Models and Algorihtm for the Optimization of Real-World Routing and Logistics Problems

Logistics involves planning, managing, and organizing the flows of goods from the point of origin to the point of destination in order to meet some requirements. Logistics and transportation aspects are very important and represent a relevant costs for producing and shipping companies, but also for public administration and private citizens. The optimization of resources and the improvement in the organization of operations is crucial for all branches of logistics, from the operation management to the transportation. As we will have the chance to see in this work, optimization techniques, models, and algorithms represent important methods to solve the always new and more complex problems arising in different segments of logistics. Many operation management and transportation problems are related to the optimization class of problems called Vehicle Routing Problems (VRPs). In this work, we consider several real-world deterministic and stochastic problems that are included in the wide class of the VRPs, and we solve them by means of exact and heuristic methods. We treat three classes of real-world routing and logistics problems. We deal with one of the most important tactical problems that arises in the managing of the bike sharing systems, that is the Bike sharing Rebalancing Problem (BRP). We propose models and algorithms for real-world earthwork optimization problems. We describe the 3DP process and we highlight several optimization issues in 3DP. Among those, we define the problem related to the tool path definition in the 3DP process, the 3D Routing Problem (3DRP), which is a generalization of the arc routing problem. We present an ILP model and several heuristic algorithms to solve the 3DRP.

Interoperability Challenges in Internet of Things Systems: a Service-Oriented Computing Approach

Internet of Things systems are pervasive systems evolved from cyber-physical to large-scale systems. Due to the number of technologies involved, software development involves several integration challenges. Among them, the ones preventing proper integration are those related to the system heterogeneity, and thus addressing interoperability issues. From a software engineering perspective, developers mostly experience the lack of interoperability in the two phases of software development: programming and deployment. On the one hand, modern software tends to be distributed in several components, each adopting its most-appropriate technology stack, pushing programmers to code in a protocol- and data-agnostic way. On the other hand, each software component should run in the most appropriate execution environment and, as a result, system architects strive to automate the deployment in distributed infrastructures. This dissertation aims to improve the development process by introducing proper tools to handle certain aspects of the system heterogeneity. Our effort focuses on three of these aspects and, for each one of those, we propose a tool addressing the underlying challenge. The first tool aims to handle heterogeneity at the transport and application protocol level, the second to manage different data formats, while the third to obtain optimal deployment. To realize the tools, we adopted a linguistic approach, i.e.\ we provided specific linguistic abstractions that help developers to increase the expressive power of the programming language they use, writing better solutions in more straightforward ways. To validate the approach, we implemented use cases to show that the tools can be used in practice and that they help to achieve the expected level of interoperability. In conclusion, to move a step towards the realization of an integrated Internet of Things ecosystem, we target programmers and architects and propose them to use the presented tools to ease the software development process.