Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Intelligent Multi-Attribute Decision Making Applications: Decision Support Systems for Performance Measurement, Evaluation and Benchmarking

Efficiency has been and continues to be an important attribute of competitive business environments where limited resources exist. Owing to growing complexity of organizations and more broadly, to global economic growth, efficiency considerations are expected to remain a top priority for organizations. Continuous performance evaluations play a significant role in sustaining efficient and effective business processes. Consequently, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency of various industries. Majority of these models focus solely on quantitative criteria omitting qualitative data. However, a thorough performance measurement and benchmarking require consideration of all available information since accurately describing and defining complex systems require utilization of both data types. Most evaluation models also function under the unrealistic assumption of evaluation criteria being dependent on one another. Furthermore, majority of these methodologies tend to utilize discrete and contemporary information eliminating historical performance data from the model environment. These shortcomings hinder the reliability of evaluation outcomes leading to inadequate performance evaluations for many businesses. This problem gains more significance for business where performance evaluations are tied in to important decisions relating to business expansion, investment, promotion and compensation. The primary purpose of this research is to present a thorough, equitable and accurate evaluation framework for operations management while filling the existing gaps in the literature. Service industry offers a more suitable platform for this study since the industry tend to accommodate both qualitative and quantitative performance evaluation factors relatively with more ease compared to manufacturing due to the intensity of customer (consumer) interaction. Accordingly, a U.S. based food franchise company is utilized for data acquisition and as a case study to demonstrate the applications of the proposed models. Compatible with their multiple criteria nature, performance measurement, evaluation and benchmarking systems require heavy utilization of Multi-Attribute Decision Making (MADM) approaches which constitute the core of this research. In order to be able to accommodate the vagueness in decision making, fuzzy values are also utilized in all proposed models. In the first phase of the study, the main and sub-criteria in the evaluation are considered independently in a hierarchical order and contemporary data is utilized in a holistic approach combining three different multi-criteria decision making methods. The cross-efficiency approach is also introduced in this phase. Building on this approach, the second phase considered the influence of the main and sub-criteria over one another. That is, in the proposed models, the main and sub-criteria form a network with dependencies rather than having a hierarchical relationship. The decision making model is built to extract the influential weights for the evaluation criteria. Furthermore, Group Decision Making (GDM) is introduced to integrate different perspectives and preferences of multiple decision makers who are responsible for different functions in the organization with varying levels of impact on decisions. Finally, an artificial intelligence method is applied to utilize the historical data and to obtain the final performance ranking. Owing to large volumes of data emanating from digital sources, current literature offers a variety of artificial intelligence and machine learning methods for big data analytics applications. Comparing the results generated by the ANNs, three additional well-established methods, viz., Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), are also employed for the same problem. In order to test the prediction capability of these methods, the most influencing criteria are obtained from the data set via Pearson Correlation Analysis and grey relational analysis. Subsequently, the corresponding parameters in each method are optimized via Particle Swarm Optimization to improve the prediction accuracy. The accuracy of artificial intelligence and machine learning methods are heavily reliant on large volumes of data. Despite the fact that several businesses, especially business that utilize social media data or on-line real-time operational data, there are organizations which lack adequate amount of data required for their performance evaluations simply due to the nature of their business. Grey Modeling (GM) technique addresses this issue and provides higher forecasting accuracy in presence of uncertain and limited data. With this motivation, a traditional multi-variate grey model is applied to predict the performance scores. Improved grey models are also applied to compare the results. Finally, the integration of the fractional order accumulation along with the background value coefficient optimization are proposed to improve accuracy

DECISION SUPPORT SYSTEM USING WEIGHTING SIMILARITY MODEL FOR CONSTRUCTING GROUND-TRUTH DATA SET

This research aims to form a ground-truth dataset in the entity-matching process used to detect duplication of records in a bibliographic database. The contribution of this research is the obtained dataset which can be used as reference in measuring and evaluating the entity matching model implemented in bibliographic databases. This aim was achieved by developing a decision support system through experts who act as decision makers in the bibliographic databases field to construct ground-truth datasets. The model used in this decision support system weights similarity by comparing each attribute of the pairwise record in the dataset. An expert who understands all characteristics of the research database can use the graphical user interface to evaluate and determine the pairwise record that meets the conditions, such as duplication of records. This research produces a ground-truth dataset using the decision support system approach

Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of selected features have been adopted to train four machine-learning based classifiers.  The two sets of selected features are based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) approach respectively.  These evolutionary-based algorithms are known to be effective in solving optimization problems.  The classifiers used in this study are Naïve Bayes, k-Nearest Neighbor, Decision Tree and Support Vector Machine that have been trained and tested using the NSL-KDD dataset. The performance of the abovementioned classifiers using different features values was evaluated.  The experimental results indicate that the detection accuracy improves by approximately 1.55% when implemented using the PSO-based selected features than that of using GA-based selected features.  The Decision Tree classifier that was trained with PSO-based selected features outperformed other classifiers with accuracy, precision, recall, and f-score result of 99.38%, 99.36%, 99.32%, and 99.34% respectively.  The results show that using optimal features coupling with a good classifier in a detection system able to reduce the classifier model building time, reduce the computational burden to analyze data, and consequently attain high detection rate

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

Positioning of a wireless relay node for useful cooperative communication

Given the exorbitant amount of data transmitted and the increasing demand for data connectivity in the 21st century, it has become imperative to search for pro-active and sustainable solutions to the effectively alleviate the overwhelming burden imposed on wireless networks. In this study a Decode and Forward cooperative relay channel is analyzed, with the employment of Maximal Ratio Combining at the destination node as the method of offering diversity combining. The system framework used is based on a three-node relay channel with a source node, relay node and a destination node. A model for the wireless communications channel is formulated in order for simulation to be carried out to investigate the impact on performance of relaying on a node placed at the edge of cell. Firstly, an AWGN channel is used before the effect of Rayleigh fading is taken into consideration. Result shows that performance of cooperative relaying performance is always superior or similar to conventional relaying. Additionally, relaying is beneficial when the relay is placed closer to the receiver