Realistic vehicle routing problems have been highly relevant for years in a wide variety of domains. One such domain is food delivery, where well-crafted routes can reduce costs and contribute to customer satisfaction. This thesis formulates a problem variant for the restaurant meal delivery problem in order to examine the reoptimization of meal delivery routes. A novel solution algorithm that combines rule-based reinforcement learning and adaptive large neighbourhood search is used to tackle the problem. This hybrid algorithm manages to incorporate both learning and handcrafted search heuristics, as well as both offline and online computation. Analysis of the algorithmic components shows that the proposed algorithmic approach is computationally feasible for small scale reoptimization problems. Analysis also demonstrates that the reinforcement learning component currently does not improve performance with respect to the established objective function.

This paper investigates possible effects of different information deficiencies on the evolution of cooperation on the basis of the Iterative Prisoner’s Dilemma. To this end, an evolving pool of players repeatedly participates in Round-Robin tournaments. The effect of 5% lost, 5% wrong and 5% delayed information on the amount of games in which both players cooperate per generation, the average payoff received by players per generation and the total age of the player pool is examined. The method used to research the different information deficiencies is discussed extensively and suggestions for further research are given. The conducted research makes it apparent how information deficiencies can decrease the amount of games in which both players cooperate, the average payoff and how it can increase the total age of all players in the pool per generation. At the same time, it is observed how cooperative behavior is still present in all of the performed experiments.