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This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based micro-simulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

The improvement of the quality of design and the reduction of failure related cost is seen as a crucial competitive requirement for UK manufacturing industry. To achieve these goals, industry must adopt current methods in support of design for quality (DFQ) for analysing potential problems and predicting quality, and integrate these effectively with the appropriate stages of their new product development process. The utilisation and success rate of these techniques in UK companies is, however, relatively low compared to those in countries such as the USA and Japan. In this paper, the fundamental concepts and key areas of opportunity in design improvement using the main DFQ support techniques are reviewed and a framework for their application and integration is presented to support concurrent product development. The typical experiences and problems concerning the application and implementation of techniques are discussed and areas where new research should be directed are touched on so that DFQ techniques may better enhance industrial practice in the achievement of high quality products.

The purpose of this paper is to examine the structural and computational potentials of a powerful class of neural networks (NNs), called multiple-valued logic neural networks (MVLNN), for predicting the behavior of phenomenological systems with highly nonlinear dynamics. MVLNNs are constructed based on the integration of a number of neurons working based on the principle of multiple-valued logics. MVLNNs possess some particular features, namely complex-valued weights, input, and outputs coded by kth roots of unity, and a continuous activation as a mean for transferring numbers from complex spaces to trigonometric spaces, which distinguish them from most of the existing NNs.

The presented study can be categorized into three sections. At the first part, the authors attempt at providing the mathematical formulations required for the implementation of ARX-based MVLNN (AMVLNN). In this context, it is indicated that how the concept of ARX can be used to revise the structure of MVLNN for online applications. Besides, the stepwise formulation for the simulation of Chua’s oscillatory map and multiple-valued logic-based BP are given. Through an analysis, some interesting characteristics of the Chua’s map, including a number of possible attractors of the state and sequences generated as a function of time, are given.

Based on a throughout simulation as well as a comprehensive numerical comparative study, some important features of AMVLNN are demonstrated. The simulation results indicate that AMVLNN can be employed as a tool for the online identification of highly nonlinear dynamic systems. Furthermore, the results show the compatibility of the Chua’s oscillatory system with BP for an effective tuning of the synaptic weights. The results also unveil the potentials of AMVLNN as a fast, robust, and efficient control-oriented model at the heart of NMPC control schemes.

This study presents two innovative propositions. First, the structure of MVLNN is modified based on the concept of ARX system identification programming to suit the base structure for coping with chaotic and highly nonlinear systems. Second, the authors share the findings about the learning characteristics of MVLNNs. Through an exhaustive comparative study and considering different rival methodologies, a novel and efficient double-stage learning strategy is proposed which remarkably improves the performance of MVLNNs. Finally, the authors describe the outline of a novel formulation which prepares the proposed AMVLNN for applications in NMPC controllers for dynamic systems.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

The purpose of this paper is twofold. First, it is to introduce a knowledge‐based system for planning processes for families of final products, instead of component items, be they parts or assemblies. Second, it is to demonstrate the feasibility and potential of a prototypical system developed for planning processes families for truck families from a multinational company.

The authors first identify the challenges in planning process families, including data and knowledge representation and constraint handling. To accommodate these challenges, the paper adopts the integrated product and process structure (IP²S) and colored timed Petri nets (CTPNs) in the proposed knowledge‐based process family planning system. On top of the IP²S and CTPNs, XML‐based knowledge representation is employed to alleviate the difficulties in modelling complex product and process family data and planning knowledge while enabling information exchange across different operating platforms. In addition, in accordance with the correspondence between PNs and knowledge‐based systems, a mechanism is designed to cope with the generation of production rules, which model constraints.

The proposed system is able to automatically generate production processes for customized products. At a higher level, such production processes provide input (e.g. operations, machines) to downstream activities for planning process details to manufacture component parts or component assemblies.

Traditional trial and error approaches to planning processes limit production performance improvement when companies need to timely produce diverse customized products. Knowledge‐based systems should be developed to help companies better plan production processes based on the available manufacturing resources.

Unlike most reported studies addressing either detailed process planning or assembly planning for component parts or component assemblies, this study tackles process planning for final products, in attempting to maintain production efficiency from a holistic view.

The purpose of this paper is to propose that the grey tolerance rough set (GTRS) and construct the GTRS-based classifiers.

The authors use grey relational analysis to implement a relationship-based similarity measure for tolerance rough sets.

The proposed classification method has been tested on several real-world data sets. Its classification performance is comparable to that of other rough-set-based methods.

The authors design a variant of a similarity measure which can be used to estimate the relationship between any two patterns, such that the closer the relationship, the greater the similarity will be.

The purpose of this paper is to demonstrate the application of vendor rationalization strategy for streamlining the supplies and manufacturing cycle time reduction in an Indian engineer-to-order (ETO) company. ETO firms are known for a large number of vendors, co-ordination hassles, rework problems and its impact on cycle time and operational excellence.

The research demonstrates the case-based application of Kraljic’s matrix for supply and leverages items, on-the-job observations, field visits, discussions and analysis of supplies reports.

The study guides on the rationalization of supplies and the necessary strategic alignments that can significantly reduce supply risk, costs, manufacturing and delivery cycle time along with co-ordination hassles. The study depicts the challenges of ETO environment with respect to supplies, and demonstrates the effectiveness of vendor rationalization application for the case company and weaknesses of commonly practiced vendor management approaches.

To be competitive, companies should rationalize supply items and vendors based on the nature of items and their subsequent usage by applying Kraljic’s matrix-based classification. The immediate implication of vendor rationalization is misunderstood as reducing supply base, but it does much more and includes review of supplies, nature of items and strategic alignments, leading to win-win situation for company and suppliers.

For the rationalization of supplies, while procuring and dealing with vendors, executives should envisage engineering nature of components, considering cross-functional requirements and integration of components in context to ETO products/projects environments. There is a dearth of studies focusing on vendor rationalization aspects in ETO setups in fast-developing country context.