An approach for scheduling a bi-objective flexible flow shop
This work considers a hybrid flow shop scheduling problem, a well-known production systems problem. It has been largely studied in the literature as a single objective optimization problem. We analyze a flow shop with two stages and ms identical and unrelated parallel machines at each stage s. Given...
- Autores:
-
Miguel Rojas, Santiago
Hulett, María
Muthuswamy, Shanthi
Niebles Atencio, Fabricio Andres
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2015
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/1046
- Acceso en línea:
- https://hdl.handle.net/11323/1046
https://repositorio.cuc.edu.co/
- Palabra clave:
- Bi-Objective scheduling
Cmax
Flexible flow shop
PSO
Tmax
- Rights
- openAccess
- License
- Atribución – No comercial – Compartir igual
Summary: | This work considers a hybrid flow shop scheduling problem, a well-known production systems problem. It has been largely studied in the literature as a single objective optimization problem. We analyze a flow shop with two stages and ms identical and unrelated parallel machines at each stage s. Given a set of jobs with their process times, the objective is to schedule the jobs such that both the makespan and the maximum tardiness are minimized. Each job is to be processed on one of the first-stage parallel machines, and then on the second-stage parallel machines. The problem under study is NP-hard and it can be represented as FF2(Pm1,Pm2||w∗Cmax+(1-w)∗ Tmax). When m1=m2= 1 and the objective is only minimize the makespan, our problem reduces to F2||Cmax. A particle swarm optimization (PSO) approach is proposed to solve the problem. In the experimental phase, instances of 5, 10, 20, 50 and 100 jobs were run. The solution quality and run time of PSO is compared with a commercial solver used to solve the mathematical formulation. Experimental study clearly highlights the advantages, in terms of solution quality and run time, of using PSO to solve large-scale problems. |
---|