A New Artificial Immune Algorithm for Flexible Job-Shop Scheduling

2010 ◽  
Vol 121-122 ◽  
pp. 266-270
Author(s):  
Lu Hong
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Immune Algorithm ◽  
Initial Population ◽  
Clonal Selection Algorithm ◽  
Scheduling Problem ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Shop Scheduling ◽  
Multi Objective

Flexible job-sop scheduling problem (FJSP) is based on the classical job-shop scheduling problem (JSP). however, it is even harder than JSP because of the addition of machine selection process in FJSP. An improved artificial immune algorithm, which combines the stretching technique and clonal selection algorithm is proposed to solve the FJSP. The algorithm can keep workload balance among the machines, improve the quality of the initial population and accelerate the speed of the algorithm’s convergence. The details of implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP.

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

A Three-Fold Approach to Solve Dynamic Job Shop Scheduling Problems by Artificial Immune Algorithm

2010 ◽  
Vol 139-141 ◽  
pp. 1666-1669
Author(s):  
Shan Shan Wu ◽  
Bei Zhi Li ◽  
Jian Guo Yang
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Immune Algorithm ◽  
Scheduling Problems ◽  
Priority Rules ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Shop Scheduling ◽  
The Individual ◽  
Dynamic Job Shop

This paper aims to propose a novel three-fold approach to solve dynamic job-shop scheduling problems by artificial immune algorithm. The proposed approach works in three phases. Firstly, priority rules are deployed to decrease problem scale instead of using scheduling algorithms directly. Secondly, immune algorithm is applied to optimize the individual scheduling modules. Finally, integration schema is employed to reschedule operations and minimize makespan of gross schedule. The integration schema is carried out in a dynamic manner that the previous modules’ machine idle time is searched continuously. In this way, the machine utilization is increased while the objective of makespan minimization is maintained. Efficacy of the proposed approach has been tested with test instances of job-shop scheduling problems. The experimentation results clearly show effectiveness of the proposed approach.

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