Introducing the MCHF/OVRP/SDMP: multicapacitated/heterogeneous fleet/open vehicle routing problems with split deliveries and multiproducts.
In this paper, we analyze a real-world OVRP problem for a production company. Considering real-world constrains, we classify our problem as multicapacitated/heterogeneous fleet/open vehicle routing problem with split deliveries and multiproduct (MCHF/OVRP/SDMP) which is a novel classification of an...
| Publicado en: | Scientific World Journal pp. 515402 - 515403 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| 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=109672964&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109672964 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: 109672964 NLM25045735 2012655912 10.1155/2014/515402 NLM25045735 PMC4083288 109672964 ppf: 515402 ppct: 1 formats: tig: atl: Introducing the MCHF/OVRP/SDMP: multicapacitated/heterogeneous fleet/open vehicle routing problems with split deliveries and multiproducts. aug: au: Yilmaz Eroglu, Duygu Caglar Gencosman, Burcu Cavdur, Fatih Ozmutlu, H Cenk Eroglu, Duygu Yilmaz Gencosman, Burcu Caglar affil: Department of Industrial Engineering, Uludag University, Gorukle, 16059 Bursa, Turkey sug: ab: In this paper, we analyze a real-world OVRP problem for a production company. Considering real-world constrains, we classify our problem as multicapacitated/heterogeneous fleet/open vehicle routing problem with split deliveries and multiproduct (MCHF/OVRP/SDMP) which is a novel classification of an OVRP. We have developed a mixed integer programming (MIP) model for the problem and generated test problems in different size (10-90 customers) considering real-world parameters. Although MIP is able to find optimal solutions of small size (10 customers) problems, when the number of customers increases, the problem gets harder to solve, and thus MIP could not find optimal solutions for problems that contain more than 10 customers. Moreover, MIP fails to find any feasible solution of large-scale problems (50-90 customers) within time limits (7200 seconds). Therefore, we have developed a genetic algorithm (GA) based solution approach for large-scale problems. The experimental results show that the GA based approach reaches successful solutions with 9.66% gap in 392.8 s on average instead of 7200 s for the problems that contain 10-50 customers. For large-scale problems (50-90 customers), GA reaches feasible solutions of problems within time limits. In conclusion, for the real-world applications, GA is preferable rather than MIP to reach feasible solutions in short time periods. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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