ÇOK SEVİYELİ DEPO YERLEŞİM DÜZENLEMESİ İÇİN PARÇACIK SÜRÜ OPTİMİZASYON ALGORİTMASI TABANLI TASARIM METODOLOJİSİ

Year 2014, Volume: 25 Issue: 77, 13 - 38, 18.02.2015

Alp Baray Emre Çakmak

Abstract

Farklı z-eksenli depolama alanlarına sahip çok seviyeli depo yerleşim düzenlemesi problemi araştırılmıştır. Bu
çalışmada, fiziksel kısıtlar altında farklı depolama alanlarına z-ekseni boyunca farklı grupların yerleştirildiği
çok seviyeli depo yerleşim düzenlemesi tasarım metodolojisi geliştirilmiştir. Önerilen matematiksel model NP-zordur.
Parçacık Sürü Optimizasyon (PSO)’da çoğunlukla kullanılan sınırlandırma koşulları, parçacıkların
olabildiğince kabul edilebilir çözüm uzayı içerisinde tutmaktadır. Buna ek olarak parçacıkları kabul edilebilir
çözüm uzayında kalmasını için iki yeni sınırlandırma koşulu önerilmiştir. Ayrıca, parçacıkların kabul edilebilir
çözüm uzayında uygun olmayan çözümleri araştırmasıyla ortaya çıkan zaman kaybı probleminin üstesinden
gelebilmek için parçacıkların başlangıç değerleri için önerilen atama algoritması kullanılmıştır.

Keywords

Depo Yerleşim Düzenlemesi, Sınırlandırma Koşulları, Sınıf-bazlı Depolama, Parçacık Sürü Optimizasyon

DESIGN METHODOLOGY FOR A MULTIPLE-LEVEL WAREHOUSE LAYOUT BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM

Abstract

The multi-level warehouse layout problem with different z-axis storage spaces is investigated. In this study, a design methodology for a multiple-level warehouse layout (MLWL) is developed in order to minimize the total material handling costs by considering different number of the storage areas allocated to different groups along the z-axis under physical constraints. The proposed mathematical model is NP-hard. Boundary conditions are often used in particle swarm optimization (PSO) in order to keep the particles as much as possible in the allowable solution spaces. Moreover, two new boundary conditions are proposed for keeping the particles in the allowable solution spaces. Besides, a proposed assignment algorithm for particles’ initial values is used to overcome the problem of the time lost while particles are searching inappropriate solutions in allowable solution spaces

Keywords

Warehouse layout, Boundary Conditions, Class-based storage, Particle swarm optimization

References

• Ashayeri, J. & Gelders, L.F., (1985), “Warehouse design optimization”, European Journal of Operational Research, 21, pp.285–294.
• Baker, P. & Canessa, M., (2009), “Warehouse design: A structured approach”, European
• Journal of Operational Research, 193(2), pp.425–436. Cakmak, E., Gunay, S.N., Aybakan G., Tanyas M., (2012), “Determining the Size and Design of Flow Type and U-Type Warehouses”, Procedia - Social and Behavioral Sciences, 58, pp.1425–1433.
• Caron F., Marchet G. & Perego A. (2000), “Layout Design in Manual Picking Systems: A
• Simulation Approach”, Integrated Manufacturing System, 94-104. Eberhart, R. C., Kennedy, J. (1995), “A new optimizer using particle swarm theory”, In
• Proceedings of 6th Symposium Micro Machine and Human Science, Nagoya, pp. 39–43. Gu, J., Goetschalckx, M. & Mcginnis, L., (2007), “Research on warehouse operation: A comprehensive review”, European Journal of Operational Research, 177(1), pp.1–21.
• Gu, J., Goetschalckx, M. & McGinnis, L.F., (2010), “Research on warehouse design and performance evaluation: A comprehensive review”, European Journal of Operational Research, 203(3), pp.539–549.
• Heragu, S.S., Du, L., Mantel, R.J., Schuur, P.C., (2005), “Mathematical model for warehouse design and product allocation”, International Journal of Production Research, 43(2), pp.327–338.
• Huertas, J.I., Ramírez, J.D. & Salazar, F.T., (2007), “Layout evaluation of large capacity warehouses”, Facilities, 25(7/8), pp.259–270.
• Lai, K.., Xue, J. & Zhang, G., (2002), “Layout design for a paper reel warehouse: A two- stage heuristic approach”, International Journal of Production Economics, 75(3), pp.231–243.
• Lambert, D.M., Stock, J.R., Ellram, L.M. (Eds.), (1998), Fundamentals of Logistics
• Management, McGraw-Hill, Singapore. pp.268. Larson, T.N., March, H. & Kusiak, A., (1997), “A heuristic approach to warehouse layout with class-based storage”, IIE Transactions, 29(4), pp.337–348.
• Le-Duc, T. & De Koster, R. (2005), “Travel distance estimation and storage zone optimization in a 2-block class-based storage strategy warehouse”, International Journal of Production Research, 43, 3561-3581.
• Li, H., Xe, H., Xie, X., Li, L., Zhou, J., Li X., (2010), “A New Boundary Condition for
• Particle Swarm Optimization”, Journal of Converge Information Technology, 5(9), pp.215–221. Macro, J.G. & Salmi, R.E., (2002), “A Simulation Tool to Determine Warehouse
• Efficiencies and Storage Allocations”, In Proceedings of the 2002 Winter Simulation Conference. pp. 1274–1281.
• Onut, S., Tuzkaya, U., Dogac, B., (2008), “A particle swarm optimization algorithm for the multiple-level warehouse layout design problem”, Computers & Industrial Engineering, 54(4), pp.783–799.
• Park, Y.H. & Webster, D.B., (1989), “Modelling of three-dimensional warehouse systems”
• International Journal of Production Research, 27(6), pp.985–1003.
• Poli, R., Kennedy, J., Blackwell,T., (2007), “Particle Swarm Optimization An Overview”,
• Swarm Intelligence, 1, pp. 33-57. Queirolo, F., Tonelli, F., Schenone, M., Nan, P., Zunino, I., (2002), “Warehouse Layout
• Design: Minimizing Travel Time with A Genetic And Simulative Approach - Methodology And Case Study”, In Proceedings 14th European Simulation Symposium. Roodbergen, K.J., (2001), “Layout and routing methods for warehouses”, Ph.D. thesis,
• RSM Erasmus University, the Netherlands. Roodbergen K.J. & Vis F.A., (2006), “A model for warehouse layout”, IIE Transactions, 38, 799-811.
• Rosenblatt M.J., Roll Y., (1984), “Warehouse design with storage policy considerations”,
• International Journal of Production Research, 22(5), pp.809-821. Rouwenhorst B., Reuter B., Stockrahm V., Van Houtum G.J., Mantel R.J. & Zijm W.H.M., (2000), “Warehouse design and control: Framework and literature review”, European
• Journal of Operational Research, 122(3), pp.515–533. Sun, C., Zeng J., Chu, S., Roddick, F.R., (2011), ”Solving Constrained Optimization
• Problems by an Improved Particle Swarm Optimization”, In Second International Conference on Innovations in Bio-inspired Computing and Applications, 35, pp.124- Ting C., Wu K., Chou H., (2014), “Particle Swarm Optimization Algorithm for The Berth
• Allocation Problem”, Expert Systems with Applications, 41(4), pp.1543–1550.
• Van Den Berg, J.P. & Zijm, W.H.M., (1999), “Models for warehouse management :
• Classification and examples”, International Journal of Production Economics, 59, pp.519–528. Xu, S. & Rahmat-Samii, Y., (2007), “Boundary Conditions in Particle Swarm Optimization
• Revisited”, IEEE Transactıons on Antennas and Propagatıon, 55(3), pp.760–765. Yang, L., Sun Y., (2004), “Expected Value Model for A Fuzzy Random Warehouse Layout
• Problem”, In IEEE International Conference Proceedings on Fuzzy Systems. pp. 751– 7 Zhang, G.Q., Xue, J. & Lai, K.K., (2002), “A class of genetic algorithms for multiple-level warehouse layout problems”, International Journal of Production Research, 40(3), pp.731–744.
• Zhang, G.Q. & Lai, K.K., (2006), “Combining path relinking and genetic algorithms for the multiple-level warehouse layout problem” European Journal of Operational Research, 169(2), pp.413–425.
• Zhang, G.Q. & Lai, K.K., (2010), “Tabu search approaches for the multi-level warehouse layout problem with adjacency constraints” Engineering Optimization, 42(8), pp.775–790.
• Zhu, J., (2009), “A Modified Particle Swarm Optimization Algorithm” Journal of Computers, 4(12), pp.1231–1236.