Çağrı KOÇ, Eren ÖZCEYLAN, Saadettin Erhan KESEN, Zeynel Abidin ÇİL, Süleyman METE

İleri tedarik zinciri ağ tasarımı problemi: Sezgisel yaklaşımlar

Tedarik zinciri içindeki tesislerin yerlerinin belirlenmesi, aralarındaki ürün akışlarının maliyeti minimize edecek şekilde optimize edilmesi tedarik zinciri ağ tasarımı (TZAT) problemi olarak karşımıza çıkmaktadır. TZAT problemleri NP-zor sınıfına girmektedir. Dolayısıyla çoğu araştırmacı tarafından üzerinde çalışılan bir konudur. Ancak literatürde araştırmacıların adil karşılaştırmalar yapabileceği test problemler mevcut değildir. Bu sebeple, küçük boyuttan büyük boyuta kadar iki, üç ve dört aşamalı olmak üzere 450 adet TZAT test problemi geliştirilmiş, matematiksel olarak da modellenmiştir. Problemin çözüm karmaşıklığından dolayı biri genetik algoritma diğeri de melez sezgisel bir yaklaşım olmak üzere iki farklı çözüm yöntemi önerilmiştir. Önerilen yaklaşımlar geliştirilen test problemlere uygulanmış ve karşılaştırmalar yapılmıştır. Elde edilen sonuçlara göre önerilen sezgisel yaklaşımlar küçük boyutlu problemler için CPLEX ile elde edilen optimal sonuçları yakalamış, büyük boyutlu problemler için ise çok daha kısa sürede kabul edilebilir sonuçlar elde etmiştir.

Anahtar Kelimeler:

Tedarik zinciri ağ tasarımı, karma tamsayılı programlama, genetik algoritma, melez sezgisel algoritma.

Forward supply Chain network design problem: Heuristic approaches

Determining positions and counting of actors, amount of product flow between and decreasing transportation costs are handled as a network design problem in supply chain management. Supply chain network design (SCND) problem belongs to the class of NP-hard problems. It has therefore appealed to a number of researchers’ close attention. However, existing literature lacks of common benchmark instances for forward SCND problems so as to make a fair comparison between developed and applied heuristic approaches. To this end, 450 new benchmark instances ranging from small to large size for forward SCND problems with two, three and four-echelon are generated and a mathematical model for each of the problems is formulated. Due to the complexity issues, we develop two heuristic solution approaches, genetic algorithm (GA) and hybrid heuristic algorithm (HHA), and we apply them to the large pool of benchmark instances. Comparative experiments show that both the GA and HHA can yield feasible solutions in much less computational time and, in particular, outperforms CPLEX regarding the solution quality as the number of echelon grows.

Keywords:

Supply chain network design, mixed integer programming, genetic algorithm, hybrid heuristic algorithm,

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Paksoy T, Bektaş T, Özceylan E. “Operational and environmental performance measures in a multi-product closed-loop supply Chain”. Transportation Research Part E, 47(4), 532-546, 2011.
Alayet C, Lehoux N, Lebel L, Bouchard M. “Centralized supply chain planning model for multiple forest companies”. INFOR: Information Systems and Operational Research, 54(3), 171-191, 2016.
Ashtab S, Caron RJ, Selvarajah E. “A characterization of alternate optimal solutions for a supply chain network design model”. INFOR: Information Systems and Operational Research, 53(2), 90-93, 2015.
Özceylan E, Paksoy T, Bektaş T. “Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing”. Transportation Research Part E, 61, 142-164, 2014.
Demirel N, Gökçen H. “A mixed integer programming model for remanufacturing in reverse logistics environment”. International Journal of Advanced Manufacturing Technology, 39(11-12), 1197-1206, 2008.
Tari I, Alumur SA. “Collection center location with equity considerations in reverse logistics networks”. INFOR: Information Systems and Operational Research, 52(4), 157-173, 2014.
Soleimani H, Govindan K, Saghafi H, Jafari H. “Fuzzy multi-objective sustainable and green closed-loop supply chain network design”. Computers & Industrial Engineering, 109, 191-203, 2017.
Syarif A, Yun Y, Gen M. “Study on multi-stage logistics chain network: A spanning tree-based genetic algorithm approach”. Computers & Industrial Engineering, 43(1-2), 299-314, 2002.
Altıparmak F, Gen M, Lin L, Karaoğlan I. “A steady-state genetic algorithm for multi-product supply chain network design”. Computers & Industrial Engineering, 56(2), 521-537, 2009.
Paksoy T, Chang CT. “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, 2010.
Paksoy T, Özceylan E, Weber GW. “Profit oriented supply chain network optimization”. Central European Journal of Operational Research, 21(2), 455-478, 2013.
Badole CM, Jain R, Rathore APS, Nepal B. “Research and opportunities in supply chain modelling: A review”. International Journal of Supply Chain Management, 1(3), 63-86, 2012.
Fahimnia B, Farahani RZ, Marian R, Luong L. “A review and critique on integrated production-distribution planning models and techniques”. Journal of Manufacturing Systems, 32(1), 1-19, 2013.
Lambiase A, Mastrocinque E, Miranda S, Lambiase A. “Strategic planning and design of supply chains: A literature review”. International Journal of Engineering Business Management, 5, 1-11, 2013.
Farias ES, Li JQ, Galvez JP, Borenstein D. “Simple heuristic for the strategic supply chain design of large-scale networks: A Brazilian case study”. Computers & Industrial Engineering, 113, 746-756, 2017.
Reinelt G. “TSPLIB - A traveling salesman problem library”. ORSA Journal on Computing, 3(4), 376-384, 1991.
Solomon MM. “Algorithms for the vehicle routing and scheduling problems with time window constraints”. Operations Research, 35(2), 254-265, 1987.
Talbot FB, Patterson JH, Gehrlein WV. “A comparative evaluation of heuristic line balancing techniques”. Management Science, 32(4), 430-454, 1986.
Qu WW, Bookbinder JH, Iyogun P. “An integrated inventory-transportation system with modified periodic policy for multiple products”. European Journal of Operational Research, 115(2), 254-269, 1999.
Zhou G, Min H, Gen M. “The balanced allocation of customers to multiple distribution centers in the supply chain network: A genetic algorithm approach”. Computers & Industrial Engineering, 43(1-2), 251-261, 2002.
Miranda PA, Garrido RA. “Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand”. Transportation Research Part E, 40(3), 183-207, 2004.
Pishvaee MS, Rabbani M. “A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment”. Advances in Engineering Software, 42(3), 57-63, 2011.
Hamta N, Shirazi MA, Ghomi SMTF, Behdad S. “Supply chain network optimization considering assembly line balancing and demand uncertainty”. International Journal of Production Research, 53(10), 2970-2994, 2015.
Jayaraman V, Pirkul H. “Planning and coordination of production and distribution facilities for multiple commodities”. European Journal of Operational Research, 133(2), 394-408, 2001.
Jang YJ, Jang SY, Chang BM, Park J. “A combined model of network design and production/distribution planning for a supply network”. Computers & Industrial Engineering, 43(1-2), 263-281, 2002.
Sabri EH, Beamon BN. “A multi-objective approach to simultaneous strategic and operational planning in supply chain design”. Omega, 28(5), 581-598, 2000.
Altıparmak F, Gen M, Lin L, Paksoy T. “A genetic algorithm for multi-objective optimization of supply chain networks”. Computers & Industrial Engineering, 51(1), 197-216, 2006.
Benyoucef L, Xie X, Tanonkou GA. “Supply chain network design with unreliable suppliers: a Lagrangian relaxation-based approach”. International Journal of Production Research, 5(21), 6435-6454, 2013.
Hwang HS. “Design of supply-chain logistics system considering service level”. Computers & Industrial Engineering, 43(1-2), 283-297, 2002.
Hasani AA. “Competitive supply chain network design considering marketing strategies: A hybrid meta-heuristic algorithm”. International Journal of Supply and Operations Management, 3(3), 1429-1441, 2016.
Yang G, Liu Y. “Optimizing an equilibrium supply chain network design problem by an improved hybrid biogeography based optimization algorithm”. Applied Soft Computing, 58, 657-668, 2017.
Badri H, Bashiri M, Hejazi TH. “Integrated strategic and tactical planning in a supply chain network design with a heuristic solution method”. Computers & Operations Research, 40(4), 1143-1154, 2013.
Jayaraman V, Ross A. “A simulated annealing methodology to distribution network design and management”. European Journal of Operational Research, 144(3), 629-645, 2003.
Farahani RZ, Elahipanah M. “A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain”. International Journal of Production Economics, 111(2), 229-243, 2008.
Babazadeh R, Razmi J, Ghodsi R. “Supply chain network design problem for a new market opportunity in an agile manufacturing system”. Journal of Industrial Engineering International, 8, 53-62, 2012.
Meixell MJ, Gargeya VB. “Global supply chain design: A literature review and critique”. Transportation Research Part E, 41(6), 531-550, 2005.
Melo MT, Nickel S, Saldanha-da-Gama F. “Facility location and supply chain management: A review”. European Journal of Operational Research, 196(2), 401-412, 2009.
Mula J, Peidro D, Díaz-Madroñero M, Vicens E. “Mathematical programming models for supply chain production and transport planning”. European Journal of Operational Research, 204(3), 377-390, 2009.
Syam SS. “A model and methodologies for the location problem with logistical components”. Computers & Operations Research, 29(9), 1173-1193, 2002.
Wang W, Fung RYK, Chai Y. “Approach of just-in time distribution requirements planning for supply chain management”. International Journal of Production Economics, 91(2), 101-107, 2003.
Melachrinoudis E, Messac A, Min H. “Consolidating a warehouse network: A physical programming approach”. International Journal of Production Economics, 97(1), 1-17, 2005.
Amiri A. “Designing a distribution network in a supply chain system: Formulation and efficient solution procedure”. European Journal of Operational Research, 171(2), 567-576, 2006.
Lee JH, Moon IK, Park JH. “Multi-level supply chain network design with routing”. International Journal of Production Research, 48(13), 3957-3976, 2010.
Cheraghi S, Hosseini-Motlagh SM, Samani MRG. “A robust optimization model for blood supply chain network design”. International Journal of Industrial Engineering & Production Research, 27(4), 425-444, 2016.
Chiadamrong N, Piyathanavong V. “Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach”. Journal of Industrial Engineering International, 13(4), 465-478, 2017.
Gen M, Altıparmak F, Lin L. “A genetic algorithm for two-stage transportation problem using priority-based encoding”. OR Spectrum, 28(3), 337-354, 2006.
Demirel N, Özceylan E, Paksoy T, Gökçen H. “A genetic algorithm approach for optimising a closed-loop supply chain network with crisp and fuzzy objectives”. International Journal of Production Research, 52(12), 3637-3664, 2014.
Koç Ç, Bektaş T, Jabali O, Laporte G. “The fleet size and mix pollution-routing problem”. Transportation Research Part B, 70, 239-254, 2014.
Koç Ç. “An evolutionary algorithm for supply chain network design with assembly line balancing”. Neural Computing and Applications, 28(11), 3183-3195, 2017.