Dynamic Perspectives on Managerial Decision Making: Essays in Honor of Richard F. Hartl

This volume collects research papers addressing topical issues in economics and management with a particular focus on dynamic models which allow to analyze and foster the decision making of firms in dynamic complex environments. The scope of the contributions ranges from daily operational challenges firms face to strategic choices in dynamic industry environments and the analysis of optimal growth paths. The volume also highlights recent methodological developments in the areas of dynamic optimization, dynamic games and meta-heuristics, which help to improve our understanding of (optimal) decision making in a fast evolving economy

Nature-inspired metaheuristics for multiobjective activity crashing

Many project tasks and manufacturing processes consist of interdependent time-related activities that can be represented as networks. Deciding which of these sub-processes should receive extra resources to speed up the whole network (i.e., where activity crashing should be applied) usually involves the pursuit of multiple objectives amid a lack of a priori preference information. A common decision support approach lies in first determining efficient combinations of activity crashing measures and then pursuing an interactive exploration of this space. As it is impossible to exactly solve the underlying multiobjective combinatorial optimization problem within a reasonable computation time for real-world problems, we have developed proper solution procedures based on three major (nature-inspired) metaheuristics. This paper describes these implementations, discusses their strengths, and provides results from computational experiments.Heuristics Multicriteria Decision making Project management

Skiba phenomena in Markov perfect equilibria of asymmetric differential games

This paper examines the existence of Markov-Perfect-Equilibria that give rise to coexisting locally stable steady states in asymmetric differential games. The strategic interactions between an incumbent in a market and a potential competitor, which tries to enter the market through product innovation, are considered. Whereas the potential entrant invests in the build-up of a knowledge stock, which is essential for product innovation, the incumbent tries to reduce this stock through interference activities. It is shown that in the presence of upper bounds on investment activities of both firms a Markov-Perfect-Equilibrium exists under which, depending on the initial conditions, the knowledge stock converges either to a positive steady state, thereby inducing an entry probability of one, or to a steady state with zero knowledge of the potential entrant. In the later case the entry probability is close to zero. It is shown that this Markov-Perfect-Equilibrium is characterized by a discontinuous value function for the incumbent and it is discussed that this feature is closely related to the existence of upper bounds on the investments of the players. Removing these constraints in general jeopardizes the existence of a Markov-Perfect-Equilibrium with multiple locally stable steady states

Experimental Analysis of Pheromone-based Heuristic Column Generation using irace

Abstract. Pheromone-based heuristic column generation (ACO-HCG) is a hybrid algorithm that combines ant colony optimization and a MIP solver to tackle vehicle routing problems (VRP) with black-box feasibility. Traditionally, the experimental analysis of such a complex algorithm has been carried out manually by trial and error. Moreover, a full-factorial statistical analysis is infeasible due to the large number of parameters and the time required for each algorithm run. In this paper, we first automatically configure the algorithm parameters by using an automatic algorithm configuration tool. Then, we perform a basic sensitivity analysis of the tuned configuration in order to understand the significance of each parameter setting. In this way, we avoid wasting effort analyzing parameter settings that do not lead to a high-performing algorithm. Finally, we show that the tuned parameter settings improve the performance of ACO-HCG on the multipile VRP and the three-dimensional loading capacitated VRP.