Minimizing total earliness and tardiness penalties with a common due date on a single-machine using a discrete particle swarm optimization algorithm

Authors:
Quan-Ke Pan;M. Fatih Tasgetiren;Yun-Chia Liang
Affiliations:
College of Computer Science, Liaocheng University, Liaocheng, China;Department of Management, Fatih University, Istanbul, Turkey;Department of Industrial Engineering and Management, Yuan Ze University, Taiwan
Venue:
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Year:
2006

Citing 3
Cited 3

Earliness-tardiness scheduling problems: II. Derivation of completion times about a restrictive common due date

Operations Research
Benchmarks for scheduling on a single machine against restrictive and unrestrictive common due dates

Computers and Operations Research
Single-machine scheduling for minimizing earliness and tardiness penalties by meta-heuristic approaches

Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics

A discrete differential evolution algorithm for the permutation flowshop scheduling problem

Computers and Industrial Engineering
Template for P discrete particle swarm optimization algorithm for multi-UCAV cooperation mission assignment

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Hybridization of harmony search with variable neighborhood search for restrictive single-machine earliness/tardiness problem

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the single machine total earliness and tardiness penalties with a common due date. A modified version of HRM heuristic presented by Hino et al. in [1], here we call it MHRM, is also presented to solve the problem. In addition, the DPSO algorithm is hybridized with the iterated local search (ILS) algorithm to further improve the solution quality. The performance of the proposed DPSO algorithm is tested on 280 benchmark instances ranging from 10 to 1000 jobs from the OR Library. The computational experiments showed that the proposed DPSO algorithm has generated better results, in terms of both percentage relative deviations from the upper bounds in Biskup and Feldmann and computational time, than Hino et al. [1].