Mike Ryan

Capability Associates

Exploratory modelling is an emerging approach which can address the challenge of model‐based decision making in dealing with input model uncertainties. Exploratory modelling samples from an input uncertainty space and generates extensive computational experiments to analyse possible model behaviours in an output solution space. The way that the inp...

Traceability has been addressed in the past from the perspective of relationships between the digital artifacts within the data and the information model of the system of interest (SoI) being developed. This paper enhances this view from a project management (PM), systems engineering (SE), and a configuration management (CM) perspective. The paper...

Conventionally, project managers schedule the activities first; later prepare for materials management, procurement, and supplier selection in project planning. This disjointed process leads in a loss of expected profit for the company owing to a lack of planning coordination. In this paper, a mixed integer programming model is developed for resour...

Modular product family architecture (PFA), in coordination with the supply chain, assists manufacturers in achieving lower costs and higher efficiency by sharing a common platform. Despite its advantages, however, the prevailing practice of PFA emphasises architectural aspects that do not focus on the interface requirements for an efficient supply...

This study presents a novel hybrid framework combining feature selection, oversampling, and machine learning (ML) to improve the prediction performance of vehicle insurance. The framework addresses the class imbalance problem in binary classification tasks by employing principal component analysis for feature selection, the synthetic minority overs...

This paper presents a decision-support methodology to support the development and assessment of military asset and resource strategies. The methodology is built around a system dynamics model that allows users to examine the performance of a strategy over time. The novelty of the model lies in its flexibility and ability to address questions about...

The resource constrained multi-project scheduling problem (RCMPSP) is a well-known NP-hard problem. In this study, a surrogate- assisted genetic algorithm (SaGA) is presented for solving the RCMPSP. A non-random initialization starts the SaGA with a certain diversity and quality. A forward-backward improvement (FBI) based local search is utilized t...

p> Almost every web-based application is managed and operated through a number of websites, each of which is vulnerable to cyber-attacks that are mounted across the same networks used by the applications, with much less risk to the attacker than physical attacks. Such web-based attacks make use of a range of modern techniques-such as structured que...

In supply chain literature, production coordination and vehicle routing have received a lot ofattention. Even though all functions in the supply chain are interrelated, they are normally handledindependently. This disconnected approach might lead to less-than-ideal outcomes. Increasing totalefficiency by integrating manufacturing and delivery sched...

The links to the Algorithms and Tables does not function properly in the PDF version of the article. All the links are now corrected in the original publication.

Recent global changes have prompted manufacturers to shift their production systems to make-to-order (MTO) supply chain (SC), enabling them to adapt customised customer requirements with their rapidly changing behaviours, reduce inventory costs, and obtain competitive advantages in the market. However, traditional MTO-based scheduling approaches fa...

Products continuously evolve over time. Realizing the pattern of product family evolution along with proper estimation of features for future products has been regarded as a critical issue for business success. Focusing on this issue, a dynamic model for product family evolution combined with forecasting is proposed in this research work. The propo...

Feature Selection (FS) is an important preprocessing step that is involved in machine learning and data mining tasks for preparing data (especially high-dimensional data) by eliminating irrelevant and redundant features, thus reducing the potential curse of dimensionality of a given large dataset. Consequently, FS is arguably a combinatorial NP-har...

Since production efficiency and costs are directly affected by the ways in which jobs are scheduled, scholars have advanced a number of meta-heuristic algorithms to solve the job shop scheduling problem (JSSP). Although this JSSP is widely accepted as a computationally intractable NP-hard problem in combinatorial optimization, its solution is essen...

Deep learning-based super-resolution methods have shown great promise, especially for single image super-resolution (SISR) tasks. Despite the performance gain, these methods are limited due to their reliance on copious data for model training. In addition, supervised SISR solutions rely on local neighbourhood information focusing only on the featur...

This research utilizes an Object-Oriented Bayesian Network (OOBN) to model the relationships between the Sustainable Development Goal (SDGs) and resilience and sustainability at national, regional, and global levels. The ability of the OOBN to learn the parameters, i.e., the conditional probability distributions between the variables included in th...

This study introduces a bi-objective integrated supply chain (SC) scheduling (SCS) model to deal with the challenges of providing highly customized and on-time delivery requirements at the least cost. To address these real-life challenges, the model integrates the supply portfolio into production scheduling with a customer-imposed delivery time win...

The resource constrained project scheduling problem with discounted cash flows (RCPSPDC) is one of the most challenging problems owing to its NP-hard characteristics. This complex combinatorial optimization problem is most relevant to project management, building and construction management, and production planning. Although several solution method...

Recently, Code Quality (CQ) has become critical in a wide range of organizations and in many areas from academia to industry. CQ, in terms of readability, security, and testability, is a major goal throughout the software development process because it affects overall Software Quality (SQ) in terms of subsequent releases, maintenance, and updates....

Sustainable development goals (SDG) represent one of the pillars of the UN’s 2030 agenda aiming to secure a more sustainable future for all nations. The 17 goals comprising the SDG framework form a transformative action plan that is designed to help nations achieve a more sustainable future focusing on their environmental, social, and economic syst...

In make-to-order or engineer-to-order systems, the overarching process of producing complex and highly customized products along with managing multiple stakeholders (including suppliers) can be considered to be a project scheduling problem. Yet, despite the need for an advanced project scheduling plan for manufacturing, there is little research in...

div>Products continuously evolve over time. Realizing the pattern of product family evolution along with proper estimation of features for future products has been regarded as a critical issue for business success. Focusing on this issue, a dynamic model for product family evolution combined with forecasting is proposed in this research work. The p...

div>Products continuously evolve over time. Realizing the pattern of product family evolution along with proper estimation of features for future products has been regarded as a critical issue for business success. Focusing on this issue, a dynamic model for product family evolution combined with forecasting is proposed in this research work. The p...

In the era of sustainable development, green supplier selection has become a key component of supply chain management, as it considers criteria such as carbon footprint, water usage, energy usage and recycling capacity. Since the green supplier selection problem involves subjective criteria and uncertainty in preferences, it is well suited to using...

The rapid evolution of the Internet of Things (IoT) paradigm during the last decade has lead to its adoption in critical infrastructure. However, the multitude of benefits that are derived from the IoT paradigm are short-lived due to the exponential rise in the associated security and privacy threats. Adversaries carry out privacy-oriented attacks...

Systems thinking is recognized as an essential skill for understanding complex problem solving and decision making associated with many of the contemporary issues faced by individuals and communities. In this article, our goal is to contribute to the knowledge of curriculum and pedagogy of formal systems thinking teaching in higher education. We be...

Tourism makes a significant contribution to the economy of almost every country, so accurate demand forecasting can help in better planning for the government and a range of stakeholders involved in the tourism industry and can aid economic sustainability. Machine learning models, and in particular, deep neural networks, can perform better than tra...

Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accid...

In project planning, traditionally, the project managers first schedule the project activities, and then plan for materials ordering and supplier selection. This disintegrated approach lacks in planning co-ordination and causes a loss in expected profit for the organization. In this study, a concurrent model is proposed for resource constraint proj...

Convolutional neural networks (CNNs) have been commonly used in medical decision support systems to predict and diagnose different diseases with good precision. CNNs are extremely successful in developing health support systems because of their ability to identify relationships and hidden patterns in healthcare data. One of the most important and u...

This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance, redundancy, or less information; this pre-processing process is often known as feat...

In this article, the UXE-Type inverter is considered for eleven-level operation. This topology exhibits a boosting capability along with reduced switches and one source. An algorithm that utilizes the redundant states to control the voltage-balance of the auxiliary direct current (DC)-link is presented. The proposed control algorithm is capable of...

The widespread Internet of Things (IoT) technologies in day life indoor environments result in an enormous amount of daily generated data, which require reliable data analysis techniques to enable efficient exploitation of this data. The recent developments in deep learning (DL) have facilitated the processing and learning from the massive IoT data...

Traditional product architecture design is motivated towards structural-functional modularity by a flat map all-in-one (AIO) approach, providing little emphasis on interface complexity and addressing conflicting goals. The resultant modular product architecture (PA) often reduces design robustness and increases complexity for product assembly and r...

Accurate prediction of wind power generation is complex due to stochastic component, but can play a significant role in minimizing operating costs, and improving reliability and security of a power system. This paper proposes a hybrid deep learning model to accurately forecast the very-short-term (5-min and 10-min) wind power generation of the Boco...

The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting...

Accurate prediction of wind power generation is complex due to stochastic component, but can play a significant role in minimizing operating costs, and improving reliability and security of a power system. This paper proposes a hybrid deep learning model to accurately forecast the very-short-term (5-min and 10-min) wind power generation of the Boco...

Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure c...

This paper presents new and efficient modulation techniques applied on the recently introduced compact nine-level switched-capacitor inverter (C9LSCI). The paper also discusses the performance of the inverter under selective harmonic elimination (SHE) and mitigation (SHM) based on the heuristic Bat-Algorithm (BA) technique. Two new modulation techn...

The development of sustainable green buildings (GBs) is a major contribution to the preservation of the environment. Sustainable thinking in GB construction is not a supplementary element, but rather necessary to achieve the building’s functional, economic, and environmental efficiency in order to preserve resources and meet current and future need...

The Multi-dimensional Knapsack Problems (MKP) has been widely accepted as a challenging research topic due to its NP-hard nature. In this paper, a binary version of the recently developed slime mould algorithm (BSMA) is proposed to solve MKP. As SMA was originally proposed to solve continuous optimization problems, it is not applicable to solve the...

Optimum modeling of the proton exchange membrane fuel cell (PEMFC) has attracted considerable research over the last decades to simulate, control, evaluate, manage, and optimize the performance of PEMFC stacks. The main problem in optimal modeling is that the model parameters are not provided by manufacturers, and the empirical dataset points are n...

Modern information technology, such as the internet of things (IoT) provides a real-time experience into how a system is performing and has been used in diversified areas spanning from machines, supply chain, and logistics to smart cities. IoT captures the changes in surrounding environments based on collections of distributed sensors and then send...

Integration of project scheduling (PS) with materials ordering has received greater attention in the last three decades as an approach to ensure the profitability of a project. The fundamental concern of the material ordering integrated PS is to select the right supplier of the right material by placing an order at the right time so that the orderi...

In this work, the evaluation of the design and optimization of proposed offgrid hybrid microgrid systems for different load dispatch strategies is presented by assessing the component sizes, system responses and different cost analyses of the proposed system. This study optimizes the sizing of the Barishal and Chattogram (two popular divisions in B...

The demand for energy in Egypt has increased dramatically due to the steady increase in economic and societal development. To meet this need, the use of more renewable energy resources is an essential part of the solution to the ultimate shortage of energy. Because of the multitude of factors involved, the selection of the most suitable renewable e...

This paper develops a multi-operator based differential evolution with a communication strategy (MCDE) being integrated with a sequential Tabu Search (MCDE/TS) to solve the job shop scheduling problem (JSSP) with the objective of minimizing makespan. The three variants of DE which are implemented in the proposed algorithm evolve as independent sub-...

Project risks are mostly considered to be independent in risk management, ignoring interdependencies among them, which can lead to inappropriate risk assessment and reduced efficacy in risk treatment. This study introduces a new Monte Carlo simulation-based risk interdependency network model to support decision makers in assessing project risks and...

Conventional PI controllers are vulnerable to changes in parameters and are difficult to tune. In this work, an Artificial Neural Network (ANN) based controller is developed for the robust operation of a single-phase modified Packed U-Cell 5-level inverter (MPUC-5) for solar PV application under variable insolation conditions. An MPUC-5 is a conver...

This paper studies a resource constrained project scheduling problem (RCPSP) under dynamic environments. Along with stochastic activity-durations, resource requests and resource availabilities are also prone to vary with time (i.e., that is, dynamic). To account for stochastic activity durations and the resource dynamic, a chance constrained based...

Reverse logistics (RL) is considered the reverse manner of gathering and redeploying goods at the end of their lifetime span from consumers to manufacturers in order to reutilize, dispose, or remanufacture. Whereas RL has many economic benefits, it presents compromises to businesses that wish to remain competitive but be responsible global citizens...

Reductions in defense expenditure require holistic and coordinated planning of two critical and interconnected defense capabilities, namely a fleet of assets and the workforce required. In this paper, we model and solve a joint problem of strategic workforce planning and fleet renewal in a military context. The joint problem studied involves addres...

This article presents an 11-level operation of the WE (Wee)-Type inverter. The topology employs a single DC-source, has a reduced number of components, and exhibits a boosting capability. A voltage balancing algorithm is proposed where the inverter’s redundant states are employed to maintain the auxiliary DC-link voltage. It is shown that with the...

Although photovoltaic (PV) energy production offers several environmental and commercial advantages, the irregular nature of PV energy can challenge the design and development of the energy management systems. Precise forecasting for PV energy production is therefore of vital importance to supply consumers to improve trust in functionality of the e...

This review provides a comprehensive overview of the state-of-the-art methods of graph-based networks from a deep learning perspective. Graph networks provide a generalized form to exploit non-euclidean space data. A graph can be visualized as an aggregation of nodes and edges without having any order. Data-driven architecture tends to follow a fix...

The optimization of photovoltaic (PV) systems relies on the development of an accurate model of the parameter values for the solar/PV generating units. This work proposes a modified artificial jellyfish search optimizer (MJSO) with a novel premature convergence strategy (PCS) to define effectively the unknown parameters of PV systems. The PCS works...

Recently, a new strong optimization algorithm called marine predators algorithm (MPA) has been proposed for tackling the single-objective optimization problems and could dramatically fulfill good outcomes in comparison to the other compared algorithms. Those dramatic outcomes, in addition to our recently-proposed strategies for helping meta-heurist...

Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as...

The optimal operation of solar cells depends on the accurate determination of parameters in the Photovoltaic (PV) models, such as resistance and currents, which may vary due to unstable weathers conditions and equipment aging. The precise selection of these parameters resembles a multi-variable, nonlinear and multi-modal problem. Despite a few para...

In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow (OPF) problem and is called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution (DE) operator in a single algorithmic framework. Additionally, an adaptive method (AM) is proposed to update the number...

Although intelligent load forecasting is essential for optimal energy management (EM) in smart cities, there is a lack of current research exploring energy management in well-regulated Internet of Things (IoT) networks. This paper develops a new deep learning (DL) model for efficient forecasting of short-term energy consumption while maintaining ef...

Resource constrained project scheduling problems (RCPSPs) are complex optimization problems that aim to minimize project completion time after considering limited resources and precedence-related activities with known durations. However, due to the dynamic nature of real-world applications, activity durations are vulnerable to change. In addition,...

Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncert...