Journal of Modelling in Management最新文献

Purpose

The purpose of this research is to address the complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance. The primary goal is to minimize total tardiness, earliness and total completion times simultaneously. This study aims to provide effective solution methods, including a Mixed-Integer Programming (MIP) model, an Epsilon-constraint method and the Nondominated Sorting Genetic Algorithm (NSGA-II), to offer valuable insights into solving large-sized instances of this challenging problem.

Design/methodology/approach

This study addresses a multiobjective unrelated parallel machine scheduling problem with sequence-dependent setup times and periodic machine maintenance activities. An MIP model is introduced to formulate the problem, and an Epsilon-constraint method is applied for a solution. To handle the NP-hard nature of the problem for larger instances, an NSGA-II is developed. The research involves the creation of 45 problem instances for computational experiments, which evaluate the performance of the algorithms in terms of proposed measures.

Findings

The research findings demonstrate the effectiveness of the proposed solution approaches for the multiobjective unrelated parallel machine scheduling problem. Computational experiments on 45 generated problem instances reveal that the NSGA-II algorithm outperforms the Epsilon-constraint method, particularly for larger instances. The algorithms successfully minimize total tardiness, earliness and total completion times, showcasing their practical applicability and efficiency in handling real-world scheduling scenarios.

Originality/value

This study contributes original value by addressing a complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance activities. The introduction of an MIP model, the application of the Epsilon-constraint method and the development of the NSGA-II algorithm offer innovative approaches to solving this NP-hard problem. The research provides valuable insights into efficient scheduling methods applicable in various industries, enhancing decision-making processes and operational efficiency.

Purpose

The purpose of this study is to analyze the operations and performance dynamics of a supply chain (SC) subject to disruptions. The preparedness of Moroccan responders in handling emergencies could be enhanced significantly, by devising digital twin-based decision support systems (DSSs).

Design/methodology/approach

The authors create a discrete-event simulation model to investigate proactively risks and resilience of a Moroccan basic-items SC (BISC). In this study, the authors analyze the effects of catastrophe-related disruptions (CRDs) on the Moroccan BISC, by the use of a simulation-based decision-supporting quantitative method.

Findings

In the disruption-free simulation experiment, the outcome was a satisfactory 100% coverage. By implementing CRDs, inventory levels have dropped, service levels decreased, lead time raised and there was an increase in backlogged products and late orders numbers. The highest impact was observed for the shutdown of paths linking suppliers to warehouses, whereas the increase in demand had a comparatively minor effect. The risk analysis approach helps to identify critical products for which the time-to-recover is longer and requires more commitment to enhance their resilience.

Practical implications

The model serves to deduce quantitative resilience assessment from simulation, streamline the selection of recovery strategies and enable the best-informed reactive decision-making to minimize the impact.

Originality/value

The research brings organizing solutions to catastrophe-related emergencies in Morocco. It would contribute significantly by visualizing, examining and unveiling the effects of disruptions on a BISC and offering actionable recommendations for remedial measures.

Purpose

Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework.

Design/methodology/approach

Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework.

Findings

According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis.

Practical implications

The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system.

Originality/value

This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.

Purpose

This research aims to understand the primary challenges encountered by entrepreneurs operating in emerging economies, where entrepreneurship plays a vital role. The study places a particular emphasis on entrepreneurs in Brazil.

Design/methodology/approach

The research methodology involved the analysis of data obtained from interviews, using both content analysis and Grey Relational Analysis techniques.

Findings

The analysis revealed several prominent difficulties that entrepreneurs face in these domains. These challenges encompassed issues such as grappling with intricate taxation systems and the associated tax burden, navigating government bureaucracy, securing access to essential financing and initial investments, contending with the absence of supportive government programs and addressing the dynamic nature of market conditions. The findings on the most critical barriers reveal potential pathways for entrepreneurs, policymakers and universities to act in developing the entrepreneurial ecosystem in emerging economies.

Originality/value

The insights garnered from this research have the potential to inform the formulation of robust public policies aimed at fostering entrepreneurship and innovation in emerging countries. Furthermore, these findings can serve as a valuable resource for planning initiatives designed to train engineers to become successful entrepreneurs.

Purpose

This paper aims to investigate the impact of quality loss in transit on e-commerce supply chain pricing, production and financing decisions.

Design/methodology/approach

The authors consider a Stackelberg game model with a supplier, logistics firm and e-commerce platform. The logistics firm is capital-constrained and obtains funding from the e-commerce platform by debt financing or equity financing. Through backward induction, this paper first solves the equilibrium results under the two financing schemes and then reveals the financing preferences of all parties.

Findings

The results demonstrate that equity financing reduces financing costs and promotes production significantly. However, it may also lead to overproduction, particularly in markets with poor profitability and high cost factors. When the percentage of product quality loss is large, equity financing is preferable. With the increasing of transportation level, the benefits of debt finance are steadily growing. In addition, equity financing is the Pareto dominant scheme for all firms under certain circumstances. The extensions consider hybrid financing and another quality loss type.

Practical implications

The paper derives the equilibrium solutions and financing preferences, then specifies the threshold for applying financing schemes. Provide guidance for logistics firms’ finance model innovation and core enterprise involvement in the logistics industry.

Originality/value

The paper investigates how logistics firms’ financing strategies are impacted by product quality loss.

Purpose

Recently, machine learning (ML) methods gained popularity in finance and accounting research as alternatives to econometric analysis. Their success in high-dimensional settings is promising as a cure for the shortcomings of econometric analysis. The purpose of this study is to prove further the relationship between intellectual capital (IC) efficiency and firm performance using ML methods.

Design/methodology/approach

This study used the double selection, partialing-out and cross-fit partialing-out LASSO estimators to analyze the IC efficiency’s linear and nonlinear effects on firm performance using a sample of 2,581 North American firms from 1999 to 2021. The value-added intellectual capital (VAIC) and its components are used as indicators of IC efficiency. Firm performance is measured by return on equity, return on assets and market-to-book ratio.

Findings

The findings revealed significant connections between IC measures and firm performance. First, the VAIC, as an aggregate measure, significantly impacts both firm profitability and value. When the VAIC is decomposed into its breakdowns, it is revealed that structural capital efficiency substantially affects firm value, and capital employed efficiency has the same function for firm profitability. In contrast to the prevalent belief in the area, human capital efficiency’s impact is found to be less important than the others. Nonlinearities are also detected in the relationships.

Originality/value

As ML tools are most recently introduced to the IC literature, only a few studies have used them to expand the current knowledge. However, none of these studies investigated the role of IC as a determinant of firm performance. The present study fills this gap in the literature by investigating the effect of IC efficiency on firm performance using supervised ML methods. It also provides a novel approach by comparing the estimation results of three LASSO estimators. To the best of the author’s knowledge, this is the first study that has used LASSO in IC research.

Purpose

The purpose of this study is developing a new buy-back coordination contract in the symbiotic supply chain. In this new contract, the goal of the supply chain members (profit maximization) is realized.

Design/methodology/approach

This paper encourages the manufacturer to order products optimally by presenting a new buy-back coordination contract, and in return, the supplier undertakes to buy the unsold products from the manufacturer at the buy-back price. By using data-driven decision-making and multiobjective decision-making and considering the existing conditions in the symbiosis industry, a contract has been presented that guarantees the profits of supply chain members.

Findings

In this paper, it was found out how the authors can determine the order quantity, buy-back price and wholesale price in a symbiotic supply chain in such a way that it makes a profit for both the supplier and the manufacturer. In other words, how to determine these variables to encourage the manufacturer to order more quantity to the supplier so that both will benefit.

Originality/value

To the best of the authors’ knowledge, this is the first paper that defines a new buy-back coordination contract in the symbiotic supply chain by considering uncertain demand and a multiobjective model. Due to the importance of environmental issues, the sharing of resources by companies and organizations with each other, and the necessity of their cooperation, industries are moving toward a symbiosis industry.

Purpose

Using recent US regional data associated with food system operations, this study aims at building optimization and econometric models to incorporate varying influential factors on food hub location decisions and generate effective facility location solutions.

Design/methodology/approach

Mathematical optimization and econometric models have been commonly used to identify hub location decisions, and each is associated with specific strengths to handle uncertainty. This paper develops an optimization model and a hurdle model of the US fresh produce sector to compare the hub location solutions between these two modeling approaches.

Findings

Econometric modeling and mathematical optimization are complementary approaches. While there is a divergence between the results of the optimization model and the econometric model, the optimization solution is largely confirmed by the econometric solution. A combination of the results of the two models might lead to improved decision-making.

Practical implications

This study suggests a future direction in which model development can move forward, for example, to explore and expose how to make the existing modeling techniques easier to use and more accessible to decision-makers.

Social implications

The models and results provide information that is currently limited and is useful to help inform sustainable decisions of various stakeholders interested in the development of regional food systems, regional infrastructure investment and operational strategies for food hubs.

Originality/value

This study sheds light on how the application of complementary modeling approaches improves the effectiveness of facility location solutions. This study offers new perspectives on elaborating key features to encompass facility location issues by applying interdisciplinary approaches.

Purpose

While being integrated together conceptually and practically, the literature on game theory in the context of financial markets lacks a cohesive understanding. This study aims to systematically scrutinize and analyse the literature of game theory in the context of financial markets, through a systematic literature review.

Design/methodology/approach

A systematic literature-based approach, through the theories, context, characteristics and methodology (TCCM) framework has been applied to 97 articles, extracted and filtered from two databases, Scopus and Web of Science, for a comprehensive review and understanding of the intellectual development in the domain of game theory and financial markets.

Findings

The review highlighted the most utilitarian theories within the literature, the context of research in terms of countries and industries, four themes which characterize the literature, as well as the methodologies and research designs used in this research domain. The paper also uncovered certain essential areas that present scope for further research.

Research limitations/implications

While two of the largest indexation databases have been used, some relevant articles may have been excluded due to the restriction of databases and screening criteria, which may lead to the less exploration of several facets of the domain.

Practical implications

Practically, the paper has implications for multiple stakeholders including traders, businesses and governments. For traders, this paper acts as a guide to entering and understanding the dynamics of financial markets. The review also covers decision-making from the perspective of firms, including venture capitalists. This paper would allow firms to understand how game theory can help influence or analyze the strategic interactions between their stakeholders in terms of information disclosure, or consumers and their behavior to stimuli from the firm’s actions.

Originality/value

To the best of the authors’ knowledge, this study is the first of its kind that attempts to comprehensively provide an overview of the literature on game theory in financial markets. In doing so, this study shall help assess the current state of knowledge in the said field and locate gaps in the literature to propose new research directions.

Purpose

This paper aims to examine the blockchain introduction and altruistic preference decisions of the supplier in agricultural food supply chains and discuss how the supplier decisions are influenced by blockchain technology and altruistic preference levels.

Design/methodology/approach

The author considers a single period two-level supply chain model to describe the supplier’s decisions. The supplier, as the leader of the game, decides whether to introduce blockchain technology and his own level of altruistic preferences. Consumers have environmental awareness and heterogeneity in green trust. Supply chain members determine their own product pricing and green effort level under Stackelberg game.

Findings

The results reveal the negative impact of unit verification cost in the technology introduction process on the supply chain. In addition, the supplier can adjust their profits by adjusting their altruistic preferences after introducing blockchain to offset the impact of blockchain through the influence of altruistic preferences as discussed by the author.

Originality/value

This paper investigates how the profits and green efforts of supply chain members are influenced by blockchain technology and altruistic preferences.