Bütünleşik Üretim ve Dağıtım Planlaması: İyileştirilmiş Bir Model ve Bütünleşik Planlamanın Değeri

Yıl 2019, Cilt: 19 Sayı: 1, 223 - 240, 01.05.2019

Oğuz Solyalı

https://doi.org/10.25294/auiibfd.559408

Öz

Bu
çalışma, sonlu bir planlama ufku boyunca bir üreticinin bir ürünü üretip birçok
perakendeciye türdeş bir araç filosu ile dağıttığı bir tedarik zinciri
problemini ele almaktadır. Amaç, her bir dönemde üreticideki üretim
miktarlarına, perakendecilere dağıtılacak ürün miktarlarına, kullanılacak
araçlara ve ziyaret edilecek perakendecilerin hangi araçlara atanacağına,
sistem maliyetini enazlayacak şekilde karar vermektir. Bu problem için
literatürde varolanlardan daha iyi sonuçlar veren bir karışık tam sayılı
doğrusal programlama modeli önerilmiştir. Ayrıca, üretim ve dağıtım
planlamasının bütünleşik ele alınması perakendecilerin kendi siparişlerini
verdikleri ve üreticinin planlamasını bu siparişlere göre yaptığı ardışık planlama
ile karşılaştırılmış ve bütünleşik planlamanın değeri değerlendirilmiştir.
Sayısal deney sonuçları bütünleşik planlamanın ardışık planlamaya göre ortalama
%8.9 ve en çok %28 maliyet tasarrufu sağladığını göstermektedir. 

Anahtar Kelimeler

Tedarik Zinciri Yönetimi, Üretim Planlama, Dağıtım Planlama, Karışık Tam Sayılı Doğrusal Programlama

Integrated Production and Distribution Planning: An Improved Formulation and the Value of Integrated Planning

Öz

This study considers a supply chain management problem in which a plant produces and distributes a product to multiple retailers using a homogeneous fleet of vehicles over a finite time horizon. The aim is to decide on the production quantities at the plant, delivery quantities to retailers, the set of vehicles to use and the assignment of retailers to vehicles in each period such that the system-wide costs are minimized. A mixed integer linear programming formulation of the problem outperforming the existing ones in the literature is proposed. This study also compares integrated production and distribution planning with the sequential planning in which retailers place their own orders and the plant makes its plan based on these orders, and assesses the value of integrated planning. The computational results indicate that average cost savings of 8.9% and maximum cost savings of 28% can be obtained with the integrated planning over the sequential planning.

Anahtar Kelimeler

Supply Chain Management, Production Planning, Distribution Planning, Mixed Integer Linear Programming

Kaynakça

• Absi, N., Archetti, C., Dauzere-Peres, S., Feillet, D. (2015). “A two-phase iterative heuristic approach for the production routing problem”. Transportation Science, 49: 784–795.
• Absi, N., Archetti, C., Dauzere-Peres, S., Feillet, D., Speranza, M.G. (2018). “Comparing sequential and integrated approaches for the production routing problem”. European Journal of Operational Research, 269: 633–646.
• Adulyasak, Y., Cordeau, J.-F., Jans, R. (2014). “Formulations and branch-and-cut algorithms for multivehicle production and inventory routing problems”. INFORMS Journal on Computing, 26: 103–120.
• Adulyasak, Y., Cordeau, J.-F., Jans, R. (2015a). “The production routing problem: a review of formulations and solution algorithms”. Computers & Operations Research, 55: 141–152.
• Adulyasak, Y., Cordeau, J.-F., Jans, R. (2015b). “Benders decomposition for production routing under demand uncertainty”. Operations Research, 63: 851–867.
• Archetti, C., Bertazzi, L., Paletta, G., Speranza, M.G. (2011). “Analysis of maximum level policy in a production-distribution system”. Computers & Operations Research, 38: 1731–1746.
• Barany, I., Van Roy T.J., Wolsey, L.A. (1984). “Uncapacitated lot sizing: the convex hull of solutions”. Mathematical Programming Studies, 22: 32–43.
• Campbell, A.M., Savelsbergh, M.W.P. (2004). “A decomposition approach for the inventory-routing problem”. Transportation Science, 38: 488–502.
• Chan, L.M.A., Muriel, A., Shen, Z.J.M., Simchi-Levi, D., Teo, C.P. (2002). “Effective zero-inventory ordering policies for the single-warehouse multiretailer problem with piecewise linear cost structures”. Management Science, 48: 1446–1460.
• Chandra, P., Fisher, M.L., (1994). “Coordination of production and distribution planning”. European Journal of Operational Research, 72: 503–517.
• Chitsaz, M., Cordeau, J.-F., Jans, R. (2019). “A unified decomposition matheuristic for assembly, production, and inventory routing”. INFORMS Journal on Computing, 31: 134–152.
• Cunha, J.O., Melo, R.A. (2016). “On reformulations for one-warehouse multi-retailer problem”. Annals of Operations Research, 238: 99–122.
• Federgruen, A., Tzur, M. (1999). “Time-partitioning heuristics: Application to one warehouse, multiitem, multiretailer lot-sizing problems”. Naval Research Logistics, 46: 463–486.
• Fumero, F., Vercellis C. (1999). “Synchronized development of production, inventory, and distribution schedules”. Transportation Science, 33: 330–340.
• Jin, Y., Muriel, A. (2009). “Single-warehouse multi-retailer inventory systems with full truckload shipments”. Naval Research Logistics, 56: 450–464.
• Kang, J.H., Kim, Y.D. (2010). “Coordination of inventory and transportation managements in a two-level supply chain”. International Journal of Production Economics, 123: 137–145.
• Levi, R., Roundy, R., Shmoys, D.B., Sviridenko, M. (2008). “A constant approximation algorithm for the one-warehouse multiretailer problem”. Management Science, 54: 763–776.
• Melo, R.A., Wolsey, L.A. (2012). “MIP formulations and heuristics for two-level production-transportation problems”. Computers & Operations Research, 39: 2776–2786.
• Qui, Y., Wang, L., Fang, X., Pardalos, P.M., Goldengorin B. (2018). “Formulations and branch-and-cut algorithms for production routing problems with time windows”. Transportmetrica A: Transport Science, 14: 669–690.
• Ruokokoski, M., Solyalı, O., Cordeau, J.-F., Jans, R., Süral, H. (2010). “Efficient formulations and a branch-and-cut algorithm for a production-routing problem”. GERAD Technical Report, G-2010-66, HEC Montreal, Canada, 1–43.
• Russell, R.A. (2017). “Mathematical programming heuristics for the production routing problem”. International Journal of Production Economics, 193: 40–49.
• Senoussi, A., Mouss, N.K., Penz, B., Brahimi, N., Dauzere-Peres, S. (2016). “Modeling and solving a one-supplier multi-vehicle production-inventory-distribution problem with clustered retailers”. International Journal of Advanced Manufacturing Technologies, 85: 971–989.
• Senoussi, A., Dauzere-Peres, S., Brahimi, N., Penz, B., Mouss, N.K. (2018). “Heuristics based on genetic algorithms for the capacitated multi vehicle production distribution problem”. Computers & Operations Research, 96: 108–119.
• Solyalı, O., Süral, H. (2012). “The one-warehouse multi-retailer problem: Reformulation, classification, and computational results”. Annals of Operations Research, 196: 517–541.
• Solyalı, O., Süral, H. (2017). “A multi-phase heuristic for the production routing problem”. Computers & Operations Research, 87: 114–124.