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Tarım gıda tedarik zinciri ağ tasarım problemi için çok amaçlı karma tamsayılı programlama modeli

Yıl 2025, Cilt: 31 Sayı: 2, 199 - 211, 29.04.2025

Bahtınur Akçay Ceren Küçükoğlu Bilge Bilgen

Öz

Tarım-gıda tedarik zinciri, mahsulün ekilmesi ve yetiştirilmesi, mahsullerin olgunlaşmasından sonra hasat edilmesi ve son olarak da mahsulün talep noktalarına teslim edilmesi gibi birçok faaliyeti içermektedir. Bu yüzden tedarik zinciri ağ tasarımında planlamalar çok önemlidir. Alınan tarımsal ve endüstriyel kararlar planlamaları etkilemektedir. Tarımsal kararlar, üretim ve hasat sürecini endüstriyel kararlar ise talebe uygun ekim planlarını, hasat planlarını, depolama ve taşıma faaliyetlerini etkilemektedir. Bu makale kapsamında, Çukurova Bölgesi’nde Osmaniye’nin Kadirli İlçesi’ndeki tarım arazilerinde yetişen ürünlerden ana ürün olarak bilinen yer fıstığı, mısır ve buğday için hasat çizelgeleme problemi ele alınmış ve stratejik-taktiksel planlama modeli oluşturulmuştur. Oluşturulan modelde minimum maliyet amacını sağlayacak şekilde ekim, hasat, taşıma ve depolama kararlarını içeren kısıtlar göz önünde bulundurulmuştur. Ele alınan problem için karma tam sayılı programlama modeli önerilmiştir. Bunun yanında süreci etkileyen farklı durumlar için senaryo analizleri yapılarak maliyete etkileri araştırılmıştır. Ayrıca elde edilecek hasat miktarının maksimum düzeyde tutulması amacı da dikkate alınarak problem artırılmış epsilon kısıtı yöntemi kullanılarak çözülmüştür.

Anahtar Kelimeler

Tarım-Gıda tedarik zinciri yönetimi StratejikTaktik planlama karar seviyeleri Karma tamsayılı programlama Artırılmış epsilon kısıtı yöntemi

Kaynakça

  • [1] Stephens EC, Jones AD, Parsons D. “Agricultural systems research and global food security in the 21st century: An overview and roadmap for future opportunities”. Agricultural Systems, 163, 1-6, 2018.
  • [2] Salman M, Pek E, Fereres E, García-Vila M. 2020. “Policy guide to improve water productivity in small-scale agriculture-The case of Burkina Faso, Morocco and Uganda”. Rome, Italy, FAO, 2020.
  • [3] Fikry I, Gheith M, Eltawil A. “An integrated productionlogistics-crop rotation planning model for sugar beet supply chains”. Computers & Industrial Engineering, 157, 107-300, 2021.
  • [4] Munhoz JR, Morabito R. "Optimization approaches to support decision making in the production planning of a citrus company: A Brazilian case study". Computers and Electronics in Agriculture, 107, 45-57, 2014.
  • [5] Costa AM, Santos LMR, Alem DJ, Santos RHS. "Sustainable vegetable crop supply problem with perishable stocks". Annals of Operations Research, 219, 265-283, 2014.
  • [6] Wishon C, Villalobos JR, Mason N, Flores H, Lujan G. "Use of MIP for planning temporary immigrant farm labor force". International Journal of Production Economics, 170, 25-33, 2015.
  • [7] Ghezavati VR, Hooshyar S, Tavakkoli-Moghaddam R. “A Benders’ decomposition algorithm for optimizing distribution of perishable products considering postharvest biological behavior in agri-food supply chain: a case study of tomato”. Central European Journal of Operations Research, 25, 29-54, 2017.
  • [8] Reis SA, Leal J E. "A deterministic mathematical model to support temporal and spatial decisions of the soybean supply chain". Journal of Transport Geography, 43, 48-58, 2015.
  • [9] Thuankaewsing S, Khamjan S, Piewthongngam K, Pathumnakul S. "Harvest scheduling algorithm to equalize supplier benefits: A case study from the Thai sugar cane industry". Computers and Electronics in Agriculture, 110, 42-55, 2015.
  • [10] Silva AF, Marins FAS, Dias EX. "Addressing uncertainty in sugarcane harvest planning through revised multi-choice goal programming model". Applied Mathematical Modelling, 39, 5540-5558, 2015.
  • [11] Rocco CD, Morabito R. "Production and logistics planning in the tomato processing industry: A conceptual scheme and mathematical model". Computers and Electronics in Agriculture, 127, 763-774, 2016.
  • [12] An K, Ouyang Y. "Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium". Transportation Research Part E, 88, 110-128, 2016.
  • [13] Rocco CD, Morabito R. "Robust optimisation approach applied to the analysis of production/logistics and crop planning in the tomato processing industry". International Journal of Production Research, 54(19), 5842-5861, 2016.
  • [14] Accorsi R, Cholette S, Manzini R, Pini C, Penazzi S. "The land-network problem: ecosystem carbon balance in planning sustainable agro-food supply chains". Journal of Cleaner Production, 112, 158-171, 2016.
  • [15] Catalá LP, Moreno MS, Blanco AM, Bandoni JA. "A biobjective optimization model for tactical planning in the pome fruit industry supply chain". Computers and Electronics in Agriculture, 130, 128-141, 2016.
  • [16] Herrera-Cáceres C, Pérez-Galarce F, Álvarez-Miranda E, Candia-Véjar A. "Optimization of the harvest planning in the olive oil production: A case study in Chile". Computers and Electronics in Agriculture, 141, 147-159, 2017.
  • [17] Varsei M, Polyakovskiy S. "Sustainable supply chain network design: A case of the wine industry in Australia". Omega, 66, 236-247, 2017.
  • [18] Caixeta-Filho JV. "Orange harvesting scheduling management: a case study". Journal of the Operational Research Society, 57(6), 637-642, 2006.
  • [19] Grillo H, Alemany MME, Ortiz A, Fuertes-Miquel VS. "Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products". Applied Mathematical Modelling, 49, 255-278, 2017.
  • [20] Gholamian MR, Taghanzadeh AH. "Integrated network design of wheat supply chain: A real case of Iran". Computers and Electronics in Agriculture, 140, 139-147, 2017.
  • [21] Miranda-Ackerman MA, Azzaro-Pantel C, Aguilar-Lasserre AA. "A green supply chain network design framework for the processed food industry: Application to the orange juice agrofood cluster". Computers & Industrial Engineering, 109, 369-389, 2017.
  • [22] Basso F, Varas M. "A MIP formulation and a heuristic solution approach for the bottling scheduling problem in the wine industry". Computers & Industrial Engineering, 105, 136-145, 2017.
  • [23] Allaoui H, Guo Y, Choudhary A, Bloemhof J. "Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach". Computers and Operations Research, 89, 369-384, 2018.
  • [24] Varas M, Basso F, Maturana S, Osorio D, Pezoa R. “A multiobjective approach for supporting wine grape harvest operations”. Computers & Industrial Engineering, 145, 106497, 2020.
  • [25] Cheraghalipour A, Paydar MM, Hajiaghaei-Keshteli M. "A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms". Applied Soft Computing, 69, 33-59, 2018.
  • [26] Flores H, Villalobos JR. "A modeling framework for the strategic design of local fresh-food systems". Agricultural Systems, 161, 1-15, 2018.
  • [27] Sazvar Z, Rahmani M, Govindan K. "A sustainable supply chain for organic, conventional agro-food products: The role of demand substitution, climate change and public health". Journal of Cleaner Production, 194, 564-583, 2018.
  • [28] Solano NEC, Llinás GAG, Montoya-Torres JR. "Towards the integration of lean principles and optimization for agricultural production systems: a conceptual review proposition". Journal of the Science of Food and Agriculture, 100, 453-464, 2020.
  • [29] Jerić SV, Šorić K." "Multi-objective optimization for the integrated supply and production planning in olive oil industry". Entrepreneurship and Economic Issues, 32(1), 129-138, 2019.
  • [30] Junqueira RÁR, Morabito R. "Modeling and solving a sugarcane harvest front scheduling problem". International Journal of Production Economics, 213, 150-160, 2019.
  • [31] Suthar RG, Barrera JI, Judge J, Brecht JK, Pelletier W, Muneepeerakul R. "Modeling postharvest loss and water and energy use in Florida tomato operations". Postharvest Biology and Technology, 153, 61-68, 2019.
  • [32] Jonkmana J, Barbosa-Póvoa AP, Bloemhof JM. "İntegrating harvesting decisions in the design of agro-food supply chains". European Journal of Operational Research, 276, 247-258, 2019.
  • [33] He P, Li J. "The two-echelon multi-trip vehicle routing problem with dynamic satellites for crop harvesting and transportation". Applied Soft Computing Journal, 77, 387-398, 2019.
  • [34] Banasik A, Kanellopoulos A, Bloemhof-Ruwaard JM, Claassen G DH. "Accounting for uncertainty in eco-efficient agri-food supply chains: A case study for mushroom production planning". Journal of Cleaner Production, 216, 249-256, 2019.
  • [35] Roghanian E, Cheraghalipour A. "Addressing a set of metaheuristics to solve a multi-objective model for closed-loop citrus supply chain considering CO2 emissions". Journal of Cleaner Production, 239, 118081, 2019.
  • [36] Flores H, Villalobos JR. "A stochastic planning framework for the discovery of complementary agricultural systems". European Journal of Operational Research, 280, 707-729, 2020.
  • [37] Alemany MM, Esteso A, Ortiz A, Pino M. "Centralized and distributed optimization models for the multi-farmer crop planning problem under uncertainty: Application to a fresh tomato Argentinean supply chain case study." Computers and Industrial Engineering, 153, 107048, 2021.
  • [38] Fikry I, Gheith M, Eltawil A. "An integrated productionlogistics-crop rotation planning model for sugar beet supply chains." Computers and Industrial Engineering, 157, 107300, 2021.
  • [39] Avanzini EL, Cawley AFM, Vera JR, Maturana S. "Comparing an expected value with a multistage stochastic optimization approach for the case of wine grape harvesting operations with quality degradation." International Transactions in Operational Research, 30, 1843-1873, 2021.
  • [40] Esteso A, Alemany MM, Ortiz A. "Sustainable agri-food supply chain planning through multi-objective optimization". Journal of Decision Systems, 33(4), 808-832, 2023.
  • [41] Montenegro-Dos Santos F, Pérez-Galarce F, MonardesConcha C, Candia-Véjar A, Seido-Nagano M, Gómez-Lagos J. "A rolling horizon scheme for rescheduling in agricultural harvest." Computers and Electronics in Agriculture, 215, 108392, 2023.
  • [42] Mavrotas G. “Effective implementation of the e-constraint method in Multi-Objective Mathematical Programming problems”. Applied Mathematics and Computation, 213, 455-465, 2009.

Multi-objective mixed integer programming model for agriculture food supply chain network design problem

Yıl 2025, Cilt: 31 Sayı: 2, 199 - 211, 29.04.2025

Bahtınur Akçay Ceren Küçükoğlu Bilge Bilgen

Öz

Agri-food supply chain includes many activities such as planting and growing the crop, harvesting the crops after they mature, and finally delivering the crop to the demand points. Therefore, planning is very important in supply chain network design. The agricultural and industrial decisions considered affect the planning. Agricultural decisions affect the production and harvesting process, while industrial decisions affect the planting plans, harvest plans, storage and transportation activities according to the demand. Within the scope of this article, the harvest scheduling problem for peanut, corn and wheat, which are known as the main products grown in the agricultural lands of Kadirli District of Osmaniye in the Çukurova Region, was discussed and a strategic-tactical planning model was proposed. In the model, constraints including planting, harvesting, transportation and storage decisions were taken into consideration to ensure minimum cost. A mixed integer programming model is proposed for the considered problem. In addition, scenario analysis were adopted for different situations affecting the process and their effects on the cost were investigated. In addition considering the aim of maximizing the amount of harvest to be obtained, the problem is solved using the augmented epsilon constraint method.

Anahtar Kelimeler

Agri-Food supply chain management Strategic-Tactical planning decision levels Mixed integer programming Augmented epsilon constraint method

Kaynakça

  • [1] Stephens EC, Jones AD, Parsons D. “Agricultural systems research and global food security in the 21st century: An overview and roadmap for future opportunities”. Agricultural Systems, 163, 1-6, 2018.
  • [2] Salman M, Pek E, Fereres E, García-Vila M. 2020. “Policy guide to improve water productivity in small-scale agriculture-The case of Burkina Faso, Morocco and Uganda”. Rome, Italy, FAO, 2020.
  • [3] Fikry I, Gheith M, Eltawil A. “An integrated productionlogistics-crop rotation planning model for sugar beet supply chains”. Computers & Industrial Engineering, 157, 107-300, 2021.
  • [4] Munhoz JR, Morabito R. "Optimization approaches to support decision making in the production planning of a citrus company: A Brazilian case study". Computers and Electronics in Agriculture, 107, 45-57, 2014.
  • [5] Costa AM, Santos LMR, Alem DJ, Santos RHS. "Sustainable vegetable crop supply problem with perishable stocks". Annals of Operations Research, 219, 265-283, 2014.
  • [6] Wishon C, Villalobos JR, Mason N, Flores H, Lujan G. "Use of MIP for planning temporary immigrant farm labor force". International Journal of Production Economics, 170, 25-33, 2015.
  • [7] Ghezavati VR, Hooshyar S, Tavakkoli-Moghaddam R. “A Benders’ decomposition algorithm for optimizing distribution of perishable products considering postharvest biological behavior in agri-food supply chain: a case study of tomato”. Central European Journal of Operations Research, 25, 29-54, 2017.
  • [8] Reis SA, Leal J E. "A deterministic mathematical model to support temporal and spatial decisions of the soybean supply chain". Journal of Transport Geography, 43, 48-58, 2015.
  • [9] Thuankaewsing S, Khamjan S, Piewthongngam K, Pathumnakul S. "Harvest scheduling algorithm to equalize supplier benefits: A case study from the Thai sugar cane industry". Computers and Electronics in Agriculture, 110, 42-55, 2015.
  • [10] Silva AF, Marins FAS, Dias EX. "Addressing uncertainty in sugarcane harvest planning through revised multi-choice goal programming model". Applied Mathematical Modelling, 39, 5540-5558, 2015.
  • [11] Rocco CD, Morabito R. "Production and logistics planning in the tomato processing industry: A conceptual scheme and mathematical model". Computers and Electronics in Agriculture, 127, 763-774, 2016.
  • [12] An K, Ouyang Y. "Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium". Transportation Research Part E, 88, 110-128, 2016.
  • [13] Rocco CD, Morabito R. "Robust optimisation approach applied to the analysis of production/logistics and crop planning in the tomato processing industry". International Journal of Production Research, 54(19), 5842-5861, 2016.
  • [14] Accorsi R, Cholette S, Manzini R, Pini C, Penazzi S. "The land-network problem: ecosystem carbon balance in planning sustainable agro-food supply chains". Journal of Cleaner Production, 112, 158-171, 2016.
  • [15] Catalá LP, Moreno MS, Blanco AM, Bandoni JA. "A biobjective optimization model for tactical planning in the pome fruit industry supply chain". Computers and Electronics in Agriculture, 130, 128-141, 2016.
  • [16] Herrera-Cáceres C, Pérez-Galarce F, Álvarez-Miranda E, Candia-Véjar A. "Optimization of the harvest planning in the olive oil production: A case study in Chile". Computers and Electronics in Agriculture, 141, 147-159, 2017.
  • [17] Varsei M, Polyakovskiy S. "Sustainable supply chain network design: A case of the wine industry in Australia". Omega, 66, 236-247, 2017.
  • [18] Caixeta-Filho JV. "Orange harvesting scheduling management: a case study". Journal of the Operational Research Society, 57(6), 637-642, 2006.
  • [19] Grillo H, Alemany MME, Ortiz A, Fuertes-Miquel VS. "Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products". Applied Mathematical Modelling, 49, 255-278, 2017.
  • [20] Gholamian MR, Taghanzadeh AH. "Integrated network design of wheat supply chain: A real case of Iran". Computers and Electronics in Agriculture, 140, 139-147, 2017.
  • [21] Miranda-Ackerman MA, Azzaro-Pantel C, Aguilar-Lasserre AA. "A green supply chain network design framework for the processed food industry: Application to the orange juice agrofood cluster". Computers & Industrial Engineering, 109, 369-389, 2017.
  • [22] Basso F, Varas M. "A MIP formulation and a heuristic solution approach for the bottling scheduling problem in the wine industry". Computers & Industrial Engineering, 105, 136-145, 2017.
  • [23] Allaoui H, Guo Y, Choudhary A, Bloemhof J. "Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach". Computers and Operations Research, 89, 369-384, 2018.
  • [24] Varas M, Basso F, Maturana S, Osorio D, Pezoa R. “A multiobjective approach for supporting wine grape harvest operations”. Computers & Industrial Engineering, 145, 106497, 2020.
  • [25] Cheraghalipour A, Paydar MM, Hajiaghaei-Keshteli M. "A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms". Applied Soft Computing, 69, 33-59, 2018.
  • [26] Flores H, Villalobos JR. "A modeling framework for the strategic design of local fresh-food systems". Agricultural Systems, 161, 1-15, 2018.
  • [27] Sazvar Z, Rahmani M, Govindan K. "A sustainable supply chain for organic, conventional agro-food products: The role of demand substitution, climate change and public health". Journal of Cleaner Production, 194, 564-583, 2018.
  • [28] Solano NEC, Llinás GAG, Montoya-Torres JR. "Towards the integration of lean principles and optimization for agricultural production systems: a conceptual review proposition". Journal of the Science of Food and Agriculture, 100, 453-464, 2020.
  • [29] Jerić SV, Šorić K." "Multi-objective optimization for the integrated supply and production planning in olive oil industry". Entrepreneurship and Economic Issues, 32(1), 129-138, 2019.
  • [30] Junqueira RÁR, Morabito R. "Modeling and solving a sugarcane harvest front scheduling problem". International Journal of Production Economics, 213, 150-160, 2019.
  • [31] Suthar RG, Barrera JI, Judge J, Brecht JK, Pelletier W, Muneepeerakul R. "Modeling postharvest loss and water and energy use in Florida tomato operations". Postharvest Biology and Technology, 153, 61-68, 2019.
  • [32] Jonkmana J, Barbosa-Póvoa AP, Bloemhof JM. "İntegrating harvesting decisions in the design of agro-food supply chains". European Journal of Operational Research, 276, 247-258, 2019.
  • [33] He P, Li J. "The two-echelon multi-trip vehicle routing problem with dynamic satellites for crop harvesting and transportation". Applied Soft Computing Journal, 77, 387-398, 2019.
  • [34] Banasik A, Kanellopoulos A, Bloemhof-Ruwaard JM, Claassen G DH. "Accounting for uncertainty in eco-efficient agri-food supply chains: A case study for mushroom production planning". Journal of Cleaner Production, 216, 249-256, 2019.
  • [35] Roghanian E, Cheraghalipour A. "Addressing a set of metaheuristics to solve a multi-objective model for closed-loop citrus supply chain considering CO2 emissions". Journal of Cleaner Production, 239, 118081, 2019.
  • [36] Flores H, Villalobos JR. "A stochastic planning framework for the discovery of complementary agricultural systems". European Journal of Operational Research, 280, 707-729, 2020.
  • [37] Alemany MM, Esteso A, Ortiz A, Pino M. "Centralized and distributed optimization models for the multi-farmer crop planning problem under uncertainty: Application to a fresh tomato Argentinean supply chain case study." Computers and Industrial Engineering, 153, 107048, 2021.
  • [38] Fikry I, Gheith M, Eltawil A. "An integrated productionlogistics-crop rotation planning model for sugar beet supply chains." Computers and Industrial Engineering, 157, 107300, 2021.
  • [39] Avanzini EL, Cawley AFM, Vera JR, Maturana S. "Comparing an expected value with a multistage stochastic optimization approach for the case of wine grape harvesting operations with quality degradation." International Transactions in Operational Research, 30, 1843-1873, 2021.
  • [40] Esteso A, Alemany MM, Ortiz A. "Sustainable agri-food supply chain planning through multi-objective optimization". Journal of Decision Systems, 33(4), 808-832, 2023.
  • [41] Montenegro-Dos Santos F, Pérez-Galarce F, MonardesConcha C, Candia-Véjar A, Seido-Nagano M, Gómez-Lagos J. "A rolling horizon scheme for rescheduling in agricultural harvest." Computers and Electronics in Agriculture, 215, 108392, 2023.
  • [42] Mavrotas G. “Effective implementation of the e-constraint method in Multi-Objective Mathematical Programming problems”. Applied Mathematics and Computation, 213, 455-465, 2009.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Gıda Mühendisliği
Bölüm Makale
Yazarlar

Bahtınur Akçay

Ceren Küçükoğlu

Bilge Bilgen

Yayımlanma Tarihi 29 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 31 Sayı: 2

Kaynak Göster

APA Akçay, B., Küçükoğlu, C., & Bilgen, B. (2025). Tarım gıda tedarik zinciri ağ tasarım problemi için çok amaçlı karma tamsayılı programlama modeli. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 31(2), 199-211.
AMA Akçay B, Küçükoğlu C, Bilgen B. Tarım gıda tedarik zinciri ağ tasarım problemi için çok amaçlı karma tamsayılı programlama modeli. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Nisan 2025;31(2):199-211.
Chicago Akçay, Bahtınur, Ceren Küçükoğlu, ve Bilge Bilgen. “Tarım gıda Tedarik Zinciri Ağ tasarım Problemi için çok amaçlı Karma tamsayılı Programlama Modeli”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 31, sy. 2 (Nisan 2025): 199-211.
EndNote Akçay B, Küçükoğlu C, Bilgen B (01 Nisan 2025) Tarım gıda tedarik zinciri ağ tasarım problemi için çok amaçlı karma tamsayılı programlama modeli. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 31 2 199–211.
IEEE B. Akçay, C. Küçükoğlu, ve B. Bilgen, “Tarım gıda tedarik zinciri ağ tasarım problemi için çok amaçlı karma tamsayılı programlama modeli”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 31, sy. 2, ss. 199–211, 2025.
ISNAD Akçay, Bahtınur vd. “Tarım gıda Tedarik Zinciri Ağ tasarım Problemi için çok amaçlı Karma tamsayılı Programlama Modeli”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 31/2 (Nisan 2025), 199-211.
JAMA Akçay B, Küçükoğlu C, Bilgen B. Tarım gıda tedarik zinciri ağ tasarım problemi için çok amaçlı karma tamsayılı programlama modeli. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2025;31:199–211.
MLA Akçay, Bahtınur vd. “Tarım gıda Tedarik Zinciri Ağ tasarım Problemi için çok amaçlı Karma tamsayılı Programlama Modeli”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 31, sy. 2, 2025, ss. 199-11.
Vancouver Akçay B, Küçükoğlu C, Bilgen B. Tarım gıda tedarik zinciri ağ tasarım problemi için çok amaçlı karma tamsayılı programlama modeli. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2025;31(2):199-211.





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