Context. Due to increase in demand for RORO shipping services,the RORO terminal gate system need to handle more number of vehicles for every RORO vessel departure. Therefore, various congestion problems can occur; so, to address all possible congestion related problemsat RORO terminal, terminal gate systems are implemented with advanced technologies and updated to full or partial functioning automated gate systems. Objectives. In this research study considering the future increase in demand for wheeled cargo shipping, we attempt to propose a solution for reducing congestion and investigating optimal positions for each automated gate system service at RORO port terminal. Methods. In this Master thesis, as part of qualitative study we conduct a literature review and case study to know about the existing related work on this research problem and know about the real world system operation and behaviour of a RORO terminal gate system.Later, applying the adequate knowledge acquired from above mentioned qualitative studies, we perform a discrete event simulation experiment using Anylogic® professional 7.02 simulation software to address defined research objectives. Results. Considering the peak and low periods of present and future estimated demand volumes as different scenarios,various simulation experiment results are generated for different key performance indicators. The result values of these key performance indicators address various research objectives. Conclusions. This research study finally concludes that, the average queue length values at each automated gate system service implicates optimal position for each service and directly address the congestion problem. We also conclude that in every estimated increase in vehicles attending the RORO terminal, assigning optimal arrival time windows for respective vehicle types minimizes the congestion problem at automated gate system.

This research investigates and develops a new approach to the management of service quality with the emphasis on patient and staff satisfaction in the healthcare sector. The challenge of measuring the quality of service in healthcare requires us to view the problem from multiple perspectives. At the philosophical level, the true nature of quality is still debated; at the psychological level, an accurate conceptual representation is problematic; whilst at the physical level, an accurate measurement of the concept still remains elusive to practitioners and academics. This research focuses on the problem of quality measurement in the healthcare sector. The contributions of this research are fourfold: Firstly, it argues that from the technological point of view the research to date into quality of service in healthcare has not considered methods of real-time measurement and monitoring. This research identifies the key elements that are necessary for developing a real-time quality monitoring system for the healthcare environment.Secondly, a unique index is proposed for the monitoring and improvement of healthcare performance using information-theoretic entropy formalism. The index is formulated based on five key performance indicators and was tested as a Healthcare Quality Index (HQI) based on three key quality indicators of dignity, confidence and communication in an Accident and Emergency department. Thirdly, using an M/G/1 queuing model and its underlying Little’s Law, the concept of Effective Satisfaction in healthcare has been proposed. The concept is based on a Staff-Patient Satisfaction Relation Model (S-PSRM) developed using a patient satisfaction model and an empirically tested model developed for measuring staff satisfaction with workload (service time). The argument is presented that a synergy between patient satisfaction and staff satisfaction is the key to sustainable improvement in healthcare quality. The final contribution is the proposal of a Discrete Event Simulation (DES) modelling platform as a descriptive model that captures the random and stochastic nature of healthcare service provision process to prove the applicability of the proposed quality measurement models.