A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem

Authors

  • F . Jolai Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 14395-515, Tehran, Iran
  • Mohammad Mirabi Group of Industrial Engineering, Ayatollah Haeri University of Meybod, P.O. Box 89619-55133, Meybod, Iran
  • S. M. T. Fatemi Ghomi Department of Industrial Engineering, Amirkabir University of Technology, P.O. Box 15916-34311, Tehran, Iran
Abstract:

Flow-shop scheduling problem (FSP) deals with the scheduling of a set of n jobs that visit a set of m machines in the same order. As the FSP is NP-hard, there is no efficient algorithm to reach the optimal solution of the problem. To minimize the holding, delay and setup costs of large permutation flow-shop scheduling problems with sequence-dependent setup times on each machine, this paper develops a novel hybrid genetic algorithm (HGA) with three genetic operators. Proposed HGA applies a modified approach to generate a pool of initial solutions, and also uses an improved heuristic called the iterated swap < /div> procedure to improve the initial solutions. We consider the make-to-order production approach that some sequences between jobs are assumed as tabu based on maximum allowable setup cost. In addition, the results are compared to some recently developed heuristics and computational experimental results show that the proposed HGA performs very competitively with respect to accuracy and efficiency of solution.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Heuristic approach to solve hybrid flow shop scheduling problem with unrelated parallel machines

In hybrid flow shop scheduling problem (HFS) with unrelated parallel machines, a set of n jobs are processed on k machines. A mixed integer linear programming (MILP) model for the HFS scheduling problems with unrelated parallel machines has been proposed to minimize the maximum completion time (makespan). Since the problem is shown to be NP-complete, it is necessary to use heuristic methods to ...

full text

A Hybrid Genetic Algorithm for the Sequence Dependent Flow-Shop Scheduling Problem

Flow-shop scheduling problem (FSP) deals with the scheduling of a set of jobs that visit a set of machines in the same order. The FSP is NP-hard, which means that an efficient algorithm for solving the problem to optimality is unavailable. To meet the requirements on time and to minimize the make-span performance of large permutation flow-shop scheduling problems in which there are sequence dep...

full text

A multi-objective genetic algorithm (MOGA) for hybrid flow shop scheduling problem with assembly operation

Scheduling for a two-stage production system is one of the most common problems in production management. In this production system, a number of products are produced and each product is assembled from a set of parts. The parts are produced in the first stage that is a fabrication stage and then they are assembled in the second stage that usually is an assembly stage. In this article, the first...

full text

A Hybrid Genetic Algorithm for the Flow-Shop Scheduling Problem

This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local s...

full text

A Novel Imperialist Competitive Algorithm to Solve Flexible Flow Shop Scheduling Problem in Order to Minimize Maximum Completion Time

This paper demonstrates solving the flexible flow shop scheduling problem (FFSP) with considering limited waiting time constraint, sequence dependent setup times and different ready time to minimize maximum completion time (i.e. makespan). Since the problem studied is NP-hard, metaheuristic algorithms are proper to solve this class of problems. Hence, in this 1 / 4

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 10  issue 2

pages  -

publication date 2014-06-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023