Unrelated parallel machine scheduling using local search

Simulated annealing and taboo search are well-established local search methods for obtaining approximate solutions to a variety of combinatorial optimization problems. More recently, genetic algorithms have also been applied. However, there are few studies which compare the relative performance of these different methods on the same problem. In this paper, these techniques are applied to the problem of scheduling jobs on unrelated parallel machines to minimize the maximum completion time. Results of extensive computational tests indicate that the quality of solutions generated by a genetic algorithm is poor. However, a hybrid method in which descent is incorporated into the genetic algorithm is comparable in performance with simulated annealing and taboo search.

[1]  Alistair I. Mees,et al.  Convergence of an annealing algorithm , 1986, Math. Program..

[2]  Colin R. Reeves,et al.  Improving the Efficiency of Tabu Search for Machine Sequencing Problems , 1993 .

[3]  David K. Smith,et al.  The application of the simulated annealing algorithm to the solution of the n/m/Cmax flowshop problem , 1990, Comput. Oper. Res..

[4]  Dk Smith,et al.  Simulated annealing for the permutation flowshop problem , 1991 .

[5]  Mauro Dell'Amico,et al.  Applying tabu search to the job-shop scheduling problem , 1993, Ann. Oper. Res..

[6]  R. Storer,et al.  New search spaces for sequencing problems with application to job shop scheduling , 1992 .

[7]  Éric D. Taillard,et al.  Parallel Taboo Search Techniques for the Job Shop Scheduling Problem , 1994, INFORMS J. Comput..

[8]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[9]  Emile H. L. Aarts,et al.  Genetic Local Search Algorithms for the Travelling Salesman Problem , 1990, PPSN.

[10]  I. Osman,et al.  Simulated annealing for permutation flow-shop scheduling , 1989 .

[11]  C. N. Potts,et al.  Analysis of a linear programming heuristic for scheduling unrelated parallel machines , 1985, Discret. Appl. Math..

[12]  Erwin Pesch,et al.  Evolution based learning in a job shop scheduling environment , 1995, Comput. Oper. Res..

[13]  É. Taillard.  Some efficient heuristic methods for the flow shop sequencing problem , 1990 .

[14]  Richard W. Eglese,et al.  Simulated annealing: A tool for operational research , 1990 .

[15]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[16]  Oscar H. Ibarra,et al.  Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors , 1977, JACM.

[17]  A. Hertz,et al.  A new heuristic method for the flow shop sequencing problem , 1989 .

[18]  David Connolly.  An improved annealing scheme for the QAP , 1990 .

[19]  Jan Karel Lenstra,et al.  Job Shop Scheduling by Simulated Annealing , 1992, Oper. Res..

[20]  Ellis Horowitz,et al.  Exact and Approximate Algorithms for Scheduling Nonidentical Processors , 1976, JACM.

[21]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[22]  Fred Glover,et al.  Tabu Search: A Tutorial , 1990 .

[23]  Steef L. van de Velde.  Duality-Based Algorithms for Scheduling Unrelated Parallel Machines , 1993, INFORMS J. Comput..

[24]  Jeremy P. Spinrad.  Worst-case analysis of a scheduling algorithm , 1985 .

[25]  Erwin Pesch,et al.  Genetic Local Search in Combinatorial Optimization , 1994, Discret. Appl. Math..

[26]  Jeffrey M. Jaffe,et al.  Algorithms for Scheduling Tasks on Unrelated Processors , 1981, JACM.

[27]  Jan Karel Lenstra,et al.  Approximation algorithms for scheduling unrelated parallel machines , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[28]  Chris N. Potts,et al.  Heuristics for scheduling unrelated parallel machines , 1991, Comput. Oper. Res..

[29]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[30]  Gunar E. Liepins,et al.  Genetic algorithms: Foundations and applications , 1990 .