Premium
A natural starting strategy for Aronson's heuristic 1 for solving the deterministic, single operator, multiple machine, multiple run cyclic scheduling
Author(s) -
Aronson Jay E.
Publication year - 1987
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1016/0272-6963(87)90009-x
Subject(s) - computer science , mathematical optimization , heuristic , heuristics , initialization , scheduling (production processes) , schedule , operator (biology) , null move heuristic , artificial intelligence , mathematics , biochemistry , chemistry , repressor , transcription factor , gene , programming language , operating system
The semiautomatic nature of machinery often makes it economical to assign more than one machine to a single operator in a cyclic schedule. If the tasks, executed in a cyclic schedule, are not ordered properly, excessive interference can occur, even in deterministic problems. In a recent article [1], Aronson presented two efficient and effective heuristics for solving a more realistic generalization of the deterministic cyclic scheduling problem—one that has a single operator, multiple nonidentical machines, and multiple nonidentical runs on each machine. Here, the author supplies a natural strategy for initializing the first heuristic, because it is not satisfactory to run the heuristic many times, each time starting with a first run of a different machine, to obtain a reasonable solution. This modification is necessary to improve the performance of the heuristic, for ease of implementation, and savings of computational effort. An example problem is presented to compare the solution found using the heuristic with the new starting strategy to that of the heuristic using the original, random selection strategy. The solution found is optimal, whereas the original version of the heuristic obtained a solution within 8.1% of optimality. The new, natural starting strategy for the heuristic should improve its ability to solve problems with a single execution, and simplify its implementation on mainframes, minicomputers, and microcomputers. Increased use of the techniques outlined here should lead to a higher utilization of labor and machinery on the production floor.