In order to maximize the capacities of resources constraints in the multiple projects environment, it is firstly necessary to make sure where the resources constraints are, and to schedule them. And then, add a set of time buffer to protect the bottle-neck resources. For some purposes, the project schedule is not easy to be altered in enterprises. Instead of adding a set of time buffer, we use others ways to protect resource constraints and to improve capacities. 4 cases are discussed in this research respectively, and the characteristics in this research contain whether the project plan is announced at the year beginning, whether the number of team members is fixed, and whether the project is outsourced or in-house. The main purpose is to investigate how the enterprises arrange the resources in 3 different periods: the projects at the year beginning, new projects joined during a year , and new demands in the existing projects during a year. The research results show: (1) Enterprises usually recruit employees at the beginning of the year, which prevents from the unqualified human resource as the projects going. (2) The teams with fixed member are allocated members in the projects which are the same domain. It¡¦s not easy to support between projects of different domains in the same team, except IT support. It¡¦s also difficult to support between teams, because the relationship of teams is competitive. (3) In the established team in terms of projects, enterprises assign team members in project which are the same domain by the function. It¡¦s easy to support between members with the same domain. It¡¦s not easy to support between members with different domain, except IT support. (4) Carrying out outsourcing projects in the enterprises, complete project in the different period to explore the resource constraints. (5) Carrying out in-house projects in the enterprise, reduce insignificance project scope or to reschedule insignificance project to explore the resource constraints. (6) Carrying outsourcing and in-house projects in the enterprises, reduce insignificance project scope or to reschedule insignificance project to explore the resource constraints.

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Critical industrial applications in embedded field devices require reliability and consistency. Cloudbased services have been gaining attraction in embedded field devices for monitoring, optimization, predictive maintenance, and other supporting use cases. A significant challenge persists in enabling cloud-connection to the embedded field devices. The central issues on this matter are diversity, resource constraint, and the critical applications of these devices. This paper proposes a novel concept for enabling cloud connection to these devices. A dedicated software module, μConnector, has been introduced for cloud-related activities. It operates on Zephyr RTOS. The purpose of μConnector is to create a separation between critical and cloud related applications within the embedded field devices. μConnector is designed to be application-agnostic while being independent of vendor selection for hardware components. The scientific contribution of the paper lies in the introduction of μConnector. The presented concept addresses the challenges associated with cloud connectivity for embedded field devices. Its primary objective is to define architectural decisions guiding the implementation of the proposed software module.

In this thesis, meta-heuristic algorithms are developed to obtain optimum or near optimum solutions for the time-cost-resource trade-off and resource leveling problems in project scheduling. Time cost trade-off, resource leveling, single-mode resource constrained project scheduling, multi-mode resource constrained project scheduling and resource constrained time cost trade-off problems are analyzed. Genetic algorithm simulated annealing, quantum simulated annealing, memetic algorithm, variable neighborhood search, particle swarm optimization, ant colony optimization and electromagnetic scatter search meta-heuristic algorithms are implemented for time cost trade-off problems with unlimited resources. In this thesis, three new meta-heuristic algorithms are developed by embedding meta-heuristic algorithms in each other. Hybrid genetic algorithm with simulated annealing presents the best results for time cost trade-off. Resource leveling problem is analyzed by five genetic algorithm based meta-heuristic algorithms. Apart from simple genetic algorithm, four meta-heuristic algorithms obtained same schedules obtained in the literature. In addition to this, in one of the test problems the solution is improved by the four meta-heuristic algorithms. For the resource constrained scheduling problems / genetic algorithm, genetic algorithm with simulated annealing, hybrid genetic algorithm with simulated annealing and particle swarm optimization meta-heuristic algorithms are implemented. The algorithms are tested by using the project sets of Kolisch and Sprecher (1996). Genetic algorithm with simulated annealing and hybrid genetic algorithm simulated annealing algorithm obtained very successful results when compared with the previous state of the art algorithms. 120-activity multi-mode problem set is produced by using the single mode problem set of Kolisch and Sprecher (1996) for the analysis of resource constrained time cost trade-off problem. Genetic algorithm with simulated annealing presented the least total project cost.

A hybrid methodology of game theory and Monte Carlo Tree Search was developed and the hybrid methodology was tested with various case studies through the nurse scheduling problem to show that it was able to form Pareto front dominance solutions, finding feasible solutions that were optimal and finding feasible partial solutions in over-constrained problems. The performance comparison was carried out with the Genetic Algorithm on the Resident Physician Scheduling problem and showed that the hybrid methodology was able to produce better quality solutions compared to the state of the art approach.