Increasingly complex variants of the vehicle routing problem with time windows (VRPTW) are coming into focus, alleviated with advances in the computing power. VRPTW is a combination of the classical travelling salesman and bin packing problems, with many real world applications in various fields – from physical resource manipulation planning to virtual resource management in the ever more popular cloud computing domain. The basis for many VRPTW approaches is a heuristic which builds a candidate solution that is subsequently improved by a search or optimization procedure. The choice of the appropriate heuristic may have a great impact on the quality of the obtained results. In this work genetic programming is used to evolve a suitable heuristic to build initial solutions for different objectives and classes of VRPTW instances. Additionally 2-phase parallel algorithm has been proposed to improve initial results obtained by genetic programming. Proposed solution is based on the divide and conquer paradigm, decomposing problem instances into smaller, mutually independent sub-problems which can be solved using traditional algorithms and integrated into a global solution of reasonably good quality. The results show great potential, since this method is applicable to different problem classes and user-defined performance objectives. It has been noticed that sometimes results for vehicle routing problem could not be used in real world applications, due to dynamic behaviour of transport systems (incidents or traffic congestion). Improving traffic control has been studied in this work. Solving traffic congestions represents a high priority issue in many big cities. Traditional traffic control systems are mainly based on pre-programmed, reactive and local techniques. This work presents an autonomic system that uses automated planning techniques instead. These techniques are easily configurable and modified, and can reason about the future implications of actions that change the default traffic lights behaviour. The proposed implemented system includes some autonomic properties, since it monitors the current traffic state, detects whether the system is degrading its performance, sets up new sets of goals to be achieved by the planner, triggers the planner that generates plans with control actions, and executes the selected courses of actions. The obtained results in several artificial and real world data-based simulation scenarios show that the proposed system can efficiently solve traffic congestion.