Inventory Based Bi-Objective Flow Shop Scheduling Model and Its Hybrid Genetic Algorithm

Flow shop scheduling problem is a typical NP-hard problem, and the researchers have established many different multi-objective models for this problem, but none of these models have taken the inventory capacity into account. In this paper, an inventory based bi-objective flow shop scheduling model w...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2013-01, Vol.2013 (2013), p.1-7
Main Authors: Qing-dao-er-ji, Ren, Wang, Yuping
Format: Article
Language:English
Subjects:
Archives & records
Benchmarking
Completion time
Computer simulation
Computers
Genetic algorithms
Industrial engineering
Inventories
Job shop scheduling
Job shops
Linear programming
Manufacturing
Mathematical models
Multiple objective analysis
Objectives
Operators
Production scheduling
Researchers
Scheduling
Searching
Sequential scheduling
Stockpiling
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Flow shop scheduling problem is a typical NP-hard problem, and the researchers have established many different multi-objective models for this problem, but none of these models have taken the inventory capacity into account. In this paper, an inventory based bi-objective flow shop scheduling model was proposed, in which both the total completion time and the inventory capacity were as objectives to be optimized simultaneously. To solve the proposed model more effectively, we used a tailor-made crossover operator, and mutation operator, and designed a new local search operator, which can improve the local search ability of GA greatly. Based on all these, a hybrid genetic algorithm was proposed. The computer simulations were made on a set of benchmark problems, and the results indicated the effectiveness of the proposed algorithm.
ISSN:1024-123X
1563-5147
DOI:10.1155/2013/976065