An artificial immune algorithm for the flexible job-shop scheduling problem

2010 ◽  
Vol 26 (4) ◽  
pp. 533-541 ◽  
Author(s):  
A. Bagheri ◽  
M. Zandieh ◽  
Iraj Mahdavi ◽  
M. Yazdani
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Immune Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Shop Scheduling

scholarly journals PENYELESAIAN MULTI-OBJECTIVE FLEXIBLE JOB SHOP SCHEDULING PROBLEM MENGGUNAKAN HYBRID ALGORITMA IMUN

2017 ◽  
Vol 6 (2) ◽  
pp. 79
Author(s):  
Yabunayya Habibi ◽  
Galandaru Swalaganata ◽  
Aprilia Divi Yustita
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Immune Algorithm ◽  
Local Optimum ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective

Flexible Job shop scheduling problem (FJSSP) is one of scheduling problems with specification: there is a job to be done in a certain order, each job contains a number of operations and each operation is processed on a machine of some available machine. The purpose of this paper is to solve Multi-objective Flexible Job Shop scheduling problem with minimizing the makespan, the biggest workload and the total workload of all machines. Because of complexity these problem, a integrated approach Immune Algorithm (IA) and Simulated Annealing (SA) algorithm are combined to solve the multi-objective FJSSP. A clonal selection is a strategy for generating new antibody based on selecting the antibody for reproduction. SA is used as a local search search algorithm for enhancing the local ability with certain probability to avoid becoming trapped in a local optimum. The simulation result have proved that this hybrid immune algorithm is an efficient and effective approach to solve the multi-objective FJSSP

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