Minimizing Makespan on Identical Parallel Machines Using Neural Networks
This paper deals with the problem of minimizing the maximum completion time (makespan) of jobs on identical parallel machines. A Hopfield type dynamical neural network is proposed for solving the problem which is known to be NP-hard even for the case of two machines. A penalty function approach is e...
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| creator | Akyol, Derya Eren Bayhan, G. Mirac |
| description | This paper deals with the problem of minimizing the maximum completion time (makespan) of jobs on identical parallel machines. A Hopfield type dynamical neural network is proposed for solving the problem which is known to be NP-hard even for the case of two machines. A penalty function approach is employed to construct the energy function of the network and time evolving penalty coefficients are proposed to be used during simulation experiments to overcome the tradeoff problem. The results of proposed approach tested on a scheduling problem across 3 different datasets for 5 different initial conditions show that the proposed network converges to feasible solutions for all initialization schemes and outperforms the LPT (longest processing time) rule. |
| doi_str_mv | 10.1007/11893295_61 |
| format | Conference Proceeding |
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Mirac</creatorcontrib><title>Minimizing Makespan on Identical Parallel Machines Using Neural Networks</title><title>Lecture notes in computer science</title><description>This paper deals with the problem of minimizing the maximum completion time (makespan) of jobs on identical parallel machines. A Hopfield type dynamical neural network is proposed for solving the problem which is known to be NP-hard even for the case of two machines. A penalty function approach is employed to construct the energy function of the network and time evolving penalty coefficients are proposed to be used during simulation experiments to overcome the tradeoff problem. The results of proposed approach tested on a scheduling problem across 3 different datasets for 5 different initial conditions show that the proposed network converges to feasible solutions for all initialization schemes and outperforms the LPT (longest processing time) rule.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Connectionism. 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Neural networks</topic><topic>Energy Function</topic><topic>Exact sciences and technology</topic><topic>Initialization Scheme</topic><topic>Penalty Parameter</topic><topic>Schedule Problem</topic><topic>Software</topic><topic>Travelling Salesman Problem</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Akyol, Derya Eren</creatorcontrib><creatorcontrib>Bayhan, G. Mirac</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Akyol, Derya Eren</au><au>Bayhan, G. Mirac</au><au>Wang, Jun</au><au>King, Irwin</au><au>Chan, Lai-Wan</au><au>Wang, DeLiang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Minimizing Makespan on Identical Parallel Machines Using Neural Networks</atitle><btitle>Lecture notes in computer science</btitle><date>2006</date><risdate>2006</risdate><spage>553</spage><epage>562</epage><pages>553-562</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540464846</isbn><isbn>3540464840</isbn><isbn>3540464794</isbn><isbn>9783540464792</isbn><eisbn>9783540464853</eisbn><eisbn>3540464859</eisbn><abstract>This paper deals with the problem of minimizing the maximum completion time (makespan) of jobs on identical parallel machines. A Hopfield type dynamical neural network is proposed for solving the problem which is known to be NP-hard even for the case of two machines. 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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2006, p.553-562 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19969898 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Computer systems and distributed systems. User interface Connectionism. Neural networks Energy Function Exact sciences and technology Initialization Scheme Penalty Parameter Schedule Problem Software Travelling Salesman Problem |
| title | Minimizing Makespan on Identical Parallel Machines Using Neural Networks |
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