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...
| Publicado en: | Scientific World Journal pp. 519520 - 519521 |
|---|---|
| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2014
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109753043&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109753043 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109753043 NLM24977204 2012633808 10.1155/2014/519520 NLM24977204 PMC3958707 109753043 ppf: 519520 ppct: 1 formats: tig: atl: MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines. aug: au: Yilmaz Eroglu, Duygu Ozmutlu, H Cenk Eroglu, Duygu Yilmaz affil: Department of Industrial Engineering, Uludag University, Gorukle Campus, 16059 Bursa, Turkey sug: subj: Algorithms Computer-Aided Design Models, Theoretical Systems Analysis Robotics Methods Workload Computer Simulation Time Factors ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|