Motivated by recent developments to deploy collaborative robots in industrial production systems, we investigate the assembly line balancing problem with collaborative robots. The problem is characterized by the possibility that human and robots can simultaneously execute tasks at the same workpiece either in parallel or in collaboration. For this novel problem type, we present a mixed-integer programming formulation for balancing and scheduling of assembly lines with collaborative robots. The model decides on both the assignment of collaborative robots to stations and the distribution of workload to workers and robotic partners, aiming to minimize the cycle time. Given the high problem complexity, a hybrid genetic algorithm is presented as a solution procedure. Based on extensive computational experiments, the algorithm reveals promising results in both computational time and solution quality. Moreover, the results indicate that substantial productivity gains can be utilized by deploying collaborative robots in manual assembly lines. This holds especially true for a high average number of robots and tasks to be assigned to every station as well as a high portion of tasks that can be executed by the robot and in collaboration.

Meeting the 21st century’s challenges of climate change and scarcity of crude oil requires a better understanding of the forces that ensure a successful market introduction of electric vehicles. Against this background, we develop a hybrid simulation approach to estimate the evolution of market shares of electric vehicles. The approach integrates a system dynamics model with an agent-based discrete choice model. System dynamics is used to model the interdependencies between consumer choice, consumer awareness, evolution of powertrain technologies, and service station availability on the macro level. By integrating the agent-based discrete choice model we refine the model parts of consumer choice and consumer awareness. This allows for considering individual consumer choices. Based on real-world data, we apply the model to the established and saturated German market, where replacement purchases dominate. This way we study the interdependencies between the evolution on the macro level and (1) individual replacement purchases as well as (2) heterogeneous consumer behavior. We show that both effects have a clear influence on the market share evolution of electric vehicles. The results indicate that neglecting individual consumer choices in aggregated system models may lead to systematically wrong estimations of the evolution of the market shares of electric vehicles.