An Interval-Valued Approach to Business Process Simulation Based on Genetic Algorithms and the BPMN

Development of E-Service Provision System Architecture Based on IoT and WSNs for Monitoring and Management of Freight Intermodal Transportation

The problems of developing intelligent service provision systems face difficulties in the representation of dynamic aspects of cargo transportation processes and integration of different and heterogeneous ICT components to support the systems' necessary functionality. This research aims to develop the architecture of the e-service provision system that can help in traffic management, coordination of works at trans-shipment terminals, and provide intellectual service support during intermodal transportation cycles. The objectives concern the secure application of the Internet of Things (IoT) technology and wireless sensor networks (WSNs) to monitor transport objects and context data recognition. The means for safety recognition of moving objects by integrating them with the infrastructure of IoT and WSNs are proposed. The architecture of the construction of the e-service provision system is proposed. The algorithms of identification, authentication, and safety connection of moving objects into an IoT platform are developed. The solution of application of blockchain mechanisms for the identification of stages of identification of moving objects is described by analysing ground transport. The methodology combines a multi-layered analysis of intermodal transportation with extensional mechanisms of identification of objects and methods of synchronization of interactions between various components. Adaptable e-service provision system architecture properties are validated during the experiments with NetSIM network modelling laboratory equipment and show their usability.

Business Process Simulation on Procedural Graphical Process Models

Business process simulation marks an essential technique for analyzing business processes and for reasoning about process improvement. With first contributions dating back to the mid-1990s, computerized business process simulation has been a continuing research focus and is widely acknowledged as foundational to Business Process Management research and practice. Reviewing contributions to the field published between 1990 and 2018, the authors assess the state of research on business process simulation and develop an organizing overview of research contributions discussing simulation approaches, tool support, results visualization, use context, application purposes, and adoption barriers. Findings inform future research on business process simulation by discussing paths for behavioral research on the use of business process simulation, user requirements, and adoption barriers as well as complementary paths for design science research addressing limitations of present approaches and simulation tool support.

An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques

Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary computation that plays a significant role in obtaining meaningful solutions to complex problems with large search space. GAs involve three fundamental operations after creating an initial population, namely selection, crossover, and mutation. The first task in GAs is to create an appropriate initial population. Traditionally GAs with randomly selected population is widely used as it is simple and efficient; however, the generated population may contain poor fitness. Low quality or poor fitness of individuals may lead to take long time to converge to an optimal (or near-optimal) solution. Therefore, the fitness or quality of initial population of individuals plays a significant role in determining an optimal or near-optimal solution. In this work, we propose a new method for the initial population seeding based on linear regression analysis of the problem tackled by the GA; in this paper, the traveling salesman problem (TSP). The proposed Regression-based technique divides a given large scale TSP problem into smaller sub-problems. This is done using the regression line and its perpendicular line, which allow for clustering the cities into four sub-problems repeatedly, the location of each city determines which category/cluster the city belongs to, the algorithm works repeatedly until the size of the subproblem becomes very small, four cities or less for instance, these cities are more likely neighboring each other, so connecting them to each other creates a somehow good solution to start with, this solution is mutated several times to form the initial population. We analyze the performance of the GA when using traditional population seeding techniques, such as the random and nearest neighbors, along with the proposed regression-based technique. The experiments are carried out using some of the well-known TSP instances obtained from the TSPLIB, which is the standard library for TSP problems. Quantitative analysis is carried out using the statistical test tools: analysis of variance (ANOVA), Duncan multiple range test (DMRT), and least significant difference (LSD). The experimental results show that the performance of the GA that uses the proposed regression-based technique for population seeding outperforms other GAs that uses traditional population seeding techniques such as the random and the nearest neighbor based techniques in terms of error rate, and average convergence.