Abstract
The tool indexing problem (TIP) is the problem of allocating cutting tools to different slots in a tool magazine of Computer Numerically Controlled machine to reduce the processing time of jobs on the machine. This is one of the mostly encountered optimization problems in manufacturing systems. In TIP, the number of tools used by the machine is at most the number of slots available in the tool magazine. In this article, a customized harmony search (HS) algorithm, which utilizes a harmony refinement strategy for faster convergence, is presented to solve TIP. The harmony refinement method also helps to avoid getting stuck into local optima. The performance of the proposed method is tested on 27 instances taken from the literature and out of these it is found to improve the best known solutions for 16 instances. For the remaining instances, it gives the same results as found in the literature. Moreover, the performance of the proposed algorithm is tested on newly adapted 41 instances and for some of these instances the results obtained using the proposed algorithm are compared with that obtained using CPLEX.
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References
Akturk MS, Ozkan S (2001) Integrated scheduling and tool management in flexible manufacturing systems. Int J Prod Res 39(12):2697–2722
Anjos MF, Yen G (2009) Provably near-optimal solutions for very large single-row facility layout problems. Optim Methods Softw 24(4–5):805–817
Anjos MF, Kennings A, Vannelli A (2005) A semidefinite optimization approach for the single-row layout problem with unequal dimensions. Discrete Optim 2(2):113–122
Askarzadeh A, Rezazadeh A (2012) Parameter identification for solar cell models using harmony search-based algorithms. Sol Energy 86(11):3241–3249
Avci S, Akturk MS (1996) Tool magazine arrangement and operations sequencing on CNC machines. Comput Oper Res 23(11):1069–1081
Baykasoğlu A, Dereli T (2002) Optimization of index positions of cutting tools in chain type of tool changers. In: 2nd International conference on responsive manufacturing. Springer, 26–28 June 2002, pp 550–554
Baykasoğlu A, Dereli T (2003) Optimal allocation of index positions considering tool duplications on chain type of automatic tool changers using simulated annealing. Int J Adv Manuf Syst 6(1):19–28
Baykasoğlu A, Dereli T (2004) Heuristic optimization system for the determination of index positions on CNC magazines with the consideration of cutting tool duplications. Int J Prod Res 42(7):1281–1303
Baykasoğlu A, Ozsoydan FB (2016) An improved approach for determination of index positions on CNC magazines with cutting tool duplications by integrating shortest path algorithm. Int J Prod Res 54(3):742–760
Baykasoğlu A, Ozsoydan FB (2017) Minimizing tool switching and indexing times with tool duplications in automatic machines. Int J Adv Manuf Technol 89(5–8):1775–1789
Dai X, Yuan X, Wu L (2017) A novel harmony search algorithm with gaussian mutation for multi-objective optimization. Soft Comput 21(6):1549–1567
Degertekin S (2008) Optimum design of steel frames using harmony search algorithm. Struct Multidiscip Optim 36(4):393–401
Dereli T, Baykasoğlu A (2005) OPPS-PRI 2.0: an open and optimized process planning system for prismatic parts to improve the performance of SMEs in the machining industry. Int J Prod Res 43(5):1039–1087
Dereli T, Filiz IH (2000) Allocating optimal index positions on tool magazines using genetic algorithms. Robot Auton Syst 33(2):155–167
Dereli T, Baykasoglu A, Gindy N, Filiz I (1998) Determination of optimal turret-index positions of cutting tools by using genetic algorithms. In: Proceedings of the second international symposium on intelligent manufacturing systems, pp 6–7
Fesanghary M, Mahdavi M, Minary-Jolandan M, Alizadeh Y (2008) Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Comput Methods Appl Mech Eng 197(33–40):3080–3091
Geem ZW (2006a) Improved harmony search from ensemble of music players. In: International conference on knowledge-based and intelligent information and engineering systems. Springer, pp 86–93
Geem ZW (2006b) Optimal cost design of water distribution networks using harmony search. Eng Optim 38(03):259–277
Geem ZW (2007) Optimal scheduling of multiple dam system using harmony search algorithm. In: International work-conference on artificial neural networks. Springer, pp 316–323
Geem ZW (2010) State-of-the-art in the structure of harmony search algorithm. In: Recent advances in harmony search algorithm. Springer, pp 1–10
Geem ZW (2012) Effects of initial memory and identical harmony in global optimization using harmony search algorithm. Appl Math Comput 218(22):11,337–11,343
Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68
Geem ZW, Tseng CL, Park Y (2005) Harmony search for generalized orienteering problem: best touring in China. In: International conference on natural computation. Springer, pp 741–750
Ghosh D (2016a) A new genetic algorithm for the tool indexing problem. Tech. rep., Indian Institute of Management Ahmedabad, Research and Publication Department
Ghosh D (2016b) Allocating tools to index positions in tool magazines using tabu search. Tech. rep., Indian Institute of Management Ahmedabad, Research and Publication Department
Ghosh D (2016c) Comparing genetic algorithm crossover and mutation operators for the indexing problem. Tech. rep., Indian Institute of Management Ahmedabad, Research and Publication Department
Ghosh D (2016d) Exploring Lin Kernighan neighborhoods for the indexing problem. Tech. rep., Indian Institute of Management Ahmedabad, Research and Publication Department
Ghosh D (2016e) Incorporating gender and age in genetic algorithms to solve the indexing problem. Tech. rep., Indian Institute of Management Ahmedabad, Research and Publication Department
Ghosh D (2016f) Speeding up neighborhood search for the tool indexing problem. Tech. rep., Indian Institute of Management Ahmedabad, Research and Publication Department
Gibbons JD, Chakraborti S (2011) Nonparametric statistical inference. In: International encyclopedia of statistical science. Springer, pp 977–979
Hertz A, Laporte G, Mittaz M, Stecke KE (1998) Heuristics for minimizing tool switches when scheduling part types on a flexible machine. IIE Trans 30(8):689–694
Hollander M, Wolfe DA (1999) Nonparametric statistical methods. Wiley-Interscience
Kaveh A, Talatahari S (2009) Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput Struct 87(5–6):267–283
Kim JH, Geem ZW, Kim ES (2001) Parameter estimation of the nonlinear Muskingum model using harmony search. JAWRA J Am Water Resour Assoc 37(5):1131–1138
Kulluk S, Ozbakir L, Baykasoglu A (2011) Self-adaptive global best harmony search algorithm for training neural networks. Procedia Comput Sci 3:282–286
Kulluk S, Ozbakir L, Baykasoglu A (2012) Training neural networks with harmony search algorithms for classification problems. Eng Appl Artif Intell 25(1):11–19
Larranaga P, Kuijpers CMH, Murga RH, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif Intell Rev 13(2):129–170
Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36):3902–3933
Li Hq, Li L (2007) A novel hybrid particle swarm optimization algorithm combined with harmony search for high dimensional optimization problems. In: The 2007 international conference on intelligent pervasive computing, 2007. IPC. IEEE, pp 94–97
Loiola EM, de Abreu NMM, Boaventura-Netto PO, Hahn P, Querido T (2007) A survey for the quadratic assignment problem. Eur J Oper Res 176(2):657–690
Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579
Omran MG, Mahdavi M (2008) Global-best harmony search. Appl Math Comput 198(2):643–656
Padberg M (2012) Harmony search algorithms for binary optimization problems. In: Operations Research Proceedings 2011. Springer, pp 343–348
Rechenberg I (1973) Evolutionsstrategie–Optimierung technisher Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog
Shivaie M, Ameli MT (2014) An implementation of improved harmony search algorithm for scenario-based transmission expansion planning. Soft Comput 18(8):1615–1630
Sinriech D, Rubinovitz J, Milo D, Nakbily G (2001) Sequencing, scheduling and tooling single-stage multifunctional machines in a small batch environment. IIE Trans 33(10):897–911
Sivasubramani S, Swarup K (2011) Multi-objective harmony search algorithm for optimal power flow problem. Int J Electr Power Energy Syst 33(3):745–752
Sörensen K (2015) Metaheuristics—the metaphor exposed. Int Trans Oper Res 22(1):3–18
Wang CM, Huang YF (2010) Self-adaptive harmony search algorithm for optimization. Expert Syst Appl 37(4):2826–2837
Wang L, Pan QK, Tasgetiren MF (2010) Minimizing the total flow time in a flow shop with blocking by using hybrid harmony search algorithms. Expert Syst Appl 37(12):7929–7936
Weyland D (2012) A rigorous analysis of the harmony search algorithm: how the research community can be. In: Modeling, analysis, and applications in metaheuristic computing: advancements and trends: advancements and trends 72
Weyland D (2015) A critical analysis of the harmony search algorithm—how not to solve sudoku. Oper Res Perspect 2:97–105
Yang XS (2009) Harmony search as a metaheuristic algorithm. In: Music-inspired harmony search algorithm. Springer, pp 1–14
Yildiz AR (2008) Hybrid Taguchi-harmony search algorithm for solving engineering optimization problems. Int J Ind Eng Theory Appl Pract 15(3):286–293
Zheng L, Diao R, Shen Q (2015) Self-adjusting harmony search-based feature selection. Soft Comput 19(6):1567–1579
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This work has been partially supported by DST-PURSE scheme, Government of India at University of Kalyani.
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Appendices
Appendix A: Illustration of the mathematical formulation
Here, we illustrate the mathematical formulation mentioned in Sect. 2.2 using an example. Let us consider the values of the parameters first. Let \(m = 5\), \(n = 5\) and the frequency matrix as given below:
The optimal assignment matrix Y can be obtained by solving the above problem and the solution to this problem is given below:
The optimal value (cost) corresponding to this solution is 150, computed using (2). This solution Y represents the following assignments of tools to the slots: \(t_1 \rightarrow s_4\), \(t_2 \rightarrow s_1\), \(t_3 \rightarrow s_5\), \(t_4 \rightarrow s_2\) and \(t_5 \rightarrow s_3\), where \(s_i\) represents the ith slot and \(t_j\) denotes the jth tool. Note that the solution Y satisfies the constraints 3, 4 and 5.
Appendix B: CPLEX OPL Model
The CPLEX codes corresponding to the OPL model and data file are mentioned here.
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Atta, S., Sinha Mahapatra, P.R. & Mukhopadhyay, A. Solving tool indexing problem using harmony search algorithm with harmony refinement. Soft Comput 23, 7407–7423 (2019). https://doi.org/10.1007/s00500-018-3385-5
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DOI: https://doi.org/10.1007/s00500-018-3385-5