National Journal of System and Information Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Industrial Scope of 2D Packing Problems


     

   Subscribe/Renew Journal


Packing problems are optimization problem encountered in many areas of business and industries and have wide applications. These problems look for good arrangement of multiple items in some larger containing regions with an objective to maximize the utilization of resource materials. 2D packing problem has wide industrial applications starting from small scale industries related to leather, furniture, glass, metal, and wood to large scale industries dealing with textile, garments, paper, shipbuilding, automobiles and VLSI design. It has been observed that using automated nesting solutions based on heuristics prove to be better over conventional methods where very few intuitive arrangements were tried by experienced craftsmen and in that case final layouts were dependent on the dexterity of skilled craftsperson. In this paper authors have summarized the different approaches used to solve 2D packing problem along with their industrial applications. Accordingly, this study is an academic review of the industrial applications of 2D packing problem.

Keywords

Packing Problem, Trim Loss Problem, Rectangle Packing, Bin Packing, Cutting And Packing
Subscription Login to verify subscription
User
Notifications
Font Size


  • Aboudi, R. and Barcia, P.(1998) Determining cutting stock patterns when defects are present, Annals of Operations Research 82: 343-354

  • Adamowicz, M. and Albano, A. (1976) Nesting two-dimensional shapes in rectangular modules, Computer Aided Design 8: 27-33.

  • Arbib, C. and Marinelli, F. (2007) An optimization model for trim loss minimization in an automotive glass plant, European Journal of Operational Research 183(3):1421- 1432.

  • Bisotto, S. and Corno, F. (1997) Loss in Flat Glass Cutting, IEE/IEEE International Conference on Genetic Algorithm Engineering Systems: Innovations and Applications (GALESIA97), Glasgow, UK. 450-455.

  • Bounsaythip, C., Maouche, S., Neus, M. (1995) Evolutionary Search Techniques Application to Automated Lay-Planning Optimization Problem (Vancouver, 1995); Canada: Proceedings of IEEE SMC'95, 4497-4503

  • Chambers, M.L. and Dyson, R.G. (1976) The Cutting Stock Problem in the Flat Glass Industry -- Selection of Stock Sizes, Operational Research Quarterly 27: 949-957.

  • Chryssolouris, G., Papakostas, N., Mourtzis, D. (2000) A decision-making approach for nesting scheduling: a textile case, International Journal of Production Research 38: 4555-6427.

  • Crispin, A.J., Clay, P., Taylor, G.E. (2005) Genetic Algorithm Coding Methods For Leather Nesting, Applied Intelligence 23: 9–20.

  • Crispin, A.J., Clay, P., Taylor, G.E., Bayes, T. and Reedman, D. (2003) Genetic algorithms applied to leather lay plan material utilisation, Journal of Engineering Manufacture 217: 1753-1756.

  • Degraeve, Z. and Vandebroek, M. (1998) Mixed integer programming model for solving a layout problem in the fashion industry, Management Science 44: 301-310.

  • Degraeve, Z., Gochet, W., Jans, R. (2002) Alternative formulations for a layout problem in the fashion industry, European Journal of Operational Research 143: 80- 93.

  • Eisemann, K. (1957) The Trim Problem, Management Science 3: 279-284.

  • Francis, J. Vasko, D. Newhart, K. L. Stott, Jr. (1999) A hierarchical approach for onedimensional cutting stock problems in the steel industry that maximizes yield and minimizes overgrading, European Journal of Operational Research 114(1):72-82.

  • Giannelos, N.E. and Georgiadis, M.C. (2001) Scheduling of Cutting-Stock Processes on Multiple Parallel Machines, Chemical Engineering Research and Design79(7): 747-753

  • Gilmore, P.C. and Gomory, R.E. (1963) A linear programming approach to the cutting stock problem: Part II. Operations Research 11: 863±888.

  • Gilmore, P.C. and Gomory, R.E. (1965) Multistage cutting stock problems of two and more dimensions, Operations Research, 12: 94-120.

  • Hahn, S.G. (1968) On the Optimal Cutting of Defective Sheets, Operations Research16: 1100-1114

  • Harjunkoski, I., Westerlund, T., Porn, R. and Skrifvars, H. (1998) Different transformations for solving non-convex trim loss problems by MINLP, European Journal of Operational Research 105: 594-603

  • Hopper, E. and Turton, B. (1999) A Genetic Algorithm for a 2D Industrial Packing Problem, Computers & Industrial Engineering 37: 375-378.

  • Hsu-Hao, Y. and Chien-Li, L. (2009) On genetic algorithms for shoe making nesting – A Taiwan case Expert Systems with Applications, 36(2): 1134-1141.

  • Jain, L. and Singh, K. (2008) Resolving Cutting and Packing Problem of Industry for Sustainable Development. RIMT J. of strategic management and technology, 73-84.

  • Johnson, M.P., Rennick, Z. E. (1997) Skiving addition to the cutting stock problem in the paper industry, SIAM REV 39: 472-483 .

  • Julia, A. Bennell, Kathryn, A. Dowsland, William B. and Dowsland (2001) The irregular cutting-stock problem - a new procedure for deriving the no-fit polygon Computers & Operations Research 28: 271-287.

  • Kos, L. and Duhovnik, J. (2002) Cutting optimization with variable-sized stock and Inventory status data, International Journal of Production Research 40: 2289-2301.

  • Martens (2004) Two genetic algorithms to solve a layout problem in the fashion industry, European Journal of Operational Research 154 : 304–322.

  • Menon, S. and Schrage, L. (2002) Order allocation for stock cutting in the paper industry, Operations Research 50: 324-332.

  • Morabito, R. and Arenales, M. (2000) Optimizing the cutting of stock plates in a furniture company, International Journal of Production Research 38: 2725-2742.

  • Morabito, R. and Garcia, V. (1997) The cutting stock problem in a hardboard industry:a case study, Computers and Operations Research 25(6): 469-485.

  • Murty, K.G. (1968) Solving the fixed charge problem by ranking the extreme points. Operations Research 16, 268-279.

  • Nonas, S.L. and Thorstenson, A. (2000) A combined cutting-stock and lot-sizing problem, European Journal of Operational Research 120: 327-342.

  • Puchinger, J., Raidl, G.R., Koller, G. (2004) Solving a real-world glass cutting problem, In Proceedings of the 4th International Conference on Combinatorial Optimization (EvoCOP 2004); Coimbra, Portugal, Springer-Verlag.162-173.

  • Reinaldo, M. and Luciano B. (2007) Optimising the cutting of wood fibre plates in the hardboard industry , European Journal of Operational Research 183: 1405- 1420.

  • Selow, R., Junior, F.N., Heitor S., Lopes, H.S. (2007) Genetic Algorithms for the Nesting Problem in the Packing Industry. IMECS: 1-6.

  • Singh, K. and Jain, L. (2009a) An empirical study of a modified Cheok-Nee’s heuristic for 2d rectangular packing problem, Appejay. J Management Technology, 53-64.

  • Singh, K. and Jain, L. (2009b) An improved Heuristic for 2D Rectangular packing problem; Patiala: Proceeding of IEEE International Advance Computing Conference, 1185-1190.

  • Singh, K. and Lall, A.K. (2001) Practical consideration and extension to the nesting algorithm of Adamowicz and Albano. J. Institute Engineers. 31-33.

  • Tokuyama, N.U. (1985) The cutting problem for large sections in the iron and steel industry, European Journal of Operational Research 22 (3): 280-292.

  • Vajda, S. (1958) Trim Loss Reduction, Readings in Linear Programming, Wiley, New York

  • Yaodong, C. and Yiping, L. (2009) Heuristic algorithm for a cutting stock problem in the steel bridge construction. Computers & Operations Research 36: 612 – 622.

  • Yaodong, Cui. and Xiaoxia, S. (2008) Applying parallelogrammic strips for cutting circles from stainless steel rolls, Journal of materials processing technology 205: 138- 145.

  • Yeung, L.H.W. and Tang, W.K.S. (2003) A Hybrid Genetic Approach for Garment Cutting in the Clothing Industry, IEEE Transactions on Industrial Electronics 50: 449-455.


Abstract Views: 289

PDF Views: 2




  • Industrial Scope of 2D Packing Problems

Abstract Views: 289  |  PDF Views: 2

Authors

Abstract


Packing problems are optimization problem encountered in many areas of business and industries and have wide applications. These problems look for good arrangement of multiple items in some larger containing regions with an objective to maximize the utilization of resource materials. 2D packing problem has wide industrial applications starting from small scale industries related to leather, furniture, glass, metal, and wood to large scale industries dealing with textile, garments, paper, shipbuilding, automobiles and VLSI design. It has been observed that using automated nesting solutions based on heuristics prove to be better over conventional methods where very few intuitive arrangements were tried by experienced craftsmen and in that case final layouts were dependent on the dexterity of skilled craftsperson. In this paper authors have summarized the different approaches used to solve 2D packing problem along with their industrial applications. Accordingly, this study is an academic review of the industrial applications of 2D packing problem.

Keywords


Packing Problem, Trim Loss Problem, Rectangle Packing, Bin Packing, Cutting And Packing

References