A novel artificial immune system for solving multiobjective scheduling problems subject to special process constraint

2010 ◽  
Vol 58 (4) ◽  
pp. 602-609 ◽  
Author(s):  
Jiaquan Gao
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Scheduling Problems ◽  
Artificial Immune ◽  
Special Process

Solving job shop scheduling problems using artificial immune system

2006 ◽  
Vol 31 (5-6) ◽  
pp. 580-593 ◽  
Author(s):  
M. Chandrasekaran ◽  
P. Asokan ◽  
S. Kumanan ◽  
T. Balamurugan ◽  
S. Nickolas
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problems ◽  
Artificial Immune ◽  
Shop Scheduling

Solving Multi Objective Job Shop Scheduling Problems Using Artificial Immune System Shifting Bottleneck Approach

2015 ◽  
Vol 766-767 ◽  
pp. 1209-1213 ◽  
Author(s):  
S. Gopinath ◽  
C. Arumugam ◽  
Tom Page ◽  
M. Chandrasekaran
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flow Time ◽  
Scheduling Problems ◽  
Artificial Immune ◽  
Shifting Bottleneck ◽  
Shop Scheduling ◽  
Multi Objective

Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered in this work. The Artificial Immune System Shifting Bottleneck Approach is used for finding optimal makespan, mean flow time, mean tardiness values of two benchmark problems. In this Artificial Immune System Shifting Bottleneck Approach (AISSB), initial sequences are generated with Artificial Immune System Algorithm (AIS) and Shifting Bottleneck Algorithm (SB) is used for finding final solutions. The results show that the AISSB Approach is effective algorithm that gives better results than literature results. The proposed AISSB Approach is an efficient problem-solving technique for multi objective job shop scheduling problem.

Heuristics and Metaheuristics for Solving Scheduling Problems

2011 ◽  
pp. 1-18 ◽  
Author(s):  
Dipak Laha
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Optimization Methods ◽  
Scheduling Problems ◽  
Artificial Immune ◽  
Successful Operation ◽  
Planning And Control ◽  
Computing Paradigm ◽  
Control Department ◽  
And Control

Manufacturing scheduling plays a very important function in successful operation of the production planning and control department of an organization. It also offers a great theoretical challenge to the researchers because of its combinatorial nature. Earlier, researchers emphasized classical optimization methods such as linear programming and branch-and-bound method to solve scheduling problems. However, these methods have the limitation of tackling only small-sized scheduling problems because of the consumption of high computational (CPU) time. As a result, heuristics as well as various efficient optimization methods based on the evolutionary computing paradigm such as genetic algorithms, simulated annealing, and artificial immune system have been applied to scheduling problems for obtaining near optimal solutions. These computational tools are currently being utilized successfully in various engineering and management fields. We briefly discuss the overview of these emerging heuristics and metaheuristics and their applications to the scheduling problems. Given the rise in attention by the researchers, more emphasis has been given to explore artificial immune system in details

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