Abstract We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time.
Advancements in on-demand power management of renewable energy can be achieved by multi-agent systems. This paper proposes an innovative approach where a population of autonomous agents are able to cooperate in managing an accumulator-bank in order to effectively deliver energy in places where it is required. The distributed and adaptive multi-agent approach is able to decrease the interferences by avoiding the negative interactions and conflicts, using the cooperation among agents. Our method uses the learning ability of agents to minimize the number of communications among agents and the central unit. This adaptive behavior lets the agents minimize the time to find the optimal routes during the search. A simulation env i- ronment has also been developed for visualizing the movements of the agents and the conflict situations. The operation and t he efficiency of the algorithm have been investigated using sim ple case studies. Keywords-renewable energy; agent; genetic algorithm (GA); cooperation
One of the actual topics on robotis research in the recent decades is the robots' autonomy. The methods of self-sufficient problem-solving of the machines brings on several questions in programming, so mobile robots started to extend as tools of education as well.
Our final goal was in this project to create the model of an automata depository that constitutes a closed system from the users' point of view. We model such circumstances that make autonomy important like extreme high or low temperature, closeness of dangerous materials. These circumstances substantiates the need of robots and that they have to solve their problems self-sufficiently, without any direct human interaction.
The model builds up from two main components: the Central Controlling Unit (CCU), and the group of robots. The robots ply in the depository using the line following method. During their activity may turn up some conflict situations, whose autonomous handling is the main topic of our research. Using the right wayfinder algorithm and the representation of the map of the depository, the robots find out after a short information excange, who of them has to give way to the other in order to solve the conflict in optimal time.
The communication between the LEGO MINDSTORMS NXT Robots and the Central Controlling Unit is based on a BlueTooth connection.
The robots' autonomy means that if they loose connection with the CCU, they can finish their commands that they have already received. Nevertheless navigating their physical relocation and sense any incidental new barrier is absolutely their task.
