KaydedilenlerDerginizi ekleyin

Dergiler / Uluslararası Anadolu Ziraat Mühendisliği Bilimleri Dergisi / 2026 / Cilt: 5 Sayı: 2

Bozulabilir Ürünlerin Tedarik Zinciri Yönetiminde Optimizasyon Stratejileri: Bir Literatür Taraması

OPTIMIZATION STRATEGIES IN SUPPLY CHAIN MANAGEMENT OF PERISHABLE PRODUCTS : A LITERATURE REVIEW

Mesut Samastı(TUBITAK TUSSIDE)0000-0002-4900-8279
Tarık Küçükdeniz(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, MÜHENDİSLİK FAKÜLTESİ, ENDÜSTRİ MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-6670-1809
Sayfa
27–41
Yayın Tarihi
2023-05-30
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bu çalışma, çabuk bozulan gıda ürünleriyle ilgili araştırmaları tedarik zinciri etki alanına göre kategorilere ayırması bakımından diğer literatür incelemelerinden farklıdır.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Bu çalışma, 2012 ile 2022 yılları arasında çabuk bozulan gıda ürünleri hakkında yayınlanan akademik makalelerin kapsamlı bir incelemesini içermektedir.
📊
Temel Bulgular & Veri:Değerlendirme prosedürü boyunca ürünlerin sınıflandırılmasında tedarik, üretim, işleme, depolama ve tüketim yerleri dikkate alınmıştır.
💡
Sonuç & Katkı:Bu çalışma, çabuk bozulan gıda ürünleriyle ilgili araştırmaları tedarik zinciri etki alanına göre kategorilere ayırması bakımından diğer literatür incelemelerinden farklıdır.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:2012 Verileri
⚙️Değişkenler:Bozulabilir ürünler · tedarik zinciri yönetimi · optimizasyon

Özet

Bu çalışma, 2012 ile 2022 yılları arasında çabuk bozulan gıda ürünleri hakkında yayınlanan akademik makalelerin kapsamlı bir incelemesini içermektedir. Değerlendirme prosedürü boyunca ürünlerin sınıflandırılmasında tedarik, üretim, işleme, depolama ve tüketim yerleri dikkate alınmıştır. Çalışma kapsamında incelenen makaleler üzerinde, makalelerin zaman, coğrafya, indeks bilgileri ve atıf verilerine göre dağılımlarını içeren genel analizler yapılmıştır. Bozulabilir tedarik zinciri yönetiminde çalışmanın kapsamı, metodolojileri ve amaç fonksiyonu, detaylı analizde ilgili maddeler için sınıflandırılmıştır. Bu çalışma, çabuk bozulan gıda ürünleriyle ilgili araştırmaları tedarik zinciri etki alanına göre kategorilere ayırması bakımından diğer literatür incelemelerinden farklıdır.

Abstract

This study involved a comprehensive review of academic articles on perishable food products published between 2012 and 2022. The classification of the products throughout the evaluation procedure took into account the supply, manufacture, processing, storage, and consumption locations. General analyses were conducted on the articles reviewed within the purview of the study, including the distribution of the articles in terms of time, geography, index information, and citation data. The scope of the study in perishable supply chain management, the methodologies, and the objective function were classified for the pertinent articles in the detailed analysis. This study is distinct from other literature reviews in that it divides research on perishable food goods into categories based on the supply chain impact area.

🔖

Bu Makaleye Atıf Yap

Samastı, M., Küçükdeniz, T. (2023). Bozulabilir Ürünlerin Tedarik Zinciri Yönetiminde Optimizasyon Stratejileri: Bir Literatür Taraması. Uluslararası Anadolu Ziraat Mühendisliği Bilimleri Dergisi, Cilt: 5 Sayı: 2, 27–41. https://ajindex.com/articles/1564975

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (67)

  1. Accorsi, R., Gallo, A., Manzini, R. (2017). A climate driven decision- A climate driven decision-support model for the distribution of perishable p support model for the distribution of perishable products. Cleaner Product, 165, 917-929. https://doi.org/10.1016/j.jclepro.2017.07.170
  2. Ahumada, O., Villalobos, J.R., Mason, A.N. (2012). Tactical planning of the production and distribution of fresh agricultural products under uncertainty. Agricultural Systems, 112, 17-26. https://doi.org/10.1016/j.agsy.2012.06.002
  3. Albrecht, W., Steinrücke, M. (2017). Coordinating continuous-time distribution and sales planning of perishable goods with quality grades. International Journal of Production Research, 56(7), 2646-2665. https://doi.org/10.1080/00207543.2017.1384584
  4. Alkaabneh, F., Diabat, A., Gao, H.O. (2020). Benders decomposition for the inventory vehicle routing problem with perishable products and environmental costs. Computers and Operations Research, 113, 104751. https://doi.org/10.1016/j.cor.2019.07.009
  5. Amorim, P., Günther, H.O., Lobo, B.A. (2012). Multi-objective integrated production and distribution planning of perishable products. International Journal of Production Economics, 138(1), 89-101. https://doi.org/10.1016/j.ijpe.2012.03.005
  6. Amorim, P., Meyr, H., Almeder, C., Lobo, B.A. (2013). Managing perishability in production-distribution planning: a discussion and review. Flexible Services and Manufacturing Journal, 25, 389-413. https://doi.org/10.1007/s10696-011-9122-3
  7. Anitha, P., Patil, M.M. (2018). A review on data analytics for supply chain management: A case study. International Journal of Information Engineering and Electronic Business, 5, 30-39. DOI: 10.5815/ijieeb.2018.05.05
  8. AriaNezhad, M.G., Makuie, A., Khayatmoghadam, S. (2013). Developing and solving two-echelon inventory system for perishable items in a supply chain: case study. Journal of Industrial Engineering Int., 39, 1-10. https://doi.org/10.1186/2251-712X-9-39
  9. Azadeh, A., Elahi, S., Hosseinabadi Farahani, M., Nasirian, B. (2017). A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment. Computers and Industrial Engineering, 104, 124-133. https://doi.org/10.1016/j.cie.2016.12.019
  10. Basset, M.A., Manograna, G. Mohamed, M. (2018). Internet of things and its impact on supply chain: A framework for building smart. secure and efficient systems. Future Generation Computer Systems, 86, 614-628. https://doi.org/10.1016/j.future.2018.04.051
  11. Behzadi, G., Sullivan, M.J., Olsen, T.L., Zhang, A. (2017). Agribusiness supply chain risk management: A review of quantitative decision models. Omega, 79, 21-42. https://doi.org/10.1016/j.omega.2017.07.005
  12. Biuki, M., Kazami, A., Alineshad, A. (2020). An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. Journal of Cleaner Production, 260(120842), 1-14. https://doi.org/10.1016/j.jclepro.2020.120842
  13. Castro, J. A. O., Coronado, L. A. S., Lozano, A. M. P. (2017). Coupling facility location models in the supply chain of perishable fruits. Research in Transportation Business & Management, 24, 73-80. https://doi.org/10.1016/j.rtbm.2017.08.002
  14. Chao, C., Zhihui, T., Baozhen, Y. (2019). Optimization of two-stage location-routing-inventory problem with time-windows in food distribution network. Annals of Operations Research, 273, 111-134. https://doi.org/10.1007/s10479-017-2514-3
  15. Dai, Z., Aqlan, F., Zheng, X, Gao, K. (2018). A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Computers & Industrial Engineering, 119, 338-352. https://doi.org/10.1016/j.cie.2018.04.007
  16. Demirtaş, N., Tuzkaya, U.R. (2012). Strategic planning of layout of the distribution center: an approach for fruits and vegetables hall. Procedia - Social and Behavioral Sciences, 58, 159-168. https://doi.org/10.1016/j.sbspro.2012.09.989
  17. Diatha, K.S., Karumanchi, R., Garg, S. (2012). Mobile enabled operations management using multi-objective based logistics planning for perishable products. Computers and Industrial Engineering, 1-15.
  18. Drezner, Z., Scott, C.H. (2013). Location of a distribution center for a perishable product. Mathematical Methods of Operations Research, 78, 301-314. https://doi.org/10.1007/s00186-013-0445-6
  19. Dutta, P., Shrivastava, H. (2019). The design and planning of an integrated supply chain for perishable products under uncertainties case study in milk industry. Supply chain for perishable products, 15(4), 1301-1337. https://doi.org/10.1108/JM2-03-2019-0071
  20. Esteso, A., Alemany, M.M.E., Ortiz, A. (2021). Impact of product perishability on agri-food supply chains design. Applied Mathematical Modelling, 96, 20-38. https://doi.org/10.1016/j.apm.2021.02.027
  21. Etemadnia, H., Goetz, S.J., Canning, P., Tavallali, M.S. (2015). Optimal wholesale facilities location within the fruit and vegetables supply chain with bimodal transportation options: An LP-MIP heuristic approach. European Journal of Operational Research, 244(2), 648-661. https://doi.org/10.1016/j.ejor.2015.01.044
  22. Farahani, P., Grunow, M., Günther, H.O. (2012). Integrated production and distribution planning for perishable food products. Flexible Services and Manufacturing Journal, 24, 28-51. https://doi.org/10.1007/s10696-011-9125-0
  23. Flores, R.G., Filho, O.V.S., Martins, R.S., Martins, C.V.B., Juliao, P. (2015). Using logistic models to optimize the food supply chain (pp. 307-330). Modeling Food Processing Operations. https://doi.org/10.1016/B978-1-78242-284-6.00011-8
  24. Ganeshkumar, C., Pachayappan, M., Madanmohan, G. (2017). Agri-food supply chain management: Literature review. Intelligent Information Management, 9, 68-96. http://dx.doi.org/10.2139/ssrn.3093241
  25. Ge, H., Goetz, S., Canning, P., Perez, A. (2018). Optimal locations of fresh produce aggregation facilities in the United States with scale economies. International Journal of Production Economics, 197, 143-157. https://doi.org/10.1016/j.ijpe.2018.01.007
  26. Ge, H., Goetz, S.J., Clary, R., Yi, J., Gomez, M.I. (2022). Facility locations in the fresh produce supply chain: An integration of optimization and empirical methods. International Journal of Production Economics, 249, 108534. https://doi.org/10.1016/j.ijpe.2022.108534
  27. Ghezavati, V.R., Hooshyar, S., Moghaddam, R.T. (2015). A Benders’ decomposition algorithm for optimizing distribution of perishable products considering postharvest biological behavior in agri-food supply chain: a case study of tomato. CEJOR, 25, 29-54. ttps://doi.org/10.1007/s10100-015-0418-3
  28. Goli, A., Tirkolaee, E.B., Weber, G.W. (2020). A perishable product sustainable supply chain network design problem with lead time and customer satisfaction using a Hybrid Whale-Genetic Algorithm (pp. 99-124). Logistics Operations and Management for Recycling and Reuse. https://doi.org/10.1007/978-3-642-33857-1_6
  29. Govindan, K., Jafarian, A., Khodaverdi, R., Devika, K. (2013). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28. https://doi.org/10.1016/j.ijpe.2013.12.028
  30. Hiassat, A., Diabat, A., Rahwan, I. (2016). A genetic algorithm approach for location-inventory-routing problemwith perishable products. Journal of Manufacturing Systems, 42, 93-103. http://dx.doi.org/10.1016/j.jmsy.2016.10.004, 93–103
  31. Huh, W.T., Lall, U. (2013). Optimal crop choice, irrigation allocation, and the impact of contract farming. Production and Operations Management, 22(5), 1126–1143. https://doi.org/10.1111/poms.12007
  32. Ji, Y., Du, J., Han, X., Wu, X., Huang, R., Wang, S., Liu, Z. (2020). A mixed integer robust programming model for two-echelon inventory routing problem of perishable products. Physica A: Statistical Machanics and its Applications, 548, 124481. https://doi.org/10.1016/j.physa.2020.124481
  33. Jonkman, J., Povoa, A.P.B., Bloemhof, J.M. (2018). Integrating harvesting decisions in the design of agro-food supply chains. European Journal of Operational Research, 276(1), 247-258. https://doi.org/10.1016/j.ejor.2018.12.024
  34. Junqueira, R.A.R., Morabito, R. (2019). Modeling and solving a sugarcane harvest front scheduling problem. International Journal of Production Economics, 213, 150–160. https://doi.org/10.1016/j.ijpe.2019.03.009
  35. Kamble, S.S., Gunasekaran, A., Gawankar, S.A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194. https://doi.org/10.1016/j.ijpe.2019.05.022
  36. Keizer, M., Akkerman, R., Grunow, M., Bloemhof, J.M., Haijema, R., Vorst, J.G.A.J. (2017). Logistics network design for perishable products with heterogeneous quality decay. European Journal of Operational Research, 262(2), 353-549. https://doi.org/10.1016/j.ejor.2017.03.049
  37. Keizer, M., Haijema, R., Bloemhof, J.M., Van Der Vorst, J.G.A. (2015). Hybrid optimization and simulation to design a logistics network for distributing perishable products. Computers & Industrial Engineering, 88, 26-38. https://doi.org/10.1016/j.cie.2015.06.017
  38. Khalili-Damghani, K., Abtahi, A.R., Ghasemi, A. (2015). A new bi-objective location-routing problem for distribution of perishable products: evolutionary computation approach. Journal of Mathematical Modelling and Algorithms in Operations Research, 14(3), 287-312. https://DOI:10.1007/s10852-015-9274-3
  39. Khamjan, W., Khamjan, S., Pathumnakul, S. (2013). Determination of the locations and capacities of sugar cane loading stations in Thailand. Computers & Industrial Engineering, 66(6), 663–674. https://doi.org/10.1016/j.cie.2013.09.006
  40. Kieu, P.T., Nguyen, V.T., Ho, T.P. (2021). A Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Combined Compromise Solution (CoCoSo) Algorithm in distribution center location selection: A case study in agricultural supply chain. Axioms, 10(2), 53. https://doi.org/10.3390/axioms10020053
  41. Kumar, R., Ganapathy, L., Gokhale, R., Tiwari, M.K. (2020). Quantitative approaches for the integration of production and distribution planning in the supply chain: a systematic literature review. International Journal of Production Research, 58(11), 3527-3553. https://doi.org/10.1080/00207543.2020.1762019
  42. Kusumastuti, R.D., Donk, D.P., Teunter, R. (2016). Crop-related harvesting and processing planning: a review. International Journal Production Economics, 174, 76-92. https://doi.org/10.1016/j.ijpe.2016.01.010
  43. Lia, Y., Chu, F., Cote, J.F., Coelho, L.C., Chu, C. (2019). The multi-plant perishable food production routing with packaging consideration. International Journal of Production Economics, 221, 107472. https://doi.org/10.1016/j.ijpe.2019.08.007
  44. Liu, A., Zhu, Q., Xu, L., Lu, Q., Fan, Y (2021). Sustainable supply chain management for perishable products in emerging markets: An integrated location-inventory-routing model. Transportation Research Part E, Logistics and Transportatiın Review, 150, 102319. https://doi.org/10.1016/j.tre.2021.102319
  45. Manzini, R., Accorsi, R. (2012). The new conceptual framework for food supply chain assessment. Journal of Food Engineering, 115(2), 251–263. https://doi.org/10.1016/j.jfoodeng.2012.10.026
  46. Mirabelli, G., Solina, V. (2022). Optimization strategies for the integrated management of perishable supply chains: A literature review. Journal of Industrial Engineering and Management, 15(1), 58-91. http://dx.doi.org/10.3926/jiem.3603
  47. Morganti, E., Feliu, J.G. (2014). City logistics for perishable products. The case of the parma’s food hub. Case Studies on Transport Policy, 3(2), 120-128. https://doi.org/10.1016/j.cstp.2014.08.003
  48. Mönch, L., Uzsoy, R., Fowler, J.W. (2018). A survey of semiconductor supply chain models part I: semiconductor supply chains, strategic network design, and supply chain simulation. International Journal of Production Research, 56(13), 4524-4545. https://doi.org/10.1080/00207543.2017.1401233
  49. Musavi, M., Amiri, A.B. (2017). A multi-objective sustainable hub location-scheduling problem for perishable food supply chain. Computers & Industrial Engineering, 113, 766–778. https://doi.org/10.1016/j.cie.2017.07.039
  50. Nabil, O.M., Afia, N.H., Shihata, L.A. (2021). Sustainable supply chain design for perishable products: A literature review. Journal of Engineering, 49(1), 1 -22. DOI: 10.21608/jesaun.2020.133685
  51. Navazi, F., Sedahgat, A., Moghaddam, R.T. (2019). A new sustainable location-routing problem with simultaneous pickup and delivery by two-compartment vehicles for a perishable product considering circular economy. IFAC-Papers OnLine, 52(13), 790–795. https://doi.org/10.1016/j.ifacol.2019.11.212
  52. Onggo, B.S., Panadero, J., Corlu, C.G. (2019). Agri-food supply chains with stochastic demands: A multi-period inventory routing problem with perishable products. Simulation Modelling Practice and Theory, 97, 101970. https://doi.org/10.1016/j.simpat.2019.101970
  53. Öztürk, A. (2013). Yöneylem araştırmasının tarihi gelişimi ve özellikleri. Alphanumeric Journal, 1(1), 1-11.
  54. Pasandideh, S.H.R., Naderi, B., Majd, Z.R. (2017). Modeling and solving the integrated ILRP in a multi-period and multi-perishable product supply chain with uncertainty: lagrangian relaxation algorithm. Computers and Chemical Engineering, 109, 9-22. https://doi.org/10.1016/j.compchemeng.2017.10.013
  55. Rahbari, A., Nasiri, M.M., Werner, F., Musavi, M, Jolai, F. (2019). The vehicle routing and scheduling problem with cross-docking for perishable products under uncertainty: Two robust bi-objective models. Applied Mathematical Modelling, 70, 605–625. https://doi.org/10.1016/j.apm.2019.01.047
  56. Rashidi, S., Saghaei, A., Sadjadi, S.J., Nezhad, S.S. (2016). Optimizing supply chain network design with location-inventory decisions for perishable items: A Pareto-based MOEA approach. Scientia Iranica - Transactions E: Industrial Engineering, 23(6), 3035-3045. doi: 10.24200/SCI.2016.4009
  57. Shukla, M., Jharkharia, S. (2013). Agri-fresh produce supply chain management: a state-of-the-art literature review. International Journal of Operations & Production Management, 33(2), 114-158. https://doi.org/10.1108/01443571311295608
  58. Siddh, M.M., Son, G., Jain, R. (2015). Perishable food supply chain quality (PFSCQ) A structured review and implications for future research. Journal of Advances in Management Research, 12(3), 292-313. https://doi.org/10.1108/JAMR-01-2015-0002
  59. Suryawanshi, P., Dutta, P. (2021). Distribution planning problem of a supply chain of perishable products under disruptions and demand stochasticity. International Journal of Productivity and Performance Management, 72(1), 246-278. https://doi.org/10.1108/IJPPM-12-2020-0674
  60. Şen, İ.K. (2014). Lojistik faaliyetlerin yönetimi ve maliyetleme yaklaşımları. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 3(1), 83-106.
  61. Taşkıner, T., Bilgen, B. (2021). Optimization models for harvest and production planning in agri-food supply chain: A systematic review. Logistics, 5(3), 52. https://doi.org/10.3390/logistics5030052
  62. Toro, A., Gunnarsson, C., Lundin, G., Jonsson, N. (2012). Cereal harvesting e strategies and costs under variable weather conditions. Biosystems Engineering, 111(4), 429-439. https://doi.org/10.1016/j.biosystemseng.2012.01.010
  63. Vrat, P., Gupta, R., Bhatnagar, A., Pathak, D.K., Fulzele, V. (2018). Literature review analytics (LRA) on sustainable cold-chain for perishable food products: research trends and future directions. Opsearch 55, 601–627. https://doi.org/10.1007/s12597-018-0338-9
  64. Wang, C., Nhieu, N., Chung, Y., Pham, H. (2021). Multi-objective optimization models for sustainable perishable intermodal multi-product networks with delivery timewindow. Mathematics, 9(4), 379. https://doi.org/10.3390/math9040379
  65. Wu, T., Shen, H., Zhu, C. (2015). A multi-period location model with transportation economies-of-scale and perishable inventory. International Journal of Production Economics, 169, 343-349. https://doi.org/10.1016/j.ijpe.2015.08.018
  66. Yakavenka, V., MAllidis, I., Vlachos, d., Iakovou, E., Eleni, Z. (2020). Development of a multi‑objective model for the design of sustainable supply chains: the case of perishable food products. Annals of Operations Research, 294, 593–621. https://doi.org/10.1007/s10479-019-03434-5
  67. Zhang, H., Xiong, Y., He, M., Qu, C. (2017). Location model for distribution centers for fulfilling electronic orders of fresh foods under uncertain demand. Hindawi Scientific Programming. https://doi.org/10.1155/2017/3423562