A hybrid approach based on genetic algorithms to solve the problem of cutting structural beams in a metalwork company

This work presents a hybrid approach based on the use of genetic algorithms to solve efficiently the problem of cutting structural beams arising in a local metalwork company. The problem belongs to the class of one-dimensional multiple stock sizes cutting stock problem, namely 1-dimensional multiple stock sizes cutting stock problem. The proposed approach handles overproduction and underproduction of beams and embodies the reusability of remnants in the optimization process. Along with genetic algorithms, the approach incorporates other novel refinement algorithms that are based on different search and clustering strategies.Moreover, a new encoding with a variable number of genes is developed for cutting patterns in order to make possible the application of genetic operators. The approach is experimentally tested on a set of instances similar to those of the local metalwork company. In particular, comparative results show that the proposed approach substantially improves the performance of previous heuristics.Gracia Calandin, CP.; Andrés Romano, C.; Gracia Calandin, LI. (2013). A hybrid approach based on genetic algorithms to solve the problem of cutting structural beams in a metalwork company. Journal of Heuristics. 19(2):253-273. doi:10.1007/s10732-011-9187-xS253273192Aktin, T., Özdemir, R.G.: An integrated approach to the one dimensional cutting stock problem in coronary stent manufacturing. Eur. J. Oper. Res. 196, 737–743 (2009)Alves, C., Valério de Carvalho, J.M.: A stabilized branch-and-price-and-cut algorithm for the multiple length cutting stock problem. Comput. Oper. Res. 35, 1315–1328 (2008)Anand, S., McCord, C., Sharma, R., et al.: An integrated machine vision based system for solving the nonconvex cutting stock problem using genetic algorithms. J. Manuf. Syst. 18, 396–415 (1999)Belov, G., Scheithauer, G.: A cutting plane algorithm for the one-dimensional cutting stock problem with multiple stock lengths. Eur. J. Oper. Res. 141, 274–294 (2002)Christofides, N., Hadjiconstantinou, E.: An exact algorithm for orthogonal 2-D cutting problems using guillotine cuts. Eur. J. Oper. Res. 83, 21–38 (1995)Elizondo, R., Parada, V., Pradenas, L., Artigues, C.: An evolutionary and constructive approach to a crew scheduling problem in underground passenger transport. J. Heuristics 16, 575–591 (2010)Fan, L., Mumford, C.L.: A metaheuristic approach to the urban transit routing problem. J. Heuristics 16, 353–372 (2010)Gau, T., Wäscher, G.: CUTGEN1: a problem generator for the standard one-dimensional cutting stock problem. Eur. J. Oper. Res. 84, 572–579 (1995)Gilmore, P.C., Gomory, R.E.: A linear programming approach to the cutting stock problem. Oper. Res. 9, 849–859 (1961)Gilmore, P.C., Gomory, R.E.: A linear programming approach to the cutting stock problem. Part II. Oper. Res. 11, 863–888 (1963)Ghiani, G., Laganà, G., Laporte, G., Mari, F.: Ant colony optimization for the arc routing problem with intermediate facilities under capacity and length restrictions. J. Heuristics 16, 211–233 (2010)Gonçalves, J.F., Resende, G.C.: Biased random-key genetic algorithms for combinatorial optimization. J. Heuristics (2011). doi: 10.1007/s10732-010-9143-1Gradisar, M., Kljajic, M., Resinovic, G., et al.: A sequential heuristic procedure for one-dimensional cutting. Eur. J. Oper. Res. 114, 557–568 (1999)Haessler, R.W.: One-dimensional cutting stock problems and solution procedures. Math. Comput. Model. 16, 1–8 (1992)Haessler, R.W., Sweeney, P.E.: Cutting stock problems and solution procedures. Eur. J. Oper. Res. 54(2), 141–150 (1991)Haessler, R.W.: Solving the two-stage cutting stock problem. Omega 7, 145–151 (1979)Hinterding, R., Khan, L.: Genetic algorithms for cutting stock problems: with and without contiguity. In: Yao, X. (ed.) Progress in Evolutionary Computation. LNAI, vol. 956, pp. 166–186. Springer, Berlin (1995)Holthaus, O.: Decomposition approaches for solving the integer one-dimensional cutting stock problem with different types of standard lengths. Eur. J. Oper. Res. 141, 295–312 (2002)Kantorovich, L.V.: Mathematical methods of organizing and planning production. Manag. Sci. 6, 366–422 (1939) (Translation to English 1960)Liang, K., Yao, X., Newton, C., et al.: A new evolutionary approach to cutting stock problems with and without contiguity. Comput. Oper. Res. 29, 1641–1659 (2002)Poldi, K., Arenales, M.: Heuristics for the one-dimensional cutting stock problem with limited multiple stock lengths. Comput. Oper. Res. 36, 2074–2081 (2009)Suliman, S.M.A.: Pattern generating procedure for the cutting stock problem. Int. J. Prod. Econ. 74, 293–301 (2001)Talbi, E.-G.: A taxonomy of hybrid metaheuristics. J. Heuristics 8, 541–564 (2002)Vahrenkamp, R.: Random search in the one-dimensional cutting stock problem. Eur. J. Oper. Res. 95, 191–200 (1996)Vanderbeck, F.: Exact algorithm for minimizing the number of set ups in the one dimensional cutting stock problems. Oper. Res. 48, 915–926 (2000)Wagner, B.J.: A genetic algorithm solution for one-dimensional bundled stock cutting. Eur. J. Oper. Res. 117, 368–381 (1999)Wäscher, G., Haußner, H., Schumann, H.: An improved typology of cutting and packing problems. Eur. J. Oper. Res. 183, 1109–1130 (2007

A hybrid genetic algorithm for route optimization in the bale collecting problem

The bale collecting problem (BCP) appears after harvest operations in grain and other crops. Its solution defines the sequence of collecting bales which lie scattered over the field. Current technology on navigation-aid systems or auto-steering for agricultural vehicles and machines, is able to provide accurate data to make a reliable bale collecting planning. This paper presents a hybrid genetic algorithm (HGA) approach to address the BCP pursuing resource optimization such as minimizing non-productive time, fuel consumption, or distance travelled. The algorithmic route generation provides the basis for a navigation tool dedicated to loaders and bale wagons. The approach is experimentally tested on a set of instances similar to those found in real situations. In particular, comparative results show an average improving of a 16% from those obtained by previous heuristics.This work was supported in part by the Spanish Government (research project AGL2010-15334).Gracia Calandin, CP.; Diezma Iglesias, B.; Barreiro Elorza, P. (2013). A hybrid genetic algorithm for route optimization in the bale collecting problem. Spanish Journal of Agricultural Research. 11(3):603-614. https://doi.org/10.5424/sjar/2013113-3635S603614113Amiama, C., Bueno, J., Álvarez, C. J., & Pereira, J. M. (2008). Design and field test of an automatic data acquisition system in a self-propelled forage harvester. Computers and Electronics in Agriculture, 61(2), 192-200. doi:10.1016/j.compag.2007.11.006Baker, B. M., & Ayechew, M. A. (2003). A genetic algorithm for the vehicle routing problem. Computers & Operations Research, 30(5), 787-800. doi:10.1016/s0305-0548(02)00051-5Baykasolu, A., Oumlzbakr, L., & Tapk, P. (2007). Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem. Swarm Intelligence, Focus on Ant and Particle Swarm Optimization. doi:10.5772/5101Bentley, J. J. (1992). Fast Algorithms for Geometric Traveling Salesman Problems. ORSA Journal on Computing, 4(4), 387-411. doi:10.1287/ijoc.4.4.387Bochtis, D. D., & Sørensen, C. G. (2009). The vehicle routing problem in field logistics part I. Biosystems Engineering, 104(4), 447-457. doi:10.1016/j.biosystemseng.2009.09.003Bochtis, D. D., & Sørensen, C. G. (2010). The vehicle routing problem in field logistics: Part II. Biosystems Engineering, 105(2), 180-188. doi:10.1016/j.biosystemseng.2009.10.006Bochtis, D. D., Dogoulis, P., Busato, P., Sørensen, C. G., Berruto, R., & Gemtos, T. (2013). A flow-shop problem formulation of biomass handling operations scheduling. Computers and Electronics in Agriculture, 91, 49-56. doi:10.1016/j.compag.2012.11.015Brady, R. M. (1985). Optimization strategies gleaned from biological evolution. Nature, 317(6040), 804-806. doi:10.1038/317804a0Chen, J.-S., Pan, J. C.-H., & Lin, C.-M. (2008). A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem. Expert Systems with Applications, 34(1), 570-577. doi:10.1016/j.eswa.2006.09.021Cook, S. E., & Bramley, R. G. V. (1998). Precision agriculture — opportunities, benefits and pitfalls of site-specific crop management in Australia. Australian Journal of Experimental Agriculture, 38(7), 753. doi:10.1071/ea97156Cordeau, J.-F., Gendreau, M., Laporte, G., Potvin, J.-Y., & Semet, F. (2002). A guide to vehicle routing heuristics. Journal of the Operational Research Society, 53(5), 512-522. doi:10.1057/palgrave.jors.2601319Dantzig, G., Fulkerson, R., & Johnson, S. (1954). Solution of a Large-Scale Traveling-Salesman Problem. Journal of the Operations Research Society of America, 2(4), 393-410. doi:10.1287/opre.2.4.393Dasgupta, D. (Ed.). (1999). Artificial Immune Systems and Their Applications. doi:10.1007/978-3-642-59901-9Davis L, 1985. Job shop scheduling with genetic algorithms. Proc of the First Int Conf on Genetic Algorithms and their Applications, Pittsburg, PA (USA). July 24-26. pp: 136-140.De Castro LN, Timmis J, 2002. Artificial immune systems: a new computational approach. Springer-Verlag Inc, London, UK.Dorigo, M., Birattari, M., Blum, C., Gambardella, L. M., Mondada, F., & Stützle, T. (Eds.). (2004). Ant Colony Optimization and Swarm Intelligence. Lecture Notes in Computer Science. doi:10.1007/b99492Eksioglu, B., Vural, A. V., & Reisman, A. (2009). The vehicle routing problem: A taxonomic review. Computers & Industrial Engineering, 57(4), 1472-1483. doi:10.1016/j.cie.2009.05.009Garey MR, Johnson DS, 1979. Computers and intractability: a guide to the theory of NP-completeness. WH Freeman & Company, NY.Gillett, B. E., & Miller, L. R. (1974). A Heuristic Algorithm for the Vehicle-Dispatch Problem. 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In: Network models. Handbooks on Operations Research and Management Science 7 (Ball MO, Magnanti TL, Monma CL, Nemhauser GL, eds.). Elsevier, Amsterdam, pp: 225-330.Kennedy JF, Kennedy J, Eberhart R, Shi Y, 2001. Swarm intelligence. Academic Press Inc, London.Laporte, G., Gendreau, M., Potvin, J.-Y., & Semet, F. (2000). Classical and modern heuristics for the vehicle routing problem. International Transactions in Operational Research, 7(4-5), 285-300. doi:10.1111/j.1475-3995.2000.tb00200.xMartin O, Otto SW, Felten EW, 1991. Large-step markov chains for the travelling salesman problem. Complex Syst 5(3): 299-326.Nikkilä, R., Seilonen, I., & Koskinen, K. (2010). Software architecture for farm management information systems in precision agriculture. Computers and Electronics in Agriculture, 70(2), 328-336. doi:10.1016/j.compag.2009.08.013Sørensen, C. G., Pesonen, L., Bochtis, D. D., Vougioukas, S. G., & Suomi, P. (2011). 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NASA Tech Briefs, October 2001

Topics include: special coverage section on composites and plastics, electronic components and systems, software, mechanics, physical sciences, information sciences, book and reports, and a special sections of Photonics Tech Briefs and Motion Control Tech Briefs

Making music out of architecture and from-architecture-music-an oddyssey

These are the documents submitted for the First Review as work-in-progress, the first (longer) and the second (shorter) versions of the PhD research project to date, together with a summary titled The Final Proposal for PhD for First Review September 2019. Please note that the first version is unfinished and needs approximately another 30,000 words, questions answered, some further exploration of points raised in discussion and other relevant points, revision and editing. The second version is on-going. Please Note: The file titled Latest save of Making music out of architecture seems unable to be viewed in Preview perhaps due to its size. It can however be viewed from Download in which case please allow some time for this to occur. The other two documents can be viewed in Previe

NASA Tech Briefs, September 1997

Topics include: Data Acquisition and Analysis; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Software; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences

NASA Tech Briefs, March 1995

This issue contains articles with a special focus on Computer-Aided design and engineering amd a research report on the Ames Research Center. Other subjects in this issue are: Electronic Components and Circuits, Electronic Systems, Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Manufacturing/Fabrication, Mathematics and Information Sciences and Life Science

A History of Materials and Technologies Development

The purpose of the book is to provide the students with the text that presents an introductory knowledge about the development of materials and technologies and includes the most commonly available information on human development. The idea of the publication has been generated referring to the materials taken from the organic and non-organic evolution of nature. The suggested texts might be found a purposeful tool for the University students proceeding with studying engineering due to the fact that all subjects in this particular field more or less have to cover the history and development of the studied object. It is expected that studying different materials and technologies will help the students with a better understanding of driving forces, positive and negative consequences of technological development, etc