4.7 Article

Constraint Programming and constructive heuristics for parallel machine scheduling with sequence-dependent setups and common servers

Journal

COMPUTERS & INDUSTRIAL ENGINEERING
Volume 172, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2022.108586

Keywords

Scheduling; Parallel machines; Sequence-dependent setups; Servers; Constraint Programming

Funding

  1. EU
  2. Ministry of Education, Youth and Sport of the Czech Republic [CZ.02.1.01/0.0/0.0/16_026/0008432]
  3. Ministry of Industry and Trade of the Czech Republic [CZ.01.1.02/0.0/0.0/20_321/0024399]

Ask authors/readers for more resources

This paper examines the scheduling problem of P|(seq), ser|C(max) and proposes a Constraint Programming (CP) model and constructive heuristics suitable for large-scale instances. The experimental comparison shows that our approach outperforms existing methods in terms of computation time and solution quality.
This paper examines scheduling problem denoted as P|(seq), ser|C(max )in Graham's notation; in other words, scheduling of tasks on parallel identical machines (P) with sequence-dependent setups (seq) each performed by one of the available servers (ser). The goal is to minimize the makespan (C-max). We propose a Constraint Programming (CP) model for finding the optimal solution and constructive heuristics suitable for large problem instances. These heuristics are also used to provide a feasible starting solution to the proposed CP model, significantly improving its efficiency. This combined approach constructs solutions for benchmark instances of up to 20 machines and 500 tasks in 10 s, with makespans 3 % to 115 % greater than the calculated lower bounds with a 5% average. The extensive experimental comparison also shows that our proposed approaches outperform the existing ones.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available