Tedarik Zinciri Optimizasyon Çalışmaları: Literatür Araştırması ve Sınıflama

Tedarik Zinciri, tedarikçiler, üreticiler, dağıtıcılar ve toptancılar gibi bir grup organizasyonu birleştiren entegre bir süreçtir. Tedarik, üretim, dağıtım ve talep planlama konularını içerir. Bu konular stratejik, taktik ve operasyonel kararlar almayı gerektirir. Bu araştırma tedarik zinciri planlamasında hangi tedarik zinciri konularının, hangi karar/planlama seviyelerinin ve hangi optimizasyon metotlarının literatürde en çok çalışıldığını göstermektedir. Çalışma 1993 ve 2016 yılları arasındaki tedarik zinciri planlama konusundaki 77 adet çalışmanın incelenmesine ait sonuçları sunmaktadır. İncelenen çalışmalar şu kriterlere gore kategorize edilmiştir: karar seviyesi, tedarik zinciri optimizasyon konuları, amaçlar, optimizasyon modelleri

Supply Chain Optimization Studies: A Literature Review and Classification

Supply chain planning is an integrated process in which a group of several organizations, such as suppliers, producers, distributors and retailers, work together. It comprises procurement, production, distribution and demand planning topics. These topics require taking strategical, tactical and operational decisions. This research aims to reveal which supply chain topics, which decision levels, and which optimization methods are mostly studied in supply chain planning. This paper presents a total of 77 reviewed works published between 1993 and 2016 about supply chain planning. The reviewed works are categorized according to following elements: decision levels, supply chain optimization topics, objectives, optimization models.

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  • Adil, G. K., & Kanyalkar, A. P. (2007). Aggregate and detailed production planning integrating procurement and distribution plans in a multi-site environment. International Journal of Production Research, 45:5329-5353.
  • Adulyasak, Y., Cordeau, J.-F., & Jans, R. (2014). Optimization-based adaptive large neighborhood search for the production routing problem. Transportation Science, 48 (1): 20-45.
  • Amorim, P., Belo-Filho, M. A., Toledo, F. M., Almeder, C., & Almada-Lobo, B. (2013). Lot sizing versus batching in the production and distribution planning of perishable goods. International Journal of Production Economics, 146(1):208– 218.
  • Archetti, C., Bertazzi, L., Paletta, G., & Speranza, M. (2011). Analysis of the maximum level policy in a production-distribution system. Computers & Operations Research, 38:1731-1746.
  • Ardalan, Z., Karimi, S., Naderi, B., & Khamseh, A. A. (2016). Supply chain networks design with multi-mode demand satisfaction policy. Computers & Industrial Engineering, 96:108-117.
  • Armentanoa, V., Shiguemotob, A., & Lİkketangenc, A. (2011). Tabu search with path relinking for an integrated production–distribution problem. computers & Operations Research, 38(8): 1199–1209.
  • Bajgiran, O. S., Zanjani, M. K., & Nourelfath, M. (2016). The value of integrated tactical planning optimization in the lumber supply chain. International Journal of Production Economics, 171(1): 22-33.
  • Bard(a), J. F., & Nananukul (a), N. (2009). The integrated production–inventory– distribution–routing problem. Journal of Scheduling, 12: 257–280.
  • Bard(b), J. F., & Nananukul(b), N. (2009). Heuristics for a multiperiod inventory routing problem with production decisions. Computers & Industrial Engineering, 57: 713–723.
  • Bard(c), J. F., & Nananukul(c), N. (2010). A branch-and-price algorithm for an integrated production and inventory routing problem. Computers and Operations Research, 37(12): 2202-2217.
  • Bertazzi, L., Paletta, G., & Speranza, M. G. (2005). Minimizing the total cost in an integrated vendor—Managed inventory system. Journal of Heuristics, 11: 393- 419.
  • Bilgen, B., & Çelebi, Y. (2013). Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling . Annals of Operations Research, 211(1): 55-82.
  • Boudia (a), M., Louly(a), M. A., & Prins(a), C. (2008). Fast heuristics for a combined production planning and vehicle routing problem. Production Planning and Control. Production Planning & Control: The Management of Operations, 19:85:96.
  • Boudia (c), M., & Prins (c), C. (2009). A memetic algorithm with dynamic population management for an integrated production–distribution problem. European Journal of Operational Research, 195:703-715.
  • Boudia(a), M., Louly(a), M. A., & Prins(a), C. (2007). A reactive GRASP and path relinking for a combined production–distribution problem. Computers & Operations Research, 34 : 3402–3419.
  • Brahimia, N., & Aouamb, T. (2015). Multi-item production routing problem with backordering: a MILP approach. International Journal of Production Research, 54(4): 1076-1093.
  • Carvalho, D. M., & Nascimento, M. C. (2016). Lagrangian heuristics for the capacitated multi-plant lot sizing problem with multiple periods and items. Computers & Operations Research, 71: 137-148.
  • Chandra, P. (1993). A Dynamic Distribution Model with Warehouse and Customer Replenishment Requirements. The Journal of the Operational Research Society, 44:681-692.
  • Chen, H., Hsueh, C., & Chang, M. (2009). Production scheduling and vehicle routing with time windows for perishable food products. Computers & Operations Research, 36(7): 2311–2319.
  • Chen, M., & Wang, W. (1997). A linear programming model for integrated steel production and distribution planning. International Journal of Operations & Production Management, 17 (6): 592-610.
  • Chern, C., & Hsieh, J. (2007). A heuristic algorithm for master planning that satisŞes multiple objectives. Computers & Operations Research, 34:3491-3513.
  • Choudhary, D., & Shankar, R. (2014). A goal programming model for joint decision making of inventory lot-size, supplier selection and carrier selection. Computers & Industrial Engineering, 71: 1–9.
  • Çetinkaya, S., Üster, H., Easwaran, G., & Keskin, B. B. (2009). An integrated outbound logistics model for Frito-Lay: Coordinating aggregate-level production and distribution decisions. Interfaces, 39(5): 460-475.
  • Darvish, M., Larrain, H., & Coelho, L. C. (2016). A dynamic multi-plant lot-sizing and distribution problem. International Journal of Production Research, 1-12.
  • Dhaenens-Flipo, C., & Finke, G. (2001). An integrated model for an industrial production- distribution problem. IIE Transactions, 33 (9): 705-715.
  • Fahimnia, B., Farahani, R. Z., Marian, R., & Luong, L. (2013). A Review and Critique on Integrated Production-Distribution Planning Models and Techniques. Journal of Manufacturing Systems, 32:1-19.
  • FahimniaB., Davarzani, H., & Eshragh, A. (2015). Planning of complex supply chains: A performance comparison of three meta-heuristic algorithms. Computers https://doi.org/10.1016/j.cor.2015.10.008. Operations Research, in Press.,
  • Fisher, M., & Chandra, P. (1994). Coordination of production and distribution planning. European Journal of Operational Research, 72: 503-517.
  • Fumero, F., & Vercellis, C. (1999). Synchronized Development of Production, Inventory, and Distribution Schedules. Transportation Science, 33:330-340.
  • Garg, K., Kannan, D., Diabat, A., & Jhaa, P. (2015). A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design. Journal of Cleaner Production, 100(1): 297–314.
  • Gupta, A., & Maranas, C. (2003). Managing demand uncertainty in supply chain planning. Computers & Chemical Engineering, 27: 1219–1227.
  • Jang(a), Y., Jang(b), S., Chang, B., & Park, J. (2002). A Combined Model of Network Design and Production/Distribution Plannig for a Supply Network. Computers and Industrial Engineering, 43: 263-281.
  • Jayaraman, V., & Pirkul, H. (2001). Planning and coordination of production and distribution facilities for multiple commodities. European Journal of Operational Research, 133: 394-408.
  • Jolaia, F., Yazdian, S. A., Shahanaghib, K., & Khojastehc, M. A. (2011). Integrating fuzzy TOPSIS and multi-period goal programming for purchasing multiple products from multiple suppliers. Journal of Purchasing and Supply Management, 17(1): 42–53.
  • Jung, H., Jeong, B., & Lee, C. (2008). An order quantity negotiation model for distributor-driven supply chains. International Journal of Production Economics, 111 (1): 147–158.
  • Keskin Aydın, G., Omurca, S. İ., N., A., & Ekinci, E. (2015). A comparative study of production–inventory model for determining effective production quantity and safety stock level. Applied Mathematical Modelling, 39(20): 6359–6374.
  • Khakdaman, M., Wong, K. Y., Zohoori, B., Tiwari, M. K., & Merkert, R. (2014). Tactical production planning in a hybrid Make-to- Stock–Make-to-Order environment under supply, process and demand uncertainties: a robust optimisation model. International Journal of Production Research, 53(5): 1358- 1386.
  • Khalili-Damghani, K., & Tajik-Khaveh, M. (2015). Solving a multi-objective multi- echelon supply chain logistic design and planning problem by a goal programming approach. International Journal of Management Science and Engineering Management, 10(4): 242-252.
  • Kuhna, H., & Liskea, T. (2011). Simultaneous supply and production planning. International Journal of Production Research, 49(13): 3795-3813.
  • Lee(b), Y., Kim(b), S. H., & Moon, C. (2010). Production-distribution planning in supply chain using a hybrid approach. Production Planning & Control: The Management of Operations, 13:35-46.
  • Lei, L., Liu, S., Ruszczynski, A., & Park, S. (2006). On the integrated production,inventory, and distribution routing problem. IIE Transactions, 38: 955-970.
  • Leung, S., & Chan, S. S. (2009). A goal programming model for aggregate production planning with resource utilization constraint. Computers & Industrial Engineering, 56 (3): 1053–1064.
  • Liang, T. F. (2007). Applying fuzzy goal programming to production/transportation planning decisions in a supply chain. International Journal of Systems Science, 38(4): 293 - 304.
  • Liu, S. C., & Lee, S. B. (2003). A two-phase heuristic method for the multi-depot location routing problem taking inventory control decisions into consideration. The International Journal of Advanced Manufacturing Technology, 22: 941- 950.
  • Liu, X., Wang, W., & Peng, R. (2015). A novel two-stage Lagrangian decomposition approach for reŞnery production scheduling with operational transitions in mode switching. Chinese Journal of Chemical Engineering, 23:1793–1800.
  • Lucas, C., MirHassani, S., Mitra, G., & Poojari, C. (2001). An application of Lagrange relaxation to a capacity planning problem under uncertainty. Journal of the Operational Research Society, 52 (11): 1256-1266.
  • Martin, H., Denver, D. C., & James, C. E. (1993). Integrated production, distribution, and inventory planning at Libbey–Owens–Ford. Interfaces, 23:68-78.
  • Mcdonald, C., & Karimi, I. (1997). Planning and scheduling of parallel semicontinuous processes. Production planning. Industrial and Engineering Chemistry Research, 36, 2691–2700.
  • Mirzapour Al-e-hashema, S., . Malekly, H., & Aryanezhada, M. (2011). A multi- objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics, 34(1): 28-42.
  • Mu˜noz, E., Capón-García, E., Laínez-Aguirre, J. M., Espu˜na, A., & Puigjaner, L. (2015). Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment. Computers and Chemical Engineering, 72:52-67.
  • Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204 :377–390.
  • Nasiri, G. R., Zolfaghari, R., & Davoudpour, H. (2014). An integrated supply chain production–distribution planning with stochastic demands. Computers & Industrial Engineering, 77:35–45.
  • Nezhad, A. M., Manzour, H., & Salhi, S. (2013). Lagrangian relaxation heuristics for the uncapacitated single-source multi-product facility location problem. Int. J. Production Economics, 145:713–723.
  • OH, H.--., & Karimi, I. (2006). Global multiproduct production–distribution planning with duty drawbacks. AIChE Journal, 52: 595–610.
  • Ozdamar, L., & Yazgac, T. (2010). A hierarchical planning approach for a production- distribution system. International Journal of Production Research, 37: 3759- 3772.
  • Paksoy, T., & Chang, C. (2010). Revised multi-choice goal programming for multi- period, multi-stage inventory controlled supply chain model with popup stores in Guerrilla marketing. Applied Mathematical Modelling, 34(11): 3586–3598.
  • Pan, F., & Rakesh, N. (2013). Multi-echelon supply chain network design in agile manufacturing. Omega, 41: 969–983.
  • Park (b), Y. B., & Hong, S. C. (2009). Integrated production and distribution planning for single-period inventory products. International Journal of Computer Integrated Manufacturing, 22: 443-457.
  • Park(a), Y. B. (2007). An integrated approach for production and distribution planning in supply chain management. International Journal of Production Research, 43: 1205-1224.
  • Pasandideh, S. H., Niakib, S. T., & Asadia, K. (2015). Optimizing a bi-objective multi-product multi-period three echelon supply chain network with warehouse reliability. Expert Systems with Applications, 42(5):2615–2623.
  • Roghanian, E., Sadjadi, S., & Aryanezhad, M. (2007). A probabilistic bi-level linear multi-objective programming problem to supply chain planning. Applied Mathematics and Computation, 188 (1): 786–800.
  • Ryu, J., Dua, V., & Pistikopoulos, E. (2004). A bilevel programming framework for enterprise-wide process networks under uncertainty. Computers & Chemical Engineering, 28 (6–7):1121–1129.
  • Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28 (5): 581- 598.
  • Safaei, A. S., S.M., M. H., Z., F. R., F., J., & Ghodsypoura, S. (2010). Integrated multi-site production-distribution planning in supply chain by hybrid modelling. International Journal of Production Research, 48(14): 4043-4069.
  • Sakawa, M., Nishizaki, I., & Uemura, Y. (2001). Fuzzy programming and proŞt and cost allocation for a production and transportation problem. European Journal of Operational Research, 131 (1): 1–15.
  • Selim, H., Am, C., & Ozkarahan, I. (2008). Collaborative production–distribution planning in supply chain: a fuzzy goal programming approach. Transportation Research Part E: Logistics and Transportation Review, 44(3): 396–419.
  • Senoussi, A., K., M. N., Penz, B., Brahimi, N., & Dauz`ere-P´er`es, S. (2015). Modeling and solving a one-supplier multi-vehicle production-inventory- distribution problem with clustered retailers. The International Journal of Advanced Manufacturing Technology, 1-19.
  • Shi, J., Zhang, G., & Sha, J. (2012). A Lagrangian based solution algorithm for a build-to-order supply chain network design problem . Advances in Engineering Software, 49:21–28.
  • Shi, L., Jiang, Y., Wang, L., & Huang, D. (2015). A novel two-stage Lagrangian decomposition approach for reŞnery production scheduling with operational transitions in mode switching. Chinese Journal of Chemical Engineering, 23:1793–1800.
  • Shiguemoto, A. L., & Armentano, V. A. (2010). A tabu search procedure for coordinating production, inventory and distribution routing problems. International Transactions In Operational Research, 17:179-195.
  • Songsong, L., & Papageorgiou, L. G. (2013). Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry. Omega, 41(2): 369–382.
  • Stacey, J., Natarajarathinam, M., & Sox, C. (2007). The storage constrained, inbound inventory routing problem. International Journal of Physical Distribution & Logistics Management, 37: 484 – 500.
  • Swaminathan, J., & Tayur, S. (2003). Tactical planning models for supply chain management. Handbooks in Operations Research and Management Science 11,, 423–454.
  • Timpe, C., & Kallrath, J. (2000). Optimal planning in large multi-site production networks. European Journal of Operational Research, 126 (2): 422–435.
  • Torabi, S., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2): 193–214.
  • Varseia, M., & Polyakovskiy, S. (2015). Sustainable supply chain network design: A case of the wine industry in Australia. Omega , 1-12.
  • Zanjani, M. K., Bajgiran, O. S., & Nourelfath, M. (2016). A hybrid scenario cluster decomposition algorithm for supply chain tactical planning under uncertainty. European Journal of Operational Research, 252 :466–476.
  • Zare-Reisabadi, E., & Mirmohammadi, S. H. (2015). Site dependent vehicle routing problem with soft time window:Modeling and solution approach. Computers & Industrial Engineering, 177-185.
  • Zhang, Q., Shah, N., Wassick, J., Helling, R., & Egerschot, P. V. (2014). Sustainable supply chain optimisation: An industrial case study. Computers & Industrial Engineering, 74 :68–83.