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The purpose of this paper is to review the applications of the chemical reactor network (CRN) approach for modeling the combustion in gas turbine combustors and classify the CRN construction methods that have been frequently used by researchers.

This paper initiates with introducing the CRN approach as a practical tool for precisely predicting the species concentrations in the combustion process with lower computational costs. The structure of the CRN and its elements as the ideal reactors are reviewed in recent studies. Flow field modeling has been identified as the most important input for constructing the CRNs; thus, the flow field modeling methods have been extensively reviewed in previous studies. Network approach, component modeling approach and computational fluid dynamics (CFD), as the main flow field modeling methods, are investigated with a focus on the CRN applications. Then, the CRN construction approaches are reviewed and categorized based on extracting the flow field required data. Finally, the most used kinetics and CRN solvers are reviewed and reported in this paper.

It is concluded that the CRN approach can be a useful tool in the entire process of combustion chamber design. One-dimensional and quasi-dimensional methods of flow field modeling are used in the construction of the simple CRNs without detailed geometry data. This approach requires fewer requirements and is used in the initial combustor designing process. In recent years, using the CFD approach in the construction of CRNs has been increased. The flow field results of the CFD codes processed to create the homogeneous regions based on construction criteria. Over the past years, several practical algorithms have been proposed to automatically extract reactor networks from CFD results. These algorithms have been developed to identify homogeneous regions with a high resolution based on the splitting criteria.

This paper reviews the various flow modeling methods used in the construction of the CRNs, along with an overview of the studies carried out in this field. Also, the usual approaches for creating a CRN and the most significant achievements in this field are addressed in detail.

In this paper we present a systemic approach to modeling coopetition between firms that provides a methodology for analyzing the strategic incentives for organizations to engage in coopetition relationships (the why) and the organization design required to address the complexities inherent in such multifaceted relationships (the how).

We pursue a model-based approach that incorporates important conceptualizations adapted from competence-based management (CBM) theory and value network approach. We illustrate the applicability of our approach by applying it to the case of coopetition between IBM and Apple in the development of PowerPC CPU.

We show how modeling can contribute to our understanding of the strategic incentives for the organizations to develop a coopetitive relationship (“the why” of coopetition) and the organization design required for accommodating and addressing the complexities and dynamics of such a multifaceted relationship (“the how” of coopetition).

First, our findings echo some of the perspectives developed in coopetition literature. Second, the study has utilized value network-level analysis in examining coopetition.

The modeling framework reported in this paper can help management practitioners in structuring choice situations involving coopetition, both in terms of the incentives to engage in a coopetitive setting and the design of a value network that can accommodate the complexities inherent in such multifaceted relations.

Modern enterprises face a strong economical pressure to increase competitiveness, to operate on a global market, and to engage in alliances of several kinds. In order to meet the requirements and challenges of participating in such alliances, companies must be able to cooperate effectively and efficiently. The purpose of this paper is to provide an overview of some major directions in inter‐organizational cooperation.

In order to cope with the challenges of inter‐organizational cooperation, to share innovative research issues and to facilitate profound discussions about them, the authors organized a series of workshops on Modeling Inter‐Organizational Systems (MIOS‐CIAO!) starting at the annual OTM Federated Conference and Continuing at the Annual CAiSE Conference. This paper summarizes the results of the workshops.

This paper provides an overview of what has been established and what is going on regarding the cooperation of enterprises in networks. The focus has been on the modeling of cooperation, from the business level down to the implementation level.

This overview is a useful source of knowledge for those who want to have a quick insight in the relevant aspects of cooperation, and in many well‐known modeling approaches and techniques. It is also an inspiring source for those who want to investigate yet unsolved or unsatisfactorily solved problems. Although developments, both in theory and in practice, will go on, no landslides are expected. Particularly for practice, the value of this report will therefore last for a considerable time.

Several core notions in the area of inter‐organizational cooperation are clarified, such as collaboration, cooperation, enterprise network, choreography, and orchestration. The whole process of developing or investigating an enterprise network is covered.

The purpose of this paper is to provide a conceptual foundation and empirical basis for exploring issues related to the design framework for modeling coordination in complex and dynamic enqvironments.

Previous research suggests that interactions among actors in a complex and dynamic environment tend to be more elastic, offering a higher degree of adaptability. Actions of actors in such an environment need to be coordinated to achieve the desired goal. With that purpose, the authors suggest a social network‐based (SN‐based) framework to model coordination in complex and dynamic environments.

The authors successfully applied the proposed SN‐based framework to model coordination in the context of soft‐target organization and emergency response preparedness.

It is apparent that much work has been done in existing studies on modeling coordination considering the specific domain situation in a complex and dynamic environment. In this paper, the authors propose a unique framework to model coordination in a complex and dynamic environment.

The engineering education accreditation (EEA) is a principal quality assurance mechanism. However, at many education institutions, the most labor-intensive work of EEA process is accomplished manually. Without the support of computer and information technology, the EEA process leads to high labor intensity, low work efficiency and poor management level. The purpose of this paper is to build a complex network model and realize an information management system of talent training program for supporting the EEA process.

Based on polychromatic graph (PG), this paper builds a network model of talent training program for engineering specialty. The related information and data are organized and processed in this network model. From the bidirections of top-down and bottom-up, the user requirements are retrieved automatically in logic layer. Together with the specialty of mechanical engineering, the proposed PG-based network modeling method is applied and the corresponding information management system is realized.

The study results show that the PG-based network modeling method takes full advantages of the strong simulation ability of PG to model the complex network system and has some unique merits in formal expression of problem, efficient processing of information and lightweight realization of system. Further, the information management system of talent training program can reduce the tedious human labor and improve the management level of EEA process dramatically.

This paper proposes a PG-based network modeling method, in which the nodes and the edges can be painted by some unified colors to describe the different kinds of activities and the various types of interactions. Theoretically, this modeling method does not distinguish the activities, the interactions and their properties in graphic symbol and the problem size is diminished about a half. Furthermore, this paper provides an effective experience and idea to the education institutions for implementing the engineering education accreditation, increasing the education management efficiency and promoting the talent training quality.

Despite rapid success in bringing SARS-CoV-2 vaccines to distribution by multiple pharmaceutical corporations, supply chain failures in production and distribution can plague pandemic recovery. This review analyzes and addresses gaps in modeling supply chain resilience in general and specifically for vaccines in order to guide researchers and practitioners alike to improve critical function of vaccine supply chains in the face of inevitable disruptions.

Systematic review of the literature on modeling supply chain resilience from 2007 to 2020 is analyzed in tandem with the vaccine supply chain manufacturing literature. These trends are then used to apply a novel matrix analysis to seven Securities and Exchange Commission (SEC) annual filings of pharmaceutical corporations involved in COVID-19 vaccine manufacture and distribution.

Pharmaceutical corporations favor efficiency as they navigate regulatory, economic and other threats to their vaccine supply chains, neglecting resilience – absorption, adaptation and recovery from inevitable and unexpected disruptions. However, explicitly applying resilience analytics to the vaccine supply chain and further leveraging emerging network science tools found in the academic literature, such as artificial intelligence (AI), stress tests and digital twins, will help supply chain managers to better quantify efficiency/resilience tradeoffs across all associated networks/domains and support optimal system performance post disruption.

This is the first review addressing resilience analytics in vaccine supply chains and subsequent extension to operational management through novel matrix analyses of SEC Filings. The authors provide analyses and recommendations that facilitate resilience quantification capabilities for vaccine supply chain managers, regulatory agencies and corporate stakeholders and are especially relevant for pandemic response, including application to the SARS-CoV-2 vaccines.

Iran Nanotechnology Initiative Council could significantly increase the production of scientific articles in the field by imposing ten-year incentive policies so that Iran ranked 7 in this area in the year 2015. But this progress was insufficient to speed up the production and commercialization of nanotechnology products and Iran ranked 44 with a share of 0.03 per cent of nanotechnology production in the world. Therefore, Iran Nanotechnology Initiative Council as a governmental policymaker institution in this area has sought for the policy threefold increase of funding to speed up the production and commercialization of products in this field. But given that the result was not so clear, this research was formed in the form of modeling Iran Nanotechnology Innovation Network and testing various scenarios to increase its efficiency.

This paper uses simulation framework of innovation networks (SKIN) in an attempt to model the production innovation network in Iran in the field of nanotechnology that can measure the effect of incentive policies in changing the network structure and, consequently, increasing the level and pace of innovation in it. Given that the volume of articles produced in Iranian universities in the field of Nanotechnology had a high speed and volume in comparison with the volume of technical knowledge produced by companies, and because the SKIN framework did not consider the distinction between the two, in the first step, the framework is developed using the model of absorptive capacity of knowledge provided by Cohen and Levinthal (1990) and then the developed model was used to model the Innovation Network.

Finally, two policies of threefold increase of budget (Scenario 1) and increasing the support for joint projects (with maintaining the current budget level) (Scenario 2) were tested in this model. The social network analysis method was used to analyze the results of the two scenarios, where innovation network topology was compared (as an index to measure the network efficiency) in three states of current status of the network (the baseline scenario), implementing the first and second scenarios of extraction and with each other.

This paper models Iranian Nanotechnology Innovation Network for studying the evolution of the network as a result of executing different supportive scenarios.

The purpose of this paper is to address the limitations of conventional risk matrix based tools such that both positive and negative connotation of uncertainty could be captured within a unified framework that is capable of modeling the direction and strength of causal relationships across uncertainties and prioritizing project uncertainties as both threats and opportunities.

Theoretically grounded in the frameworks of Bayesian belief networks (BBNs) and interpretive structural modeling (ISM), this paper develops a structured process for assessing uncertainties in projects. The proposed process is demonstrated by a real application in the construction industry.

Project uncertainties must be prioritized on the basis of their network-wide propagation impact within a network setting of interacting threats and opportunities. Prioritization schemes neglecting interdependencies across project uncertainties might result in selecting sub-optimal strategies. Selection of strategies should focus on both identifying common cause uncertainty triggers and establishing the strength of interdependency between interconnected uncertainties.

This paper introduces a novel approach that integrates both facets of project uncertainties within a project uncertainty network so that decision makers can prioritize uncertainty factors considering the trade-off between threats and opportunities as well as their interactions. The ISM based development of the network structure helps in identifying common cause uncertainty triggers whereas the modeling of a BBN makes it possible to visualize the propagation impact of uncertainties within a network setting. Further, the proposed approach utilizes risk matrix data for project managers to be able to adopt this approach in practice. The proposed process can be used by practitioners while developing uncertainty management strategies, preparing risk management plans and formulating their contract strategy.

This research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.

A feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.

The findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.

Based on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.

The present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

The purpose of this paper is to assist a manufacturing firm in designing the closed-loop supply chain network under risks that are affecting its supply quality and logistics operations. The modeling approach adopted aims at the embedding supply chain risks in a closed-loop supply chain (CLSC) network design process and suggests optimal supply chain configuration and risk mitigation strategies.

The method proposes a closed-loop supply chain network and identifies the network parameter and variables required for closing the loop. Mixed-integer-linear-programming-based mathematical modeling approach is used to formulate the research problem. The solutions and test results are obtained from CPLEX solver.

The outcomes of the proposed model were demonstrated through a case study conducted in an Indian hospital furniture manufacturing firm. The modern supply chain is mapped to make it closed loop, and potential risks in its supply chain are identified. The supply chain network of the firm is redesigned through embedding risk in the modeling process. It was found that companies can be in great profit if they follow closed-loop practices and simultaneously keep a check on risks as well. The cost of making the supply chain risk averse was found to be insignificant.

Although the study was conducted in a practical case situation, the obtained results are not indiscriminate to the other circumstances. However, the approach followed and proposed methodology can be applied to many industries once a firm decides to redesign its supply chain for closing its loop or model under risks.

By using the identified CLSC parameters and applying the proposed network design methodology, a firm can design/redesign their supply chain network to counter the risk and accordingly come up with planned mitigation strategies to achieve a certain degree of robustness.