The Monte Carlo Rollout method (MCR) is a novel approach to solve combinatorial optimization problems with uncertainties approximatively. It combines ideas from Rollout algorithms for combinatorial optimization and the Monte Carlo Tree Search in game theory. In this paper the results of an investigation of applying the MCR to a Scheduling Problem are shown. The quality of the MCR method depends on the model parameters, search depth and search width, which are strong linked to process parameters. These dependencies are analyzed by different simulations. The paper also deals with the question whether the Lookahead Pathology occurs
Greedy Randomized Adaptive Search Procedures (GRASP) are among the most popular metaheuristics for t...
This article considers Markov chain computational methods for incorporating uncertainty about the d...
Production planning has a fundamental role in any manufacturing operation. The problem is to decide ...
The Monte Carlo Rollout method (MCR) is a novel approach to solve combinatorial optimization problem...
To optimize a combinatorial problem one can use complex algorithms, e.g. branchand- bound algorithms...
Abstract. Greedy heuristics may be attuned by looking ahead for each possible choice, in an approach...
The application of optimization in industrial processes is faced with many challenges. One of the ma...
The application of optimization in industrial processes is faced with many challenges. One of the ma...
The Monte Carlo Tree Search (MCTS) algorithm has recently proved to be able to solve difficult probl...
International audienceMany state-of-the-art methods for combinatorial games rely on Monte Carlo Tree...
Most classical scheduling formulations assume a fixed and known duration for each ac-tivity. In this...
This paper presents a methodology for applying scheduling algorithms using Monte Carlo simulation. T...
The topic of this thesis are new approximation methods for job-shop scheduling that dispatch jobs ba...
Most classical scheduling formulations assume a fixed and known duration for each activity. In this ...
Monte Carlo tree search (MCTS) is a sampling and simulation based technique for searching in large s...
Greedy Randomized Adaptive Search Procedures (GRASP) are among the most popular metaheuristics for t...
This article considers Markov chain computational methods for incorporating uncertainty about the d...
Production planning has a fundamental role in any manufacturing operation. The problem is to decide ...
The Monte Carlo Rollout method (MCR) is a novel approach to solve combinatorial optimization problem...
To optimize a combinatorial problem one can use complex algorithms, e.g. branchand- bound algorithms...
Abstract. Greedy heuristics may be attuned by looking ahead for each possible choice, in an approach...
The application of optimization in industrial processes is faced with many challenges. One of the ma...
The application of optimization in industrial processes is faced with many challenges. One of the ma...
The Monte Carlo Tree Search (MCTS) algorithm has recently proved to be able to solve difficult probl...
International audienceMany state-of-the-art methods for combinatorial games rely on Monte Carlo Tree...
Most classical scheduling formulations assume a fixed and known duration for each ac-tivity. In this...
This paper presents a methodology for applying scheduling algorithms using Monte Carlo simulation. T...
The topic of this thesis are new approximation methods for job-shop scheduling that dispatch jobs ba...
Most classical scheduling formulations assume a fixed and known duration for each activity. In this ...
Monte Carlo tree search (MCTS) is a sampling and simulation based technique for searching in large s...
Greedy Randomized Adaptive Search Procedures (GRASP) are among the most popular metaheuristics for t...
This article considers Markov chain computational methods for incorporating uncertainty about the d...
Production planning has a fundamental role in any manufacturing operation. The problem is to decide ...