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1 – 10 of over 1000
Article
Publication date: 5 September 2023

Taicir Mezghani, Mouna Boujelbène and Souha Boutouria

This paper investigates the predictive impact of Financial Stress on hedging between the oil market and the GCC stock and bond markets from January 1, 2007, to December 31, 2020…

Abstract

Purpose

This paper investigates the predictive impact of Financial Stress on hedging between the oil market and the GCC stock and bond markets from January 1, 2007, to December 31, 2020. The authors also compare the hedging performance of in-sample and out-of-sample analyses.

Design/methodology/approach

For the modeling purpose, the authors combine the GARCH-BEKK model with the machine learning approach to predict the transmission of shocks between the financial markets and the oil market. The authors also examine the hedging performance in order to obtain well-diversified portfolios under both Financial Stress cases, using a One-Dimensional Convolutional Neural Network (1D-CNN) model.

Findings

According to the results, the in-sample analysis shows that investors can use oil to hedge stock markets under positive Financial Stress. In addition, the authors prove that oil hedging is ineffective in reducing market risks for bond markets. The out-of-sample results demonstrate the ability of hedging effectiveness to minimize portfolio risk during the recent pandemic in both Financial Stress cases. Interestingly, hedgers will have a more efficient hedging performance in the stock and oil market in the case of positive (negative) Financial Stress. The findings seem to be confirmed by the Diebold-Mariano test, suggesting that including the negative (positive) Financial Stress in the hedging strategy displays better out-of-sample performance than the in-sample model.

Originality/value

This study improves the understanding of the whole sample and positive (negative) Financial Stress estimates and forecasts of hedge effectiveness for both the out-of-sample and in-sample estimates. A portfolio strategy based on transmission shock prediction provides diversification benefits.

Details

Managerial Finance, vol. 50 no. 3
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 14 September 2023

Julia T. Thomas and Mahesh Kumar

The purpose of the paper is set to minimize the total cost of a manufacturing system when an acceptance sampling plan (ASP) is carried out in a fuzzy environment.

Abstract

Purpose

The purpose of the paper is set to minimize the total cost of a manufacturing system when an acceptance sampling plan (ASP) is carried out in a fuzzy environment.

Design/methodology/approach

A fuzzy acceptance sampling plan (FASP) is employed for the inspection of the batch of products and a fuzzy cost optimization problem is formulated.

Findings

The extent of uncertainty determines an interval for the total cost function with upper and lower bounds. The effect of variation in the ambiguity of the proportion of defectives in the probability of acceptance is determined.

Practical implications

The proposed model is specifically designed for production and supply units with ASP for attributes. Still, the proportion of defectives in the inspection process is fuzzy.

Originality/value

Fuzzy probability distribution is used to model an optimal inspection plan for a general supply chain. Economic design of supply chain under fuzzy proportion of defectives is discussed for the first time.

Details

International Journal of Quality & Reliability Management, vol. 41 no. 3
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 12 January 2023

Jia Jia Chang, Zhi Jun Hu and Changxiu Liu

In this study, a dynamic contracting model is developed between a venture capitalist (VC) and an entrepreneur (EN) to explore the influence of asymmetric beliefs regarding…

Abstract

Purpose

In this study, a dynamic contracting model is developed between a venture capitalist (VC) and an entrepreneur (EN) to explore the influence of asymmetric beliefs regarding output-relevant parameters, agency conflicts and complementarity on the VC's posterior beliefs through the EN's unobservable effort choices to influence the optimal dynamic contract.

Design/methodology/approach

The authors construct the contracting model by incorporating the VC's effort, which is ignored in most studies. Using backward induction and a discrete-time approximation approach, the authors solve the continuous-time contract design problem, which evolves into a nonlinear ordinary differential equation (ODE).

Findings

The optimal equity share that the VC provides to the EN decreases over time. In accordance with the empirical evidence, the EN's optimistic beliefs regarding the project's profitability positively affect its equity share. However, the interactions between the optimal equity share, project risk and both partners' degrees of risk aversion are not monotonic. Moreover, the authors find that the optimal equity share increases with the degree of complementarity, which indicates that the EN is willing to cooperate with the VC. This study’s results also show that the optimal equity shares at each time are interdependent if the VC is risk-averse and independent if the VC is risk-neutral.

Research limitations/implications

In conclusion, the authors highlight two potential directions for future research. First, the authors only considered a single VC, whereas in practice, a risk project may be carried out by multiple VCs, and it is interesting to discuss how the degree of complementarity affects the number of VCs that ENs contract. Second, the authors may introduce jumps and consider more general multivariate stochastic volatility models for output dynamics and analyze the characteristics of the optimal contracts. Third, further research can deal with other forms of discretionary output functions concerning complementarity, such as Cobb–Douglas and constant elasticity of substitution (See Varian, 1992).

Social implications

The results of this study have several implications. First, it offers a novel approach to designing dynamic contracts that are specific and easy to operate. To improve the complicated venture investment situation and abate conflict between contractual parties, this study plays a good reference role. Second, the synergy effect proposed in this study provides a theoretical explanation for the executive compensation puzzle in economics, in which managers are often “rewarded for luck” (Bertrand and Mullainathan, 2001; Wu et al., 2018). This result indicates a realistic perspective on financing and establishing cooperative relationships, which enhances the efficiency of venture investment. Third, from an empirical standpoint, one can apply this framework to study research and development (R&D) problems.

Originality/value

First, the authors introduce asymmetric beliefs and Bayesian learning to study the dynamic contract design problem and discuss their effects on equity share. Second, the authors incorporate the VC's effort into the contracting problem, and analyze the synergistic effect of effort complementarity on the optimal dynamic contract.

Details

Kybernetes, vol. 53 no. 4
Type: Research Article
ISSN: 0368-492X

Keywords

Open Access
Article
Publication date: 12 October 2023

V. Chowdary Boppana and Fahraz Ali

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the…

424

Abstract

Purpose

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

Design/methodology/approach

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

Findings

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

Research limitations/implications

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

Practical implications

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

Originality/value

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

Details

International Journal of Industrial Engineering and Operations Management, vol. 6 no. 2
Type: Research Article
ISSN: 2690-6090

Keywords

Article
Publication date: 27 November 2023

Velmurugan Kumaresan, S. Saravanasankar and Gianpaolo Di Bona

Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in…

Abstract

Purpose

Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.

Design/methodology/approach

To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.

Findings

The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.

Originality/value

The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

Details

Journal of Quality in Maintenance Engineering, vol. 30 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Content available
Article
Publication date: 19 December 2023

Tamara Apostolou, Ioannis N. Lagoudis and Ioannis N. Theotokas

This paper aims to identify the interplay of standard Capesize optimal speeds for time charter equivalent (TCE) maximization in the Australia–China iron ore route and the optimal…

Abstract

Purpose

This paper aims to identify the interplay of standard Capesize optimal speeds for time charter equivalent (TCE) maximization in the Australia–China iron ore route and the optimal speeds as an operational tool for compliance with the International Maritime Organization (IMO) carbon intensity indicator (CII).

Design/methodology/approach

The TCE at different speeds have been calculated for four standard Capesize specifications: (1) standard Capesize with ecoelectronic engine; (2) standard Capesize with non-eco engine (3) standard Capesize vessel with an eco-electronic engine fitted with scrubber and (4) standard Capesize with non-eco engine and no scrubber fitted.

Findings

Calculations imply that in a highly inflationary bunker price context, the dollar per ton freight rates equilibrates at levels that may push optimal speeds below the speeds required for minimum CII compliance (C Rating) in the Australia–China trade. The highest deviation of optimal speeds from those required for minimum CII compliance is observed for non-eco standard Capesize vessels without scrubbers. Increased non-eco Capesize deployment would see optimal speeds structurally lower at levels that could offer CII ratings improvements.

Originality/value

While most of the studies have covered the use of speed as a tool to improve efficiency and emissions in the maritime sector, few have been identified in the literature to have examined the interplay between the commercial and operational performance in the dry bulk sector stemming from the freight market equilibrium. The originality of this paper lies in examining the above relation and the resulting optimal speed selection in the Capesize sector against mandatory environmental targets.

Details

Maritime Business Review, vol. 9 no. 1
Type: Research Article
ISSN: 2397-3757

Keywords

Article
Publication date: 29 March 2024

Tugrul Oktay and Yüksel Eraslan

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design…

Abstract

Purpose

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.

Design/methodology/approach

The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.

Findings

Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.

Originality/value

This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

Details

Aircraft Engineering and Aerospace Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1748-8842

Keywords

Book part
Publication date: 4 March 2024

Oswald A. J. Mascarenhas, Munish Thakur and Payal Kumar

We revisit the problem of redesigning the Master in Business Administration (MBA) program, curriculum, and pedagogy, focusing on understanding and seeking to tame its “wicked…

Abstract

Executive Summary

We revisit the problem of redesigning the Master in Business Administration (MBA) program, curriculum, and pedagogy, focusing on understanding and seeking to tame its “wicked problems,” as an intrinsic part and challenge of the MBA program venture, and to render it more realistic and relevant to address major problems and their consequences. We briefly review the theory of wicked problems and methods of dealing with their consequences from multiple perspectives. Most characterization of problems classifies them as simple (problems that have known formulations and solutions), complex (where formulations are known but not their resolutions), unstructured problems (where formulations are unknown, but solutions are estimated), and “wicked” (where both problem formulations and their resolutions are unknown but eventually partially tamable). Uncertainty, unpredictability, randomness, and ambiguity increase from simple to complex to unstructured to wicked problems. A redesigned MBA program should therefore address them effectively through the four semesters in two years. Most of these problems are real and affect life and economies, and hence, business schools cannot but incorporate them into their critical, ethical, and moral thinking.

Details

A Primer on Critical Thinking and Business Ethics
Type: Book
ISBN: 978-1-83753-312-1

Article
Publication date: 30 January 2023

Xiaoxi Zhu, Juan Liu, Meifei Gu and Changhui Yang

To examine how shareholding affects optimal profits, R&D innovation, NEV market scale and social welfare in two supply chain models with partial and cross ownership patterns.

Abstract

Purpose

To examine how shareholding affects optimal profits, R&D innovation, NEV market scale and social welfare in two supply chain models with partial and cross ownership patterns.

Design/methodology/approach

The gradual retreat of government subsidies has directly weakened the financial support available to the stakeholders of new energy vehicles (NEVs). In this context, upstream and downstream enterprises of NEV are constantly seeking new business models of cooperation to achieve possible win-wins. NEV supply chain shareholding is an emerging new practice for such explorations. However, its performance in the NEV supply chain is seldom investigated. In this paper, we employ a Stackelberg game model to investigate how partial and cross-ownership affect the optimal decisions in a NEV supply chain.

Findings

Results showed that: (1) Compared with the unilateral shareholding model, the battery supplier will benefit from cross-ownership in the supply chain, while the NEV manufacturer will not necessarily benefit from it. At the same time, cross-ownership will bring the greatest incentive for battery R&D (2) Supply chain downstream competition will not necessarily lead to the improvement of the total consumption of NEVs or the level of battery design. Pareto improvement can be brought only when one of the manufacturers holds less than a certain equity threshold. In addition, downstream competition will also not necessarily bring more benefits to the battery supplier.

Originality/value

At present, NEV supply chain management has attracted widespread attention from scholars from all walks of life. Previous studies have been carried out that covers topics such as pricing strategies and optimal profits and the role of NEV in the sustainable development of the automotive industry supply chain, or disparate impacts of government subsidies and carbon emission regulation on supply chain members. However, as far as the authors know, compared with the new emerging NEV corporate practice, the shareholding phenomenon between upstream and downstream in the supply chain of NEV has not been studied in the existing studies.

Details

Kybernetes, vol. 53 no. 4
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 26 December 2023

Yan Li, Ming K. Lim, Weiqing Xiong, Xingjun Huang, Yuhe Shi and Songyi Wang

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental…

Abstract

Purpose

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

Design/methodology/approach

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

Findings

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

Research limitations/implications

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

Originality/value

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

Details

Industrial Management & Data Systems, vol. 124 no. 3
Type: Research Article
ISSN: 0263-5577

Keywords

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