Design of Iterated Local Search Algorithms An Example Application to the Single Machine Total Weighted Tardiness Problem

In this article we investigate the application of iterated local search (ILS) to the single machine total weighted tardiness problem. Our research is inspired by the recently proposed iterated dynasearch approach, which was shown to be a very effective ILS algorithm for this problem. In this paper we systematically configure an ILS algorithms by optimizing the single procedures part of ILS and optimizing their interaction. We come up with a highly effective ILS approach, which outperforms our implementation of the iterated dynasearch algorithm on the hardest benchmark instances.

[1]  E. Baum.  Towards practical `neural' computation for combinatorial optimization problems , 1987 .

[2]  Hirofumi Matsuo,et al.  A controlled search simulated annealing method for the single machine weighted tardiness problem , 1990 .

[3]  Chris N. Potts,et al.  A survey of algorithms for the single machine total weighted tardiness scheduling problem , 1990, Discret. Appl. Math..

[4]  Chris N. Potts,et al.  Single Machine Tardiness Sequencing Heuristics , 1991 .

[5]  Edward W. Felten,et al.  Large-Step Markov Chains for the Traveling Salesman Problem , 1991, Complex Syst..

[6]  Helena Ramalhinho Dias Lourenço,et al.  Job-shop scheduling: Computational study of local search and large-step optimization methods , 1995 .

[7]  Olivier C. Martin,et al.  Combining simulated annealing with local search heuristics , 1993, Ann. Oper. Res..

[8]  Helena Ramalhinho Dias Lourenço,et al.  Combining the Large-Step Optimization with Tabu-Search: Application to The Job-Shop Scheduling Problem , 1996 .

[9]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[10]  Andrew B. Kahng,et al.  Improved Large-Step Markov Chain Variants for the Symmetric TSP , 1997, J. Heuristics.

[11]  Chris N. Potts,et al.  Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem , 1998, INFORMS J. Comput..

[12]  Thomas Sttzle,et al.  Applying iterated local search to the permutation ow shop problem , 1998 .

[13]  Thomas Stützle,et al.  Evaluating Las Vegas Algorithms: Pitfalls and Remedies , 1998, UAI.

[14]  Silvano Martello,et al.  Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization , 2012 .

[15]  Thomas Stützle,et al.  Local search algorithms for combinatorial problems - analysis, improvements, and new applications , 1999, DISKI.

[16]  C. R. Reeves,et al.  Landscapes, operators and heuristic search , 1999, Ann. Oper. Res..

[17]  Daniel Merkle,et al.  An Ant Algorithm with a New Pheromone Evaluation Rule for Total Tardiness Problems , 2000, EvoWorkshops.

[18]  S. Kreipl.  A large step random walk for minimizing total weighted tardiness in a job shop , 2000 .

[19]  Matthijs den Besten,et al.  Ant Colony Optimization for the Total Weighted Tardiness Problem , 2000, PPSN.

[20]  Chris N. Potts,et al.  An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem , 2002, INFORMS J. Comput..