Review
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BELİRSİZLİK ALTINDA ÜRETİM PLANLAMADA NİCEL YÖNTEMLERİN KULLANIMI ÜZERİNE BİR DERLEME ÇALIŞMASI

Year 2023, Issue: 2 - HAZİRAN, 15 - 30, 11.09.2023

Bilge Meydan

Abstract

Bir üretim sürecinde planlama yapmak her dönem için zor olsa da günümüzde belirsizlik unsurlarının artması ile daha dikkatli çalışılması gereken bir konu haline gelmiştir. Bu araştırma, üretim planlamasında belirsizlikleri ele alan ve nicel yöntemler kullanarak belirsizliklerin bulunduğu üretim sürecini optimize eden çalışmaları inceleyen bir derleme çalışması olarak oluşturulmuştur. Dönem olarak 2010-2017 yılları arasında yayımlanan 26 çalışma incelenmiştir. Araştırmada Web of Science veri tabanında taranan öncü dergilerde yayınlanan çalışmalar baz alınmıştır. Çalışmalar; üretim planlamada ele alınan konu ve alt konular, belirsizlik yaratan unsurlar ve belirsizliklerin çözümü için kullanılan nicel yöntemler dikkate alınarak tasnif edilmiştir. Bu çalışma konu ile ilgili çalışan araştırmacılara kaynak oluşturması açısından önemlidir.

Keywords

Üretim operasyonları belirsizlik altında üretim planlaması nicel yöntemler.

References

  • Al-e-Hashem, S. M., Malekly, H., ve Aryanezhad , M. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. Int. J. Production Economics, 28-42.
  • Alemany, M., Grillo, H., Ortiz, A., ve Fuertes-Miquel, V. (2015). A fuzzy model for shortage planning under uncertainty due to lack of homogeneity in planned production lots. Applied Mathematical Modelling, 4463–4481.
  • Avraamidou, S., ve Pistikopoulos, E. (2017). A Multiparametric Mixed-integer Bi-level Optimization Strategy for Supply Chain Planning Under Demand Uncertainty. IFAC PapersOnLine, 10178-10183.
  • Chatterjee, S., Sethi, M., ve Asad, M. (2016). Production phase and ultimate pit limit design under commodity price uncertainty. EuropeanJournalofOperationalResearch, 658-667.
  • Chatzikontidou, A., Longinidis, P., Tsiakis, P., ve Georgiadis, M. (2017). Flexible supply chain network design under uncertainty. Chemical Engineering Research and Design, 290-305.
  • Chen, Z., ve Sarker, B. (2015). Aggregate production planning with learning effect and uncertain demand: A case based study. Journal of Modelling in Management, 296-324.
  • Dias, L. S., ve Ierapetritou, M. (2016). Integration of scheduling and control underuncertainties: Review and challenges. Chemical Engineering Research and Design, 98-113.
  • Ejikeme-Ugwu, E., Liu, S., ve Wang, M. (2011). Integrated Refinery Planning Under Product Demand Uncertainty. 21st European Symposium on Computer Aided Process Engineering (s. 950-954). Elsevier.
  • Ekin, T. (2017). Integrated maintenance and production planning with endogenous uncertain yield. Reliability Engineering and System Safety, 1-10.
  • Govindan, K., Fattahi, M., ve Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 108-141.
  • H. Lee, J. (2014). Energy supply planning and supply chain optimization under uncertainty. Journal of Process Control, 323-331.
  • Hatefi, S. M., JolaiS. , F., Torabi, A., ve Tavakkoli-Moghaddam, R. (2015). Reliable design of an integrated forward-revere logistics network under uncertainty and facility disruptions: A fuzzy possibilistic programing model. Journal of Civil Engineering, 1117–1128.
  • Jamalnia, A., Yang, J.-B., Xu, D.-L., ve Feili, A. (2017). Novel decision model based on mixed chase and level strategy for aggregate production planning under uncertainty: Case study in beverage industry. Computers ve Industrial Engineering, 54-68.
  • Jouzdani, J., Sadjadi, S., ve Fathian, M. (2013). Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran. Applied Mathematical Modelling, 8467–8483.
  • Klibi, W., Martel, A., ve Guitouni, A. (2010). The design of robust value-creating supply chain networks: A critical review. European Journal of Operational Research, 283–293.
  • Kostin, A., Guillén-Gosálbez, G., Mele, F., Bagajewicz, M., ve Jiménez, L. (2012). Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty. Chemical Engineering Research and Design, 359-376.
  • Lawson, A., Goldstein, M., ve Dent, C. (2016). Bayesian framework for power network planning under uncertainty. Sustainable Energy, Grids and Networks, 47-57.
  • Liu, M., Zhang, Z., ve Zhang, D. (2017). Logistics planning for hospital pharmacy trusteeship under a hybrid of uncertainties. Transportation Research Part E, 201-2015.
  • Marques, C. M., Moniz, S., Sousa, J., ve Barbosa-Póvoa, A. (2017). A simulation-optimization approach to integrate process design andplanning decisions under technical and market uncertainties: A casefrom the chemical-pharmaceutical industry. Computers and Chemical Engineering, 796-813.
  • Moreno, M. S., ve Montagna, J. (2012). Multiperiod production planning and design of batch plants under uncertainty. Computers and Chemical Engineering, 181-190.
  • Moret, S., Gironès , V., Bierlaire , M., ve Maréchal, F. (2017). Characterization of input uncertainties in strategic energy planning models. Applied Energy, 597-617.
  • Mula, J., Poler, R., Garcia-Sabater, J., ve Lario, F. (2006). Models for production planning under uncertainty: A review. Int. J. Production Economics, 271-285.
  • Rahmani, D., Ramezanian, R., Fattahi, P., ve Heydari, M. (2013). A robust optimization model for multi-product two-stage capacitated production planning under uncertainty. Applied Mathematical Modelling, 8957–8971.
  • Silva, A. F., ve Marins, F. (2014). A Fuzzy Goal Programming model for solving aggregate production-planning problems under uncertainty: A case study in a Brazilian sugar mill. Energy Economics, 196-204.
  • W.White, S. (2013). An experimentallyconfirmedresourceplanningmodelofservicesunder. Int. J.ProductionEconomics, 484-484.
  • Zeng, X., Zhu, Y., Chen, C., Tong, Y., Li, Y., Huang, G., . . . Wang, X. (2017). A production-emission nexus based stochastic-fuzzy model for identification of urban industry-environment policy under uncertainty. Journal of Cleaner Production, 61-82.
  • Zhang, Y., Jin, X., Feng, Y., ve Rong, G. (2017). Data-driven Robust Optimization under Correlated Uncertainty: a case study of production. Computers and Chemical Engineering, 48-67.

Year 2023, Issue: 2 - HAZİRAN, 15 - 30, 11.09.2023

Bilge Meydan

Abstract

Planning in the production process is a hard problem all the while. Nowadays, because of the increase of uncertainty elements, it has become a subject that needs to be studied more carefully. This study is created as a review study that deals with uncertainties in production process and optimize the process in which under uncertainties with quantitative methods. It examines 26 papers that was published between 2010-2017. It concerned the papers which is in lead journals in Web of Science. The papers has classified regards to subject and sub-topics about production planning, the elements that create uncertainty and the quantitative methods are used for the solution. This study is important in terms of creating a resource for researchers working on the subject.

Keywords

Production operations production planning under uncertainty quantitative methods

References

  • Al-e-Hashem, S. M., Malekly, H., ve Aryanezhad , M. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. Int. J. Production Economics, 28-42.
  • Alemany, M., Grillo, H., Ortiz, A., ve Fuertes-Miquel, V. (2015). A fuzzy model for shortage planning under uncertainty due to lack of homogeneity in planned production lots. Applied Mathematical Modelling, 4463–4481.
  • Avraamidou, S., ve Pistikopoulos, E. (2017). A Multiparametric Mixed-integer Bi-level Optimization Strategy for Supply Chain Planning Under Demand Uncertainty. IFAC PapersOnLine, 10178-10183.
  • Chatterjee, S., Sethi, M., ve Asad, M. (2016). Production phase and ultimate pit limit design under commodity price uncertainty. EuropeanJournalofOperationalResearch, 658-667.
  • Chatzikontidou, A., Longinidis, P., Tsiakis, P., ve Georgiadis, M. (2017). Flexible supply chain network design under uncertainty. Chemical Engineering Research and Design, 290-305.
  • Chen, Z., ve Sarker, B. (2015). Aggregate production planning with learning effect and uncertain demand: A case based study. Journal of Modelling in Management, 296-324.
  • Dias, L. S., ve Ierapetritou, M. (2016). Integration of scheduling and control underuncertainties: Review and challenges. Chemical Engineering Research and Design, 98-113.
  • Ejikeme-Ugwu, E., Liu, S., ve Wang, M. (2011). Integrated Refinery Planning Under Product Demand Uncertainty. 21st European Symposium on Computer Aided Process Engineering (s. 950-954). Elsevier.
  • Ekin, T. (2017). Integrated maintenance and production planning with endogenous uncertain yield. Reliability Engineering and System Safety, 1-10.
  • Govindan, K., Fattahi, M., ve Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 108-141.
  • H. Lee, J. (2014). Energy supply planning and supply chain optimization under uncertainty. Journal of Process Control, 323-331.
  • Hatefi, S. M., JolaiS. , F., Torabi, A., ve Tavakkoli-Moghaddam, R. (2015). Reliable design of an integrated forward-revere logistics network under uncertainty and facility disruptions: A fuzzy possibilistic programing model. Journal of Civil Engineering, 1117–1128.
  • Jamalnia, A., Yang, J.-B., Xu, D.-L., ve Feili, A. (2017). Novel decision model based on mixed chase and level strategy for aggregate production planning under uncertainty: Case study in beverage industry. Computers ve Industrial Engineering, 54-68.
  • Jouzdani, J., Sadjadi, S., ve Fathian, M. (2013). Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran. Applied Mathematical Modelling, 8467–8483.
  • Klibi, W., Martel, A., ve Guitouni, A. (2010). The design of robust value-creating supply chain networks: A critical review. European Journal of Operational Research, 283–293.
  • Kostin, A., Guillén-Gosálbez, G., Mele, F., Bagajewicz, M., ve Jiménez, L. (2012). Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty. Chemical Engineering Research and Design, 359-376.
  • Lawson, A., Goldstein, M., ve Dent, C. (2016). Bayesian framework for power network planning under uncertainty. Sustainable Energy, Grids and Networks, 47-57.
  • Liu, M., Zhang, Z., ve Zhang, D. (2017). Logistics planning for hospital pharmacy trusteeship under a hybrid of uncertainties. Transportation Research Part E, 201-2015.
  • Marques, C. M., Moniz, S., Sousa, J., ve Barbosa-Póvoa, A. (2017). A simulation-optimization approach to integrate process design andplanning decisions under technical and market uncertainties: A casefrom the chemical-pharmaceutical industry. Computers and Chemical Engineering, 796-813.
  • Moreno, M. S., ve Montagna, J. (2012). Multiperiod production planning and design of batch plants under uncertainty. Computers and Chemical Engineering, 181-190.
  • Moret, S., Gironès , V., Bierlaire , M., ve Maréchal, F. (2017). Characterization of input uncertainties in strategic energy planning models. Applied Energy, 597-617.
  • Mula, J., Poler, R., Garcia-Sabater, J., ve Lario, F. (2006). Models for production planning under uncertainty: A review. Int. J. Production Economics, 271-285.
  • Rahmani, D., Ramezanian, R., Fattahi, P., ve Heydari, M. (2013). A robust optimization model for multi-product two-stage capacitated production planning under uncertainty. Applied Mathematical Modelling, 8957–8971.
  • Silva, A. F., ve Marins, F. (2014). A Fuzzy Goal Programming model for solving aggregate production-planning problems under uncertainty: A case study in a Brazilian sugar mill. Energy Economics, 196-204.
  • W.White, S. (2013). An experimentallyconfirmedresourceplanningmodelofservicesunder. Int. J.ProductionEconomics, 484-484.
  • Zeng, X., Zhu, Y., Chen, C., Tong, Y., Li, Y., Huang, G., . . . Wang, X. (2017). A production-emission nexus based stochastic-fuzzy model for identification of urban industry-environment policy under uncertainty. Journal of Cleaner Production, 61-82.
  • Zhang, Y., Jin, X., Feng, Y., ve Rong, G. (2017). Data-driven Robust Optimization under Correlated Uncertainty: a case study of production. Computers and Chemical Engineering, 48-67.
There are 27 citations in total.

Details

Primary Language Turkish
Subjects Business Administration
Journal Section Research Articles
Authors

Bilge Meydan 0000-0003-1478-5999

Early Pub Date September 11, 2023
Publication Date September 11, 2023
Published in Issue Year 2023 Issue: 2 - HAZİRAN

Cite

APA Meydan, B. (2023). BELİRSİZLİK ALTINDA ÜRETİM PLANLAMADA NİCEL YÖNTEMLERİN KULLANIMI ÜZERİNE BİR DERLEME ÇALIŞMASI. İşletme Ve Girişimcilik Araştırmaları Dergisi(2), 15-30.
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