Simulation analysis for integrated container terminal activities

Analisi simualtiva delle attività  logistiche di un container terminal con utilizzo del software Arenaope

An Agent-based Approach for Improving the Performance of Distributed Business Processes in Maritime Port Community

In the recent years, the concept of “port community” has been adopted by the maritime transport industry in order to achieve a higher degree of coordination and cooperation amongst organizations involved in the transfer of goods through the port area. The business processes of the port community supply chain form a complicated process which involves several process steps, multiple actors, and numerous information exchanges. One of the widely used applications of ICT in ports is the Port Community System (PCS) which is implemented in ports in order to reduce paperwork and to facilitate the information flow related to port operations and cargo clearance. However, existing PCSs are limited in functionalities that facilitate the management and coordination of material, financial, and information flows within the port community supply chain. This research programme addresses the use of agent technology to introduce business process management functionalities, which are vital for port communities, aiming to the enhancement of the performance of the port community supply chain. The investigation begins with an examination of the current state in view of the business perspective and the technical perspective. The business perspective focuses on understanding the nature of the port community, its main characteristics, and its problems. Accordingly, a number of requirements are identified as essential amendments to information systems in seaports. On the other hand, the technical perspective focuses on technologies that are convenient for solving problems in business process management within port communities. The research focuses on three technologies; the workflow technology, agent technology, and service orientation. An analysis of information systems across port communities enables an examination of the current PCSs with regard to their coordination and workflow management capabilities. The most important finding of this analysis is that the performance of the business processes, and in particular the performance of the port community supply chain, is not in the scope of the examined PCSs. Accordingly, the Agent-Based Middleware for Port Community Management (ABMPCM) is proposed as an approach for providing essential functionalities that would facilitate collaborative planning and business process management. As a core component of the ABMPCM, the Collaborative Planning Facility (CPF) is described in further details. A CPF prototype has been developed as an agent-based system for the domain of inland transport of containers to demonstrate its practical effectiveness. To evaluate the practical application of the CPF, a simulation environment is introduced in order to facilitate the evaluation process. The research started with the definition of a multi-agent simulation framework for port community supply chain. Then, a prototype has been implemented and employed for the evaluation of the CPF. The results of the simulation experiments demonstrate that our agent-based approach effectively enhances the performance of business process in the port community

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

Second Annual Workshop on Space Operations Automation and Robotics (SOAR 1988)

Papers presented at the Second Annual Workshop on Space Operation Automation and Robotics (SOAR '88), hosted by Wright State University at Dayton, Ohio, on July 20, 21, 22, and 23, 1988, are documented herein. During the 4 days, approximately 100 technical papers were presented by experts from NASA, the USAF, universities, and technical companies. Panel discussions on Human Factors, Artificial Intelligence, Robotics, and Space Systems were held but are not documented herein. Technical topics addressed included knowledge-based systems, human factors, and robotics

Risk Management

Every business and decision involves a certain amount of risk. Risk might cause a loss to a company. This does not mean, however, that businesses cannot take risks. As disengagement and risk aversion may result in missed business opportunities, which will lead to slower growth and reduced prosperity of a company. In today's increasingly complex and diverse environment, it is crucial to find the right balance between risk aversion and risk taking. To do this it is essential to understand the complex, out of the whole range of economic, technical, operational, environmental and social risks associated with the company's activities. However, risk management is about much more than merely avoiding or successfully deriving benefit from opportunities. Risk management is the identification, assessment, and prioritization of risks. Lastly, risk management helps a company to handle the risks associated with a rapidly changing business environment