Solving job shop scheduling problems using artificial immune system

Chandrasekaran, M.; Asokan, P.; Kumanan, S.; Balamurugan, T.; Nickolas, S.

doi:10.1007/s00170-005-0226-3

Cited by 54 publications

(25 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The operative mechanisms of immune systems are very efficient from a computational standpoint. The AIS has appeared to offer powerful and robust information processing capabilities for solving complex problems [11][12][13][14]. However, to the best of out knowledge, the use of artificial immune systems for training the HMM has been not found in the existing literatures.…”

Section: Ais-based Hmm Learning Algorithmmentioning

confidence: 99%

A Hybrid Algorithm Based on Artificial Immune System and Hidden Markov Model for Multiple Sequence Alignment

Ge¹,

Zhong²,

Du³

et al. 2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

View full text Add to dashboard Cite

Multiple sequence alignment (MSA) has become an essential tool in the analysis of biologic sequences. In this paper, an artificial immune system (AIS) is proposed to train hidden Markov models (HMMs). Further, an integration algorithm based on the HMM and AIS for the MSA is constructed and a decoding algorithm based on Viterbi algorithm is also proposed. The approach is tested on a set of standard instances taken from the benchmark alignment database, BAliBASE. Numerical results are compared with those obtained by using the Baum-Welch training algorithm. The results show that the proposed algorithm not only improves the alignment abilities, but also reduces the time cost.

show abstract

Section: Ais-based Hmm Learning Algorithmmentioning

confidence: 99%

A Hybrid Algorithm Based on Artificial Immune System and Hidden Markov Model for Multiple Sequence Alignment

Ge¹,

Zhong²,

Du³

et al. 2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

View full text Add to dashboard Cite

show abstract

“…It also includes development of associated solution techniques (Wiers, 1997). Some widely studied classical models comprise single machine, parallel machine, flowshop scheduling and job shop scheduling models (Baker, 1974;Chandrasekaran et al, 2005;Fink & Vob, 2003). The objective of JSSP and FSSP is to find a permutation schedule that minimizes the maximum completion time of a sequence.…”

Section: Introductionmentioning

confidence: 99%

“…A computational method for JSSP was proposed, anchored in the principles of artificial immune system (Chandrasekaran et al, 2005). The objective considered was to find the optimal makespan values.…”

Section: Introductionmentioning

confidence: 99%

An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling problems

Anandaraman¹

2011

IJIEC

View full text Add to dashboard Cite

Job Shop Scheduling Problem (JSSP) and Flow Shop Scheduling Problem (FSSP) are strong NP-complete combinatorial optimization problems among class of typical production scheduling problems. An improved Sheep Flock Heredity Algorithm (ISFHA) is proposed in this paper to find a schedule of operations that can minimize makespan. In ISFHA, the pairwise mutation operation is replaced by a single point mutation process with a probabilistic property which guarantees the feasibility of the solutions in the local search domain. A Robust-Replace (R-R) heuristic is introduced in place of chromosomal crossover to enhance the global search and to improve the convergence. The R-R heuristic is found to enhance the exploring potential of the algorithm and enrich the diversity of neighborhoods. Experimental results reveal the effectiveness of the proposed algorithm, whose optimization performance is markedly superior to that of genetic algorithms and is comparable to the best results reported in the literature.

show abstract

“…Some previous publications have focused on applying AIS to different scheduling problems, for example, flow shop scheduling problem 15 the job shop scheduling problem 16,17,18 the hybrid flow scheduling problem 19 and the multiprocessor scheduling problem 20,21 . Due to human factors or operation faults, the processing times and due dates are unknown exactly in the multi objective flow shop problems.…”

Section: Introductionmentioning

confidence: 99%

A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems

Kahraman¹,

Engin²,

Yılmaz³

2009

IJCIS

View full text Add to dashboard Cite

In this paper a new artificial immune system (AIS) algorithm is proposed to solve multi objective fuzzy flow shop scheduling problems. A new mutation operator is also described for this AIS. Fuzzy sets are used to model processing times and due dates. The objectives are to minimize the average tardiness and the number of tardy jobs. The developed new AIS algorithm is tested on real world data collected at an engine cylinder liner manufacturing process. The feasibility and effectiveness of the proposed AIS is demonstrated by comparing it with genetic algorithms. Computational results demonstrate that the proposed AIS algorithm is more effective meta-heuristic for multi objective flow shop scheduling problems with fuzzy processing time and due date.

show abstract

Solving job shop scheduling problems using artificial immune system

Cited by 54 publications

References 17 publications

A Hybrid Algorithm Based on Artificial Immune System and Hidden Markov Model for Multiple Sequence Alignment

A Hybrid Algorithm Based on Artificial Immune System and Hidden Markov Model for Multiple Sequence Alignment

An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling problems

A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop Problems

Contact Info

Product

Resources

About