Designing machine operating strategy with simulated annealing and Monte Carlo simulation

Journal of the Franklin Institute

Volumn 343, Issue 4-5, 2006, Pages 372-388

  Al Aomar, Raid ^a  

^a JORDAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Jordan)

Author keywords

Monte Carlo simulation; Parameter design; Simulated annealing; Stochastic modeling

Indexed keywords

CONTROL SYSTEMS; FAILURE ANALYSIS; HEURISTIC METHODS; LOGIC DESIGN; MATHEMATICAL MODELS; MONTE CARLO METHODS; QUALITY ASSURANCE; RANDOM PROCESSES; RELIABILITY THEORY; REPAIR; SIMULATED ANNEALING; THROUGHPUT;

CYCLE TIME (CT); DEFECTS RATE (DR); MACHINE OPERATING STRATEGY; MAINTENANCE LEVEL; OPERATING PATTERN; PARAMETER DESIGN; SCHEDULED SHUTDOWNS; STOCHASTIC MODELING; TIME-BETWEEN-FAILURE (TBF); TIME-TO-REPAIR (TTR);

COMPUTER SYSTEMS;

EID: 33747750525     PISSN: 00160032     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfranklin.2006.02.019     Document Type: Article

References (43)

1. Krajewski L.J., and Ritzman L.R. Operations Management: Processes and Value Chains (2005), Prentice-Hall, Upper Saddle River, NJ
2. Armarego E.J.A., and Brown R.H. The Machining of Metal (1969), Prentice-Hall, Englewood Cliffs, NJ
3. Hati S.K., and Rao S.S. Determination of optimum machining conditions: deterministic and probabilistic approaches. Trans. ASME, J. Eng. Ind. 98 (1976) 354-359
4. Freiheit T., and Hu S.J. Impact of machining parameters on machine reliability and system productivity. ASME J. Manuf. Sci. Eng. 124 2 (2002) 296-304
5. Arezzo B., Ridgway K., and Al-Ahmari A.M.A. Selection of cutting tools and conditions of machining operations using an expert system. Comput. Ind. 42 (2000) 43-58
6. Yeo S.H. A multipass optimization strategy for CNC lathe operations. Int. J. Prod. Econ. 40 (1995) 209-218
7. Kimbler D.L., Wysk R.A., and Davis R.P. Alternative approaches to the machining parameter optimization problem. Comput. Ind. Eng. 2 4 (1978) 195-202
8. Chen M.C., and Tsai D.M. A simulated annealing approach for optimization of multi-pass turning operations. Int. J. Prod. Res. 34 10 (1996) 2803-2825
9. Al-Ahmari A.M.A. Mathematical model for determining machining parameters in multi-pass turning operations with constraints. Int. J. Prod. Res. 39 15 (2001) 3367-3376
10. Hashmi K., El Baradie M.A., and Ryan M. Fuzzy logic based intelligent selection of machining parameters. Comput. Ind. Eng. 35 3 (1998) 571-574
11. Koren Y., Hu. S.J., and Weber T.W. Impact of manufacturing system configuration on performance. Ann. CIRP 47 1 (1998) 369-372
12. Schoch D. Critical factors for achievement of successful high speed metal forming production. J. Mater. Process. Technol. 46 3-4 (1994) 409-414
13. Seo D.K., Klein C.M., and Jang W. Single machine stochastic scheduling to minimize the expected number of tardy jobs using mathematical programming models. Comput. Ind. Eng. 48 2 (2005) 153-161
14. Liu J., and Tang L. A modified genetic algorithm for single machine scheduling. Comput. Ind. Eng. 37 1 (1999) 43-46
15. Guo Y., Lim A., Rodrigues B., and Yu S. Minimizing total flow time in single machine environment with release time: an experimental analysis. Comput. Ind. Eng. 47 2 (2004) 123-140
16. Agnetis A., Alfieri A., and Nicosia G. A heuristic approach to batching and scheduling a single machine to minimize setup costs. Comput. Ind. Eng. 46 4 (2004) 793-802
17. Abdou G., and Tereshkovich W. Optimal operating parameters in high speed milling operations for aluminum. Int. J. Prod. Res. 39 10 (2001) 2197-2214
18. Toth T., Erdelyi F., and Rayegani F. Intensity type state variables in the integration of planning and controlling manufacturing processes. Comput. Ind. Eng. 37 1 (1999) 89-92
19. Challap K., and Berra B. Automated planning and optimization of machining processes: a systems approach. Comput. Ind. Eng. 1 1 (1976) 35-46
20. Metwalli S.M., Salama M.S., and Taher R.A. Computer aided reliability for optimum maintenance planning. Comput. Ind. Eng. 35 4 (1998) 603-606
21. Sundararajan S., Srinivasan G., Staehle W.O., and Zimmers Jr. E.W. Application of a decision support system for operational decisions. Comput. Ind. Eng. 35 2 (1998) 141-144
22. Taguchi G. Introduction to Quality Engineering: Designing Quality into Products and Processes (1986), Quality Resources, White Plains, NY
23. Chang T.C., Wysk R.A., and Wang H.P. Computer-Aided Manufacturing. second ed. (1998), Prentice-Hall, Upper Saddle River, NJ
24. Feldman R.M., and Valdez-Flores C. Applied Probability and Stochastic Processes (1996), PWS Publishing Co., Boston, MA
25. Montgomery D.C., and Runger G.C. Applied Statistics and Probability for Engineers. third ed. (2003), Wiley, New York
26. Al-Aomar R. A methodology for determining system and process-level manufacturing performance metrics. SAE Trans. J. Mater. Manuf. 111 5 (2003) 1043-1050
27. Montgomery D.C. Design and Analysis of Experiments. fifth ed. (2003), Wiley, New York
28. Metropolis N., and Ulam S. The Monte Carlo method. J. Am. Stat. Assoc. 44 (1949) 335-341
29. Rubinstein R.Y. Simulation and the Monte Carlo Method (1981), Wiley, New York
30. Pincus M. A Monte Carlo method for the approximate solution of certain types of constrained optimization problems. Oper. Res. 18 (1970) 1225-1228
31. Taha H.A. Operations Research. seventh ed. (2003), Prentice-Hall, Saddle River, NJ
32. Kirkpatrick S.C. Optimization by simulated annealing-quantitative studies. J. Stat. Phys. 34 (1984) 975-986
33. Gelfand S.B., and Mitter S.K. Simulated annealing with noisy or imprecise energy measurements. J. Optim. Theory Appl. 62 (1989) 49-62
34. Gutjahr W.J., and Pflug G.Ch. Simulated annealing for noisy cost functions. J. Global Optim. 8 (1996) 1-13
35. Alrefaei M.H., and Andradóttir S. A simulated annealing algorithm with constant temperature for discrete stochastic optimization. Manag. Sci. 45 (1999) 748-764
36. Laarhoven P.J.M., and Aarts E. Simulated Annealing: Theory and Applications (1987), D. Reidel Publishing Company, Holland
37. Eglese R.W. Simulated annealing: a tool for operational research. Eur. J. Oper. Res. 46 3 (1990) 271-279
38. Metropolis N., Rosenbluth A., Teller A., and Teller E. Equation of state calculations by fast computing machines. J. Chem. Phys. 21 6 (1953) 1087-1092
39. Vanderbilt D., and Louie S.G. A Monte Carlo simulated annealing approach to optimization over continuous variables. J. Comput. Phys. 56 2 (1984) 259-271
40. Binder K., and Stauffer D. A simple introduction to Monte Carlo simulations and some specialized topics. In: Binder K. (Ed). Applications of the Monte Carlo Method in Statistical Physics (1985), Springer, Berlin
41. Aarts E., and Korst J. Simulated Annealing and Boltzmann Machines (1990), Wiley, New York
42. Youssef A.Y., Beauchamp Y., and Thomas M. Comparison of a full factorial experiment to fractional and Taguchi designs in a lathe dry turning operation. Comput. Ind. Eng. 27 1 (1994) 59-62
43. Chen J., Sun D.X., and Wu C.F.J. A catalogue of two-level and three-level fractional factorial designs with small runs. Int. Stat. Rev. 61 (1993) 131-145