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Etmen tabanlı modelleme yöntemi ile tesis içi dinamik bir milk-run sistemi

Yıl 2026, Cilt: 32 Sayı: 1

Öz

Geleneksel araç rotalama problemleri, belirli koşullar altında toplam rota maliyetini en aza indirecek bir çözüm bulmayı amaçlamaktadır. Ancak gerçek hayattaki problemler farklı dinamik durumları dikkate almaktadır. Bunlardan en yaygın olanı talebin dinamik olmasıdır. Dinamizmin ve değişken rotalar ve rota süreleri, dinamik envanter devirleri gibi dinamizmin etkilerinin yönetimi ile başa çıkabilmek tesis içi taşıma faaliyetlerinde önemli bir konu olmaktadır. Dinamik talep, tesis içindeki hareketlerin değişkenliğini artırmaktadır. Bu çalışmada dinamik talep altında tesis içi taşımacılık problemlerini çözmek için bir milk-run modeli geliştirilmiştir. Model, karmaşık ve dinamik sistemlerin modellenmesinde etkili bir yöntem olan etmen tabanlı yaklaşımla geliştirilmiştir. Modelin davranışı örnek bir durum üzerinden çeşitli senaryolarla analiz edilmiş ve modelin etkinliği ortaya koyulmuştur. Senaryolara dayalı olarak, ortalama doluluk oranı, ortalama mesafe ve ortalama bekleme süresi performans ölçütleri dikkate alındığında, sistemin büyüklüğüne uygun düşük tren kapasitesine sahip yüksek sayıda tren önerilmektedir.

Kaynakça

  • [1] Sadjadi SJ, Jafari M, Amini T. “A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study)”. International Journal of Advanced Manufacturing Technology, 44 (1–2), 194–200, 2009.
  • [2] Domingo R, Alvarez R, Pena MM, Calvo R. “Material flows improvement in a lean assembly line: a case study”. Assembly Automation, 27 (2), 141-147, 2007.
  • [3] Hanson R, Finnsgard C. “Impact of unit load size on in-plant materials supply efficiency”. International Journal of Production Economics, 133 (1), 312-318, 2011.
  • [4] Emde S, Boysen N. “Optimally routing and scheduling tow trains for JIT supply of mixed model assembly lines”, European Journal of Operational Research, 217, 287-299, 2012.
  • [5] Kilic HS, Durmusoglu MB, Baskak M. “Classification and modeling for in-plant milk-run distribution systems”. The International Journal of Advanced Manufacturing Technology, 62 (9–12), 1135–1146, 2012.
  • [6] Kilic HS, Durmusoglu MB. “A mathematical model and a heuristic approach for periodic material delivery in lean production environment”. The International Journal of Advanced Manufacturing Technology, 69(5–8), 977–992, 2013.
  • [7] Patel D, Dr. Patel MB. “Optimization approach of vehicle routing by a milk-run material supply system”. International Journal for Scientific Research & Development, 1 (6), 1357–1360, 2013.
  • [8] Satoglu SI, Sahin IE. “Design of a just-in-time periodic material supply system for the assembly lines and an application in electronics industry”. The International Journal of Advanced Manufacturing Technology, 65 (1–4), 319–332, 2013.
  • [9] Alnahhal N, Noche B. “Dynamic material flow control in mixed model assembly lines”. Computers & Industrial Engineering, 85, 110-119, 2015.
  • [10] Wu Q, Wang X, He YD, Xuan J, He WD. “A robust hybrid heuristic algorithm to solve multi-plant milk-run pickup problem with uncertain demand in automobile parts industry”. Advances in Production Engineering and Management, 13(2), 169–178, 2018.
  • [11] Golz J, Gujjula R, Günther HO, Rinderer, S, Ziegler M. “Part feeding at high-variant mixed-model assembly lines”. Flexible Services and Manufacturing Journal, 24 (2), 119–141, 2012.
  • [12] Adriano DD, Montez C, Novaes AGN, Wangham M. “Dmrvr: Dynamic milk-run vehicle routing solution using fog-based vehicular ad hoc networks”. Electronics (Switzerland), 9(12), 1–24, 2020.
  • [13] Bocewicz G, Banaszak Z, Rudnik K, Smutnicki C, Witczak M, Wójcik R. “An ordered-fuzzy-numbers-driven approach to the milk-run routing and scheduling problem”. Journal of Computational Science, 49, 2021.
  • [14] Sipahioğlu A, Altın İ. “A mathematical model for in-Plant Milk-Run routing”. Pamukkale University Journal of Engineering Sciences, 25(9), 1050-1055, 2019.
  • [15] Choi W, Lee Y. “A dynamic part-feeding system for an automotive assembly line”. Computers & Industrial Engineering, 43 (1), 123–134, 2002.
  • [16] Faccio M, Gamberi M, Persona A, Regattieri A, Sgarbossa F, “Design and simulation of assembly line feeding systems in the automotive sector using supermarket, kanbans and tow trains: a general framework”. Journal of Management Control, 24 (2), 187–208, 2013.
  • [17] Bae K HG, Evans LA, Summers A, “Lean design and analysis of a milkrun delivery system: case study”. Proceedings of the 2016 Winter Simulation Conference, 2855-2866, 2016.
  • [18] Korytkowski P, Karkoszka R. “Simulation-based efficiency analysis of an in-plant milk-run operator under disturbances”. The International Journal of Advanced Manufacturing Technology, 82 (5–8), 827–837, 2016.
  • [19] Emde S, Gendreau M. “Scheduling inhouse transport vehicles to feeds parts to automotive assembly lines”. European Journal of Operational Research, 260, 255-267, 2017.
  • [20] Guner AR, Murat A, Chinnam RB. “Dynamic routing for milk-run tours with time windows in stochastic time-dependent networks”. Transportation Research Part E: Logistics and Transportation Review, 97(Supplement C), 251–267, 2017.
  • [21] Fedorko G, Molnar V, Honus S, Neradilova H, Kampf R. “The application of simulation model of a milk run to identify the occurrence of failures”. International Journal of Simulation Modelling, 17(3), 444–457, 2018.
  • [22] Kluska K, Pawlewski P. “The use of simulation in the design of Milk-Run intralogistics systems”. IFAC-PapersOnLine, 51(11), 1428–1433, 2018.
  • [23] Aragão DP, Novaes AGN, Luna MMM. “An agent-based approach to evaluate collaborative strategies in milk-run OEM operations”. Computers & Industrial Engineering, 129, 545–555, 2019.
  • [24] Baykasoglu A, Gorkemli L. “Dynamic virtual cellular manufacturing through agent-based modelling”. International Journal of Computer Integrated Manufacturing, 30(6), 564–579, 2017.
  • [25] Becker T, Illigen C, McKelvey B, Hülsmann M, Windt K. “Using an agent-based neural-network computational model to improve product routing in a logistics facility”. International Journal of Production Economics, 174, 156–167, 2016.
  • [26] Lau HYK, Woo SO. “An agent-based dynamic routing strategy for automated material handling systems”. International Journal of Computer Integrated Manufacturing, 21(3), 269–288, 2008.
  • [27] Malekkhouyan S, Aghsami A, Rabbani M. “An integrated multi-stage vehicle routing and mixed-model job-shop-type robotic disassembly sequence scheduling problem for e-waste management system”. International Journal of Computer Integrated Manufacturing, 34(11), 1237–1262. 2021.
  • [28] Meyyappan L, Soylemezoglu A, Saygin C, Dagli CH. “A wasp-based control model for real-time routing of parts in a flexible manufacturing system”. International Journal of Computer Integrated Manufacturing, 21(3), 259–268, 2008.
  • [29] Zolfpour-Arokhlo M, Selamat A, Mohd Hashim SZ, Afkhami H. “Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms”. Engineering Applications of Artificial Intelligence, 29, 163–177, 2014.
  • [30] Chambers F, Serugendo GD, Cruz C. “Autonomous Generation of a Public Transportation Network by an Agent-Based Model: Mutual Enrichment with Knowledge Graphs for Sustainable Urban Mobility”. Sustainability, 16(20), 1-22, 2024.
  • [31] Sanogo K, Benhafssa AM, Sahnoun M, Bettayeb B, Abderrahim M, Bekrar A. “A multi-agent system simulation based approach for collision avoidance in integrated Job-Shop Scheduling Problem with transportation tasks”. Journal of Manufacturing Systems, 68, 209-226, 2023.
  • [32] Weiss G., “Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence”. Massachusetts Institute of Technology. London, 1999.

A dynamic in-plant milk-run system via agent-based modelling

Yıl 2026, Cilt: 32 Sayı: 1

Öz

Traditional vehicle routing problems aim to find a solution to minimize the total route cost under certain conditions. However, real-life problems consider different dynamic situations. One of the most common one is dynamic demand. To cope with the dynamism and management of its effects, such as unstable routes and route times, dynamic inventory turnovers are significant issues in in-plant transportation activities. The dynamic demand increases the dynamic movements within the facility. In this study, a milk-run model was developed to solve in-plant transportation problems under dynamic demand. The model was developed with an agent-based approach, which is an effective method in modeling complex and dynamic systems. The behavior of the model was analyzed with various scenarios through a sample case. As a result of the analysis, the efficiency of the model was shown. Based on the scenarios, considering performance measures such as average occupancy rate, average distance travelled, and average waiting time, a high number of trains with low train capacity suitable for the size of the system is recommended.

Kaynakça

  • [1] Sadjadi SJ, Jafari M, Amini T. “A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study)”. International Journal of Advanced Manufacturing Technology, 44 (1–2), 194–200, 2009.
  • [2] Domingo R, Alvarez R, Pena MM, Calvo R. “Material flows improvement in a lean assembly line: a case study”. Assembly Automation, 27 (2), 141-147, 2007.
  • [3] Hanson R, Finnsgard C. “Impact of unit load size on in-plant materials supply efficiency”. International Journal of Production Economics, 133 (1), 312-318, 2011.
  • [4] Emde S, Boysen N. “Optimally routing and scheduling tow trains for JIT supply of mixed model assembly lines”, European Journal of Operational Research, 217, 287-299, 2012.
  • [5] Kilic HS, Durmusoglu MB, Baskak M. “Classification and modeling for in-plant milk-run distribution systems”. The International Journal of Advanced Manufacturing Technology, 62 (9–12), 1135–1146, 2012.
  • [6] Kilic HS, Durmusoglu MB. “A mathematical model and a heuristic approach for periodic material delivery in lean production environment”. The International Journal of Advanced Manufacturing Technology, 69(5–8), 977–992, 2013.
  • [7] Patel D, Dr. Patel MB. “Optimization approach of vehicle routing by a milk-run material supply system”. International Journal for Scientific Research & Development, 1 (6), 1357–1360, 2013.
  • [8] Satoglu SI, Sahin IE. “Design of a just-in-time periodic material supply system for the assembly lines and an application in electronics industry”. The International Journal of Advanced Manufacturing Technology, 65 (1–4), 319–332, 2013.
  • [9] Alnahhal N, Noche B. “Dynamic material flow control in mixed model assembly lines”. Computers & Industrial Engineering, 85, 110-119, 2015.
  • [10] Wu Q, Wang X, He YD, Xuan J, He WD. “A robust hybrid heuristic algorithm to solve multi-plant milk-run pickup problem with uncertain demand in automobile parts industry”. Advances in Production Engineering and Management, 13(2), 169–178, 2018.
  • [11] Golz J, Gujjula R, Günther HO, Rinderer, S, Ziegler M. “Part feeding at high-variant mixed-model assembly lines”. Flexible Services and Manufacturing Journal, 24 (2), 119–141, 2012.
  • [12] Adriano DD, Montez C, Novaes AGN, Wangham M. “Dmrvr: Dynamic milk-run vehicle routing solution using fog-based vehicular ad hoc networks”. Electronics (Switzerland), 9(12), 1–24, 2020.
  • [13] Bocewicz G, Banaszak Z, Rudnik K, Smutnicki C, Witczak M, Wójcik R. “An ordered-fuzzy-numbers-driven approach to the milk-run routing and scheduling problem”. Journal of Computational Science, 49, 2021.
  • [14] Sipahioğlu A, Altın İ. “A mathematical model for in-Plant Milk-Run routing”. Pamukkale University Journal of Engineering Sciences, 25(9), 1050-1055, 2019.
  • [15] Choi W, Lee Y. “A dynamic part-feeding system for an automotive assembly line”. Computers & Industrial Engineering, 43 (1), 123–134, 2002.
  • [16] Faccio M, Gamberi M, Persona A, Regattieri A, Sgarbossa F, “Design and simulation of assembly line feeding systems in the automotive sector using supermarket, kanbans and tow trains: a general framework”. Journal of Management Control, 24 (2), 187–208, 2013.
  • [17] Bae K HG, Evans LA, Summers A, “Lean design and analysis of a milkrun delivery system: case study”. Proceedings of the 2016 Winter Simulation Conference, 2855-2866, 2016.
  • [18] Korytkowski P, Karkoszka R. “Simulation-based efficiency analysis of an in-plant milk-run operator under disturbances”. The International Journal of Advanced Manufacturing Technology, 82 (5–8), 827–837, 2016.
  • [19] Emde S, Gendreau M. “Scheduling inhouse transport vehicles to feeds parts to automotive assembly lines”. European Journal of Operational Research, 260, 255-267, 2017.
  • [20] Guner AR, Murat A, Chinnam RB. “Dynamic routing for milk-run tours with time windows in stochastic time-dependent networks”. Transportation Research Part E: Logistics and Transportation Review, 97(Supplement C), 251–267, 2017.
  • [21] Fedorko G, Molnar V, Honus S, Neradilova H, Kampf R. “The application of simulation model of a milk run to identify the occurrence of failures”. International Journal of Simulation Modelling, 17(3), 444–457, 2018.
  • [22] Kluska K, Pawlewski P. “The use of simulation in the design of Milk-Run intralogistics systems”. IFAC-PapersOnLine, 51(11), 1428–1433, 2018.
  • [23] Aragão DP, Novaes AGN, Luna MMM. “An agent-based approach to evaluate collaborative strategies in milk-run OEM operations”. Computers & Industrial Engineering, 129, 545–555, 2019.
  • [24] Baykasoglu A, Gorkemli L. “Dynamic virtual cellular manufacturing through agent-based modelling”. International Journal of Computer Integrated Manufacturing, 30(6), 564–579, 2017.
  • [25] Becker T, Illigen C, McKelvey B, Hülsmann M, Windt K. “Using an agent-based neural-network computational model to improve product routing in a logistics facility”. International Journal of Production Economics, 174, 156–167, 2016.
  • [26] Lau HYK, Woo SO. “An agent-based dynamic routing strategy for automated material handling systems”. International Journal of Computer Integrated Manufacturing, 21(3), 269–288, 2008.
  • [27] Malekkhouyan S, Aghsami A, Rabbani M. “An integrated multi-stage vehicle routing and mixed-model job-shop-type robotic disassembly sequence scheduling problem for e-waste management system”. International Journal of Computer Integrated Manufacturing, 34(11), 1237–1262. 2021.
  • [28] Meyyappan L, Soylemezoglu A, Saygin C, Dagli CH. “A wasp-based control model for real-time routing of parts in a flexible manufacturing system”. International Journal of Computer Integrated Manufacturing, 21(3), 259–268, 2008.
  • [29] Zolfpour-Arokhlo M, Selamat A, Mohd Hashim SZ, Afkhami H. “Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms”. Engineering Applications of Artificial Intelligence, 29, 163–177, 2014.
  • [30] Chambers F, Serugendo GD, Cruz C. “Autonomous Generation of a Public Transportation Network by an Agent-Based Model: Mutual Enrichment with Knowledge Graphs for Sustainable Urban Mobility”. Sustainability, 16(20), 1-22, 2024.
  • [31] Sanogo K, Benhafssa AM, Sahnoun M, Bettayeb B, Abderrahim M, Bekrar A. “A multi-agent system simulation based approach for collision avoidance in integrated Job-Shop Scheduling Problem with transportation tasks”. Journal of Manufacturing Systems, 68, 209-226, 2023.
  • [32] Weiss G., “Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence”. Massachusetts Institute of Technology. London, 1999.
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Görüşü ve Çoklu Ortam Hesaplama (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Yasemin Sevim 0000-0001-9284-8284

Latife Görkemli Aykut 0000-0002-8233-237X

Erken Görünüm Tarihi 2 Kasım 2025
Yayımlanma Tarihi 8 Kasım 2025
Gönderilme Tarihi 20 Ağustos 2024
Kabul Tarihi 2 Haziran 2025
Yayımlandığı Sayı Yıl 2026 Cilt: 32 Sayı: 1

Kaynak Göster

APA Sevim, Y., & Görkemli Aykut, L. (2025). A dynamic in-plant milk-run system via agent-based modelling. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 32(1). https://doi.org/10.5505/pajes.2025.39960
AMA Sevim Y, Görkemli Aykut L. A dynamic in-plant milk-run system via agent-based modelling. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Kasım 2025;32(1). doi:10.5505/pajes.2025.39960
Chicago Sevim, Yasemin, ve Latife Görkemli Aykut. “A dynamic in-plant milk-run system via agent-based modelling”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 32, sy. 1 (Kasım 2025). https://doi.org/10.5505/pajes.2025.39960.
EndNote Sevim Y, Görkemli Aykut L (01 Kasım 2025) A dynamic in-plant milk-run system via agent-based modelling. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 32 1
IEEE Y. Sevim ve L. Görkemli Aykut, “A dynamic in-plant milk-run system via agent-based modelling”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 32, sy. 1, 2025, doi: 10.5505/pajes.2025.39960.
ISNAD Sevim, Yasemin - Görkemli Aykut, Latife. “A dynamic in-plant milk-run system via agent-based modelling”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 32/1 (Kasım2025). https://doi.org/10.5505/pajes.2025.39960.
JAMA Sevim Y, Görkemli Aykut L. A dynamic in-plant milk-run system via agent-based modelling. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2025;32. doi:10.5505/pajes.2025.39960.
MLA Sevim, Yasemin ve Latife Görkemli Aykut. “A dynamic in-plant milk-run system via agent-based modelling”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 32, sy. 1, 2025, doi:10.5505/pajes.2025.39960.
Vancouver Sevim Y, Görkemli Aykut L. A dynamic in-plant milk-run system via agent-based modelling. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2025;32(1).





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