Short-Term Resource Allocation and Management

Almost all sectors of the economy, such as, government, healthcare, education, ship repair, construction, and manufacturing require project management. A key component of project management deals with scheduling of tasks such that limited resources are utilized in an effective manner. Current research on resource constrained project-scheduling has been classified as: a) Single project with single mode for various tasks, b) Single project with multiple task modes, c) Multiple projects with single task mode, and d) Multiple projects with multiple task modes.;This work extends the current multi-project, multi-mode scheduling techniques. The resources can be renewable, and non-renewable. In addition, it focuses on short term scheduling, that is, scheduling on an hourly, daily, or weekly basis. Long term scheduling assumes a stable system, that is, resources, priorities, and other constraints do no change during the scheduling period. In this research, short term scheduling assumes a dynamic system, that is, resources, priorities, and other constraints change over time.;A hybrid approach is proposed to address the dynamic nature of the problem. It is based on discrete event simulation and a set of empirical rules provided by the project manager. The project manager is assumed to be highly knowledgeable about the project. He/she is regarded as an integral part of the system. Such an approach is better suited to deal with real world scheduling. The proposed approach does not seek to provide a single optimum solution, instead, it generates a series of feasible solutions, along with the impact of each solution on schedule and cost.;Two project case studies dealing with finding an optimum solution were selected from the literature. The proposed technique was applied to the data set in these studies. In both cases the proposed approach found the optimum solution. The model was then applied to two additional problems to test the features that could not be tested on the dataset from the literature.;As for practical implications, the proposed approach enhances the decision making process, by providing more resource allocation flexibility, and results in improved solutions in terms of total project duration and cost. From an academic viewpoint, this research enriches the existing literature, as it provides an extension of the resource constrained project scheduling problems, a discrete event simulation and four cases studies which highlights relevant issues to model properly the complexity of real-life projects

Trends of Digital Transformation in the Shipbuilding Sector

The new paradigms of Industry 4.0 force all the industrial sectors to face a deep digital transformation in order to be on the edge in a competitive and globalized scenario. Following this trend, the shipbuilding industry has to establish its own path to adapt itself to the digital era. This chapter aims to explore this challenge and give an outlook on the multiple transformative technologies that are involved. For that reason, a case of study is presented as a starting point, in which the digital technologies that can be applied are easily recognized. A social network analysis (SNA) is developed among these key enabling technologies (KETs), in order to stress their correlations and links. As a result, artificial intelligence (AI) can be highlighted as a support to the other technologies, such as vertical integration of naval production systems (e.g., connectivity, Internet of things, collaborative robotics, etc.), horizontal integration of value networks (e.g., cybersecurity, diversification, etc.), and life cycle reengineering (e.g., drones, 3D printing (3DP), virtual and augmented reality, remote sensing networks, robotics, etc.)

Transferring best practices enabled by Building information modeling (BIM) in Architecture, Engineering and Construction (AEC) to shipbuilding industry: An explorative study

This thesis was carried out as a part of Triad 2014 - the joint master’s thesis project of Cruise and Ferry Experience program, Aalto University. Meridiem funded and actively supported Triad 2014. This research explored the best practices enabled by Building information modeling in Finnish AEC industry and the current state of 3D CAD tools in Finnish shipbuilding industry. Furthermore, discussions were carried out on whether these best BIM-enabled practices can be transferred to the shipbuilding industry. Data were collected by in-depth interviews with seven BIM experts and seven shipbuilding professionals in Finland. The top four BEPs mentioned by BIM experts were clash detection, visualization, quantity takeoff and scheduling. Through interviews with shipbuilding professionals it was found that the same CAD tool is used by different design disciplines in a shipbuilding project, i.e., the “one CAD” solution. In spite of benefits such as better design coordination and comprehensive collision detection, two major limitations of the solution, i.e., the lack of an open standard and the interior design is carried out with 2D CAD tools were identified. Compared with BIM, 3D CAD in shipbuilding industry can carry out more comprehensive collision detection. Besides, shipbuilding industry also has a longer history of utilizing object-oriented 3D modeling. Both 3D CAD and BIM can generate high-quality quantity takeoffs. Scheduling in shipbuilding is conducted by specialized project management software other than 3D CAD. For future research aiming at increasing the productivity of shipbuilding industry, the identified limitations of current 3D CAD tools can be good starting points

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209