved ultimate tensile strength and elongation but a decreased yield strength. The variation in UTS of AM-IF increases due to the surface roughness near the internal feature as compared to smooth internal surfaces in M-IF. Hence, the outcomes of this study are believed to be valuable for the industry in terms of determining the appropriate production strategy of parts with IF using AM and postprocessing processes.

The digital economy is profoundly transforming the manufacturing industry's fundamental concepts and value creation logic, making digital transformation (DT) strategy a crucial decision for manufacturers. And faced with increasingly severe environmental issues, DT may become an important means to achieve sustainable development. This paper mainly discusses the strategic choice of the manufacturer's DT and analyzes the impact of DT on carbon emissions.

Based on the carbon cap-and-trade mechanism, the authors have constructed two decision models to study the DT strategy of the manufacturer, further exploring the impact of the mechanism on the DT strategy and production strategy of the manufacturer. Finally, the authors discussed the effect of manufacturers' DT on their carbon emissions.

The authors found that the manufacturer should initiate DT to enhance their competitiveness, regardless of whether they are in a low digital technology scenario or a high digital technology scenario. Notably, DT can enhance the ability of the manufacturer to respond to external emergencies. In a low digital technology market scenario, both carbon emissions per unit of product and carbon price are positively affecting the digitization level of the manufacturer. In a high digital technology market scenario, the manufacturer will initiate a full degree of DT. Moreover, the impact of DT on total carbon emissions varies in markets with different levels of digital technology.

Innovatively, the authors divided the DT of the manufacturer into market scenarios with low digital technology and high digital technology. Provide the manufacturer with DT decisions according to different scenarios. At the same time, it verifies the uncertainty of DT on carbon emission and enriches the related research.

In mobile banking (m-banking), understanding the factors contributing to customer satisfaction is crucial for bank managers to design effective strategies for enhancing the uptake of mobile banking services. This study assesses the relationships between quality, technology acceptance and credibility factors and behavioural outcomes (actual use, continuance intention and loyalty) and satisfaction with m-banking. It further investigates the moderating influence of economy type, innovation level, connectivity level and sample size on all these relationships.

The study employs a meta-analysis technique and reviews 54 published studies to investigate the antecedents and consequences of satisfaction with m-banking.

The study finds a significant relationship between satisfaction with m-banking and quality, technology acceptance and credibility factors and behavioural outcomes. It concludes that the moderating effect of economy type, innovation level, connectivity level and sample size partially moderate the majority of the hypothesized relationships.

Drawing on a comprehensive literature review, this study presents a novel framework elucidating the antecedents and behavioural outcomes of satisfaction with mobile banking. It contributes to the literature by exploring the moderating effects of sample size and country context on the relationships between these factors, presenting important implications for future mobile banking research.

This study has practical implications for m-banking service providers, offering insights into the factors that drive user satisfaction with mobile banking and highlighting the need for tailored strategies in different country contexts.

This study examines the effects of factors leading to satisfaction and the subsequent outcomes within the context of m-banking. The findings offer fresh perspectives that can be valuable for managers and policymakers, enabling them to enhance customer satisfaction in the realm of m-banking.

The paper proposes an efficient and insightful approach for solving neutral delay differential equations (NDDE) with high-frequency inputs. This paper aims to overcome the need to use a very small time step when high frequencies are present. High-frequency signals abound in communication circuits when modulated signals are involved.

The method involves an asymptotic expansion of the solution and each term in the expansion can be determined either from NDDE without oscillatory inputs or recursive equations. Such an approach leads to an efficient algorithm with a performance that improves as the input frequency increases.

An example shall indicate the salient features of the method. Its improved performance shall be shown when the input frequency increases. The example is chosen as it is similar to that in literature concerned with partial element equivalent circuit (PEEC) circuits (Bellen et al., 1999). Its structure shall also be shown to enable insights into the behaviour of the system governed by the differential equation.

The method is novel in its application to NDDE as arises in engineering applications such as those involving PEEC circuits. In addition, the focus of the method is on a technique suitable for high-frequency signals.

The numerical methods are of great importance for approximating the solutions of a system of nonlinear singular ordinary differential equations. In this paper, the authors present the biorthogonal flatlet multiwavelet collocation method (BFMCM) as a numerical scheme for a class of system of Lane–Emden equations with initial or boundary or four-point boundary conditions.

The approach is involved in combining the biorthogonal flatlet multiwavelet (BFM) with the collocation method. The authors investigate the properties and procedure of the BFMCM for first time on this class of equations. By using the BFM and the collocation points, the method is constructed and it transforms the nonlinear differential equations problem into a system of nonlinear algebraic equations. The unknown coefficients of the assuming solution are determined by solving the obtained system. Additionally, convergence analysis and numerical stability of the suggested method are provided.

According to the attained results, the proposed BFMCM has more accurate results in comparison with results of other methods. The maximum absolute errors are calculated by using the BFMCM for comparison purposes provided.

The key desirable properties of BFMCM are its efficiency, simple applicability and minimizes errors. Therefore, the proposed method can be used to solve nonlinear problems or problems with singular points.