New algorithms for solving the tasks scheduling problems in discrete manufacturing systems

• Autorzy: Józefczyk, J.
• Języki publikacji: EN

Abstrakty

EN

The new algorithms for solving the tasks scheduling problem with moving executors to minimize the sum of completion times are considered. The corresponding combinatorial optimization problem is formulated on the basis of the formulations of the problems for the other scheduling performance indices. The heuristic simulation annealing solution algorithm is presented. It is compared with the evolutionary solution algorithm using computer simulation experiments. The influence of the parameters of the solution algorithm as well as the tasks scheduling problem on the quality of results and on the time of computation is investigated.

Słowa kluczowe

EN

problem solving; agile manufacturing systems; heuristic methods; artificial intelligence; computational methods; parameter estimation; simulated annealing; hamiltonians; algorithms; computer simulation

• Czasopismo: Systems Science
• Rocznik: 2003
• Tom: Vol. 29, nr 4
• Strony: 85-98
• Opis fizyczny: Bibliogr. 6 poz.

Twórcy

autor

Józefczyk, J.

• Systems Research Institute, Polish Academy of Sciences, Lab. Knowledge Syst./Artif. Intell., 50-449 Wroclaw, Poland

Bibliografia

• [1] Grefenstette J. et al., Genetic algorithms for the traveling salesman problem, Proc. of Int. Conf. on Genetic Algorithms, Lawrence Erlbaum Associates, Mahaw NJ, USA, 1985.
• [2] Józefczyk J., An algorithm for scheduling tasks on moving executors in complex operation systems, Proc. of 1st IFAC Workshop on Manufacturing Systems MIM'97, Wien, Austria, 1997, 139-144.
• [3] Józefczyk J., Scheduling tasks on moving executors to minimise the maximum lateness, European Journal of Operational Research, 131, 2001, 171-187.
• [4] Józefczyk J., Application of genetic algorithms for solving the scheduling problem with moving executors, Systems Science, 27, 2001, 87-95.
• [5] Józefczyk J., Solving of the scheduling problem with moving executors using advanced genetic algorithms, Proc. of 3rd Symposium Al Meth, Gliwice, Poland, 2002, 205-208.
• [6] Józefczyk J., On the application of evolutionary algorithms with multiple crossovers for solving the task scheduling problem. Proc. of IASTED Int. Conf. on Intelligent Systems and Control, Salzburg, Austria, 2003, (in press).