A proxy for reliable 5G (and beyond) mmWave communications. Contributions to multi-path scheduling for a reliability focused mmWave proxy

Reliable, consistent and very high data rate mobile communication will become especially important for future services such as, among other things, future emergency communication needs. MmWave technology provides the needed capacity, however, lacks the reliability due to the abrupt capacity changes any one path experiences. Intelligently making use of varying numbers of available mmWave paths, efficiently scheduling data across the paths, perhaps even through multi-operator agreements; and balancing mobile power consumption with path costs and the need for reliable consistent quality will be critical to attaining this aim. In this thesis, the multipath scheduling problem in a mmWave proxy when the paths have dynamically changing path characteristics is considered. To address this problem, a hybrid scheduler is proposed, the performance of which is compared with the Round Robin scheduler, Random scheduler and the Highest Capacity First scheduler. Forward error correction is explored as a means of enhancing the scheduling. Keywords:Multipath Scheduling, mmWave Proxy, Forward Error Correction, beyond 5G

Production and Logistics Systems Improvements - Biim Ultrasound AS

A crucial aspect of the supply chain network design process is deciding on optimal locations to situate new facilities. Facility location decisions rely on many factors, some of which might be conflicting with each other. The decision factors can be either quantitative or qualitative, thus a brute-force prioritization of one over another could be detrimental overall. To ensure the efficacy of the selection process, decision makers must consider both the quantitative and qualitative factors in tandem. Some of the common methods employed in the literature by organizations to facilitate their decision-making process include: optimization models and algorithms, decision support systems and computerized analytics tools. To this end, this thesis proposes a hybrid Multi-Criteria Decision Making (MCDM) model to aid the selection of an optimal location that suits the strategic fit of an organization. The proposed model integrates the Analytic Hierarchy Process (AHP) methodology for Multi-Attribute Decision Making (MADM) with Mixed Integer Programming (MIP). The solution is modeled and implemented with the AIMMS modeling language as well as the Gurobi Optimization tool in Python. This thesis work is based on a case study from Biim Ultrasound

Production and Logistics Systems Improvements - Biim Ultrasound AS

A crucial aspect of the supply chain network design process is deciding on optimal locations to situate new facilities. Facility location decisions rely on many factors, some of which might be conflicting with each other. The decision factors can be either quantitative or qualitative, thus a brute-force prioritization of one over another could be detrimental overall. To ensure the efficacy of the selection process, decision makers must consider both the quantitative and qualitative factors in tandem. Some of the common methods employed in the literature by organizations to facilitate their decision-making process include: optimization models and algorithms, decision support systems and computerized analytics tools. To this end, this thesis proposes a hybrid Multi-Criteria Decision Making (MCDM) model to aid the selection of an optimal location that suits the strategic fit of an organization. The proposed model integrates the Analytic Hierarchy Process (AHP) methodology for Multi-Attribute Decision Making (MADM) with Mixed Integer Programming (MIP). The solution is modeled and implemented with the AIMMS modeling language as well as the Gurobi Optimization tool in Python. This thesis work is based on a case study from Biim Ultrasound