
Number of Journals | 34 |
Number of Issues | 1,306 |
Number of Articles | 9,427 |
Article View | 9,188,349 |
PDF Download | 5,620,769 |
Supplier selection and order allocation under uncertain conditions using evolutionary algorithms (case study: Isar general items support company) | ||
مدیریت زنجیره تأمین | ||
Volume 22, Issue 68, January 2021, Pages 92-108 PDF (2.15 M) | ||
Document Type: Research/ Original/ Regular Article | ||
Authors | ||
Hossein-ali Hassan-pour* 1; hossein ghaffari Turan2; mostafa zaree3; ali mohammadi4 | ||
1Industrial Eng Group/ Imam Hussein Comprehensive University | ||
2Manager of supply chain (amad) research group, logistics studies and research institute, | ||
3Ph.D. Candidate of Industrial Engineering, University of Imam Hossein (PBUH) | ||
4MSc. in Industrial Engineering, University of Imam Hossein (PBUH), Tehran | ||
Receive Date: 25 May 2020, Revise Date: 19 June 2020, Accept Date: 20 July 2020 | ||
Abstract | ||
Supply Chain Management is one of the new approaches in supply chain management and a competitive advantage for organizations. The purpose of this research is to present a hybrid model for supplier selection and order allocation under uncertainty in which a two-objective nonlinear integer programming model is presented to optimize the general supply chain where all parameters of objective functions and constraints are presented. It is considered uncertain. The method of solving this is that the appropriate weights of the relevant metrics and sub-criteria for the producers are first obtained by using the organization's criteria through the multi-criteria decision making method. These weights provide input to the proposed mathematical model. The mathematical model presented is solved as a case study using GAMS software. Then, two multiobjective meta-heuristic algorithms, including genetic algorithm and particle optimization algorithm with inappropriate sorting, are presented and their results are compared with accurate software for validation. The responses of the meta-algorithms to the GAMS solutions for different problems are less than 2.5%. These results show that the proposed algorithms converge to the optimal and efficient solution | ||
Keywords | ||
Emergency Resilient Supply Chain; Nonlinear Integer Programming; Multi-Objective Optimization; NSGA-II; MOSPO | ||
References | ||
[1] Sawik T., "Selection of resilient supply portfolio under disruption risks," Omega, vol. 41, no. 2, pp. 259-269, 2013
[2] فکور ثقیه,ا. الفت، ل, فیضی، ک. “مدلی برای قابلیت ارتجاعی زنجیره تامین برای رقابتپذیریدرشرکتهای خودروسازی ایران,” فصلنامه علمی – مدیریت تولید وعملیات, سال پنجم، شماره1، 1393، 143-164. [3] Hatefi, S.M. and Jolai F. "Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions," Applied Mathematical Modelling, vol. 38, no. 1-2, p. 2630–2647, May 2014.
[4] پیشوایی. م.س.، سلسبیل. م. و شفیعا. م.ع و “برنامه ریزی تاکتیکی استوار زنجیرة تأمین جهانی سه سطحی تحت شرایط اختلال تحریم با در نظر گرفتن عمر قفسه ای( مطالعة موردی: زنجیرة تأمین دارو),” مدیریت صنعتی دانشگاه تهران, جلد 7, شماره2 ،1394، 305-332. [5] یحیی زاده اندواری,ی. الفت، ل. و امیری، م. “رویکرد بهینه سازی استوار در انتخاب تأمین کننده و تخصیص [6] کریم میان,ز. قدسی پور، ح. و قیدر خلجانی، ج. “انتخاب تأمین کننده با درنظرگرفتن ارتباطات میان تأمین کنندگان و ریسکاختلال تأمین در محصولات پیچیده,” فصلنامه علمی – مدیریت تولید و عملیات, سال هشتم، شماره2، 1396، 135-150. [7] Rajesh R and . Ravi V., "Supplier selection in resilient supply chains: a grey relational analysis approach," Journal of Cleaner Production, vol. 86, no. 16, p. 343-359, 2015
[8] Torabi S.A, Baghersad M. and Mansouri S., "Resilient supplier selection and order allocation under operational and disruption risks," Transportation Research Part E, vol. 79, 22-48, 2015.
[9]. Jabbarzadeh A. et al. "Designing a supply chain resilient to major disruptions and supply/demand interruptions, " Transportation Research Part B, vol. 94, no. 8, p. 121–149, September 2016. [10] PrasannaVenkatesan S. et al." Multi-objective supplier selection and order allocation under disruption risk," Transportation Research Part E: Logistics and Transportation Review, vol. 95, no. 6, p. 124–142, November 2016.
[11] Sadeque Hamdan and Ali Cheaitou, Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach , Computers and Operation Research,http://dx.doi.org/10.1016/j.cor.2016.11.005.
[12] Jolai F., Khalili, S. and Torabi, S. A. "in, Integrated production–distribution planning two-echelon systems: a resilience view," International Journal of Production Research, 2016.
[13] جبله م, حسن پور ح و مصدق خواه م, “طراحی شبکه زنجیره تأمین چندهدفه چندسطحی مبتنی بر چابکی و ارزش های محوری و حل با یک روش کارا,” پایان نامه کارشناسی ارشد-دانشگاه جامع امام حسین, تهران, 1395. [14] Reza Alikhani, S.A. Torabi, Nezih Altay, Strategic supplier selection under sustainability and risk criteria, International Journal of Production Economics (2018), doi: 10.1016/j.ijpe.2018.11.018 [15] Vahidi F, Torabi SA, Ramezankhani MJ, Sustainable supplier selection and order allocation under operational and disruption risks, Journal of Cleaner Production (2017), doi: 10.1016/j.jclepro.2017.11.012. [16] یاری م .، پیشوایی م س, و جبار زاده آ, “طراحی زنجیره تأمین رقابتی با در نظر گرفتن اختلال در تأمین,” فصلنامه علمی – پژوهشی مطالعات مدیریت صنعتی, جلد 13, شماره 48، 1397، 53-31 [17] Ahmed Mohammed, Irina Harris, Govindan Kannan, A hybrid MCDMFMOO approach for sustainable supplier selection and order allocation, International Journal of Production Economics (2019), doi: 10.1016/j.ijpe.2019.02.003 [18] Xu, J., Zhou, X.,. "Approximation based fuzzy multi-objective models with expected objectives & chance constraints" application to earth-rock work allocation. Inf. Sci. 238, 75–95,2013
[19] Chan, F. T., Jha, A. and Tiwari, M. K., "Bi-Objective Optimization of Three Echelon Supply Chain involving Truck Selection and Loading using NSGA-II with Heuristics algorithm," Applied Soft Computing, vol. 38, pp. 978-987, 2016.
[20] نوتاش, م., زندیه, م. و درّی, ب., “طراحی چند هدفه شبکه زنجیره تأمین با رویکرد الگوریتم ژنتیک,” پژوهش های مدیریت در ایران, جلد 18, شماره 4، 1393، 183-203.
[21] ذوالفقاری جایگانی, ن. و حسن پور, ح., “طراحی شبکه توزیع کالاهای پرخطر و حل آن با یک روش کارآمد,” در پایان نامه کارشناسی ارشد، رشته مهندسی صنایع- گرایش لجستیک و مدیریت زنجیره تأمین, تهران, دانشگاه جامع امام حسین(ع), 1394
[22] مقدم, س. “آشنایی با سیستم مدیریت ارتباط با تأمین کنندگان SRM در SAP,” شرکت ثامن ارتباط عصر, تهران, 1396.
[23] Amin, S. H. and Zhang, G., "A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return," Applied Mathematical Modelling, vol. 37, no. 6, pp. 4165-4176,
[24] E. R. Kennedy J., "Particle Swarm
Optimization," IEEE Transactions On, vol.
8, no. 3, pp. 1942-1948, 1995. | ||
Statistics Article View: 444 PDF Download: 368 |