MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines
We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel alg...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/519520 |
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| author | Duygu Yilmaz Eroglu H. Cenk Ozmutlu |
| author_facet | Duygu Yilmaz Eroglu H. Cenk Ozmutlu |
| author_sort | Duygu Yilmaz Eroglu |
| collection | DOAJ |
| description | We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes’ random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. |
| format | Article |
| id | doaj-art-fbcd28c3022f4415a4e757dade3d2347 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-fbcd28c3022f4415a4e757dade3d23472025-02-03T01:33:28ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/519520519520MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel MachinesDuygu Yilmaz Eroglu0H. Cenk Ozmutlu1Department of Industrial Engineering, Uludag University, Gorukle Campus, 16059 Bursa, TurkeyDepartment of Industrial Engineering, Uludag University, Gorukle Campus, 16059 Bursa, TurkeyWe developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes’ random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.http://dx.doi.org/10.1155/2014/519520 |
| spellingShingle | Duygu Yilmaz Eroglu H. Cenk Ozmutlu MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines The Scientific World Journal |
| title | MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines |
| title_full | MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines |
| title_fullStr | MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines |
| title_full_unstemmed | MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines |
| title_short | MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines |
| title_sort | mip models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines |
| url | http://dx.doi.org/10.1155/2014/519520 |
| work_keys_str_mv | AT duyguyilmazeroglu mipmodelsandhybridalgorithmsforsimultaneousjobsplittingandschedulingonunrelatedparallelmachines AT hcenkozmutlu mipmodelsandhybridalgorithmsforsimultaneousjobsplittingandschedulingonunrelatedparallelmachines |