### Abstract

Production scheduling problems attract a lot of attention among applied scientists and practitioners working in the field of combinatorial optimization and optimization software development since they are encountered in many different manufacturing processes and thus effective solutions to them offer great benefits. In this work, two commonly used heuristic methods for solving production scheduling problems, namely, the Nearest Neighbor (NN) and Ant Colony Optimization (ACO) have been tested on a specific real-life problem and the results discussed. The problem belongs to the class of Asymmetric Travelling Salesman Problems (ATSP), which is known as a hard type problem with no effective solutions for large scale problems available yet. The performances of the Nearest Neighbor algorithm and the Ant Colony Optimization technique were evaluated and compared using two criteria, namely: The minimum value of the objective function achieved and the CPU time it took to find it (including the statistical confidence limits). The conclusions drawn suggest that on one hand the ACO algorithm works better than NN if looking at the achieved minimum values of the objective function. On the other hand, the computational time of the ACO algorithm is slightly longer.

Original language | English |
---|---|

Pages (from-to) | 118-122 |

Number of pages | 5 |

Journal | Information Technology and Control |

Volume | 40 |

Issue number | 2 |

Publication status | Published - 2011 |

### Fingerprint

### Keywords

- Ant colony optimization
- Asymmetric travelling salesman problem
- Nearest neighbor
- Production scheduling
- Theory of algorithms

### ASJC Scopus subject areas

- Computer Science Applications
- Electrical and Electronic Engineering
- Control and Systems Engineering

### Cite this

*Information Technology and Control*,

*40*(2), 118-122.

**Comparison of two heuristic approaches for solving the production scheduling problem.** / Andziulis, Arunas; Dzemydiene, Dale; Steponavičius, Raimundas; Jakovlev, Sergej.

Research output: Contribution to journal › Article

*Information Technology and Control*, vol. 40, no. 2, pp. 118-122.

}

TY - JOUR

T1 - Comparison of two heuristic approaches for solving the production scheduling problem

AU - Andziulis, Arunas

AU - Dzemydiene, Dale

AU - Steponavičius, Raimundas

AU - Jakovlev, Sergej

PY - 2011

Y1 - 2011

N2 - Production scheduling problems attract a lot of attention among applied scientists and practitioners working in the field of combinatorial optimization and optimization software development since they are encountered in many different manufacturing processes and thus effective solutions to them offer great benefits. In this work, two commonly used heuristic methods for solving production scheduling problems, namely, the Nearest Neighbor (NN) and Ant Colony Optimization (ACO) have been tested on a specific real-life problem and the results discussed. The problem belongs to the class of Asymmetric Travelling Salesman Problems (ATSP), which is known as a hard type problem with no effective solutions for large scale problems available yet. The performances of the Nearest Neighbor algorithm and the Ant Colony Optimization technique were evaluated and compared using two criteria, namely: The minimum value of the objective function achieved and the CPU time it took to find it (including the statistical confidence limits). The conclusions drawn suggest that on one hand the ACO algorithm works better than NN if looking at the achieved minimum values of the objective function. On the other hand, the computational time of the ACO algorithm is slightly longer.

AB - Production scheduling problems attract a lot of attention among applied scientists and practitioners working in the field of combinatorial optimization and optimization software development since they are encountered in many different manufacturing processes and thus effective solutions to them offer great benefits. In this work, two commonly used heuristic methods for solving production scheduling problems, namely, the Nearest Neighbor (NN) and Ant Colony Optimization (ACO) have been tested on a specific real-life problem and the results discussed. The problem belongs to the class of Asymmetric Travelling Salesman Problems (ATSP), which is known as a hard type problem with no effective solutions for large scale problems available yet. The performances of the Nearest Neighbor algorithm and the Ant Colony Optimization technique were evaluated and compared using two criteria, namely: The minimum value of the objective function achieved and the CPU time it took to find it (including the statistical confidence limits). The conclusions drawn suggest that on one hand the ACO algorithm works better than NN if looking at the achieved minimum values of the objective function. On the other hand, the computational time of the ACO algorithm is slightly longer.

KW - Ant colony optimization

KW - Asymmetric travelling salesman problem

KW - Nearest neighbor

KW - Production scheduling

KW - Theory of algorithms

UR - http://www.scopus.com/inward/record.url?scp=79959807260&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79959807260&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:79959807260

VL - 40

SP - 118

EP - 122

JO - Information Technology and Control

JF - Information Technology and Control

SN - 1392-124X

IS - 2

ER -