Solving Real-Life Locomotive-Scheduling Problems

Solving Real-Life Locomotive-Scheduling Problems

The locomotive scheduling problem (or the locomotive assignment problem) is to assign a consist (a set of locomotives) to each train in a pre-planned train schedule so as to provide them sufficient power to pull them from their origins to their destinations. Locomotive scheduling problems are among the most important problems in railroad scheduling. In this paper, we report the results of a stu...

Solving Real-Life Time-Tabling Problems

We will compare the performance of a Genetic Algorithm and Simulated Annealing on instances of difficult real-life Time-Tabling Problems. We will further explain how the performance can be increased significantly by applying a general and very effective genotype-to-phenotype decoding. Furthermore, we will contrast the performance of Lamarckian Learning to the performance of Baldwin Learning. Fi...

Solving Scheduling Problems by

We deene a general methodology to deal with a large family of scheduling problems. We consider the case when some of the constraints are expressed through the minimization of a loss function. We study with more details a benchmark example consisting in some jigsaw puzzle problem with additional constraints. This is the occasion to discuss some algorithmic issues typical of scheduling problems, ...

Study of Complex Problem-solving in Real Life Problems

On Solving Flowshop Scheduling Problems

This paper proposes two variants of heuristic algorithms to solve the classic permutation flowshop scheduling problem. Both algorithms are simple and very efficient. The first algorithm is a constructive heuristic which builds the optimal schedule of jobs on the basis of a selective-greedy process. To escape the trap of local optimum points, for the second heuristic algorithm a stochastic featu...