A multi-objective production smoothing model with compressible operating times

Applied Mathematical Modelling

Volumn 35, Issue 7, 2011, Pages 3596-3610

  Karimi Nasab, M ^a   Aryanezhad, M B ^a  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

Author keywords

Available time; Compressible operating time; Multi objective; Multi period; Multi product; Production smoothing

Indexed keywords

AVAILABLE TIME; MULTI OBJECTIVE; MULTI-PERIOD; MULTI-PRODUCTS; OPERATING TIME; PRODUCTION SMOOTHING;

GENETIC ALGORITHMS; PLANNING; PRODUCTION CONTROL; PROJECT MANAGEMENT;

MULTIOBJECTIVE OPTIMIZATION;

EID: 79953222174     PISSN: 0307904X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apm.2011.01.038     Document Type: Article

References (54)

1. Aryanezhad M.B., Karimi-Nasab M., Bakhshi S. Miltiple batch sizing through batch size smoothing. Qazvin J. Indust. Eng. 2008, 3:37-46.
2. Ehrhardt R. Finished goods management for JIT production: new models for analysis. Int. J. Comput. Integr. Manuf. 1998, 11:217-225.
3. M.B. Aryanezhad, M. Karimi-Nasab, A multi-objective approach for production smoothing problem, in: Proceedings of the Second International Conference of Iranian Operations Research Society, Iran, 2009, pp. 315-319.
4. Azoza M.A., Bonney M.C. Disaggregating aggregate production plans to smooth workload variation. Eng. Costs Prod. Econ. 1990, 19:125-131.
5. Yavuz M., Tufekci S. Analysis and solution to the single-level batch production smoothing problem. Int. J. Prod. Res. 2007, 45:3893-3916.
6. Yavuz M., Akcali E. Production smoothing in just-in-time manufacturing systems: a review of the models and solution approaches. Int. J. Prod. Res. 2007, 45:3579-3597.
7. Yavuz M., Akcali E., Tufekci S. Optimizing production smoothing decisions via batch selection for mixed-model just-in-time manufacturing systems with arbitrary setup and processing times. Int. J. Prod. Res. 2006, 44:3061-3081.
8. Yavuz M., Tufekci S. A bounded dynamic programming solution to the batching problem in mixed-model just-in-time manufacturing systems. Int. J. Prod. Econ. 2006, 103:841-862.
9. Sengupta S., Davis R.P., Ferrell W.G. Production planning and control in a JIT environment. Appl. Math. Model. 1993, 17:41-46.
10. Matsui Y. An empirical analysis of just-in-time production in Japanese manufacturing companies. Int. J. Prod. Econ. 2007, 108:153-164.
11. Mollick A.V. Production smoothing in the Japanese vehicle industry. Int. J. Prod. Econ. 2004, 91:63-74.
12. Bertrand J.W.M., Van-Oijen H.P.G. Integrating material coordination and capacity load smoothing in multi-product multi-phase production systems. Int. J. Prod. Econ. 1996, 46-47:1-12.
13. Pochet Y., Wolsey L.A. Production Planning by Mixed Integer Programming 2006, Springer Science+Business Media, Inc., New York.
14. Chinneck J.W. Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods 2008, Springer Science+Business Media, New York.
15. McMullen P.R. A Kohonen self-organizing map approach to addressing a multiple objective, mixed-model JIT sequencing problem. Int. J. Prod. Econ. 2001, 72:59-71.
16. Haral U., Chen R.W., Ferrell W.G., Kurz M.B. Multi-objective single machine scheduling with nontraditional requirements. Int. J. Prod. Econ. 2007, 106:574-584.
17. Azzaro-Pantel C., Zarate P. Mutual benefits of two multicriteria analysis methodologies: a case study for batch plant design. Eng. Appl. Artif. Intell. 2009, 22:546-556.
18. Berrichi A., Amodeo L., Yalaoui F., Châtelet E., Mezghiche M. Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem. J. Intell. Manuf. 2009, 20:389-400.
19. Soares M.M., Vieira G.E. A new multi-objective optimization method for master production scheduling problems based on genetic algorithm. Int. J. Adv. Manuf. Technol. 2009, 41:549-567.
20. Sahoo N.P., Biswal M.P. Computation of a multi-objective production planning model with probabilistic constraints. Int. J. Comput. Math. 2009, 86:185-198.
21. Yulan J., Zuhua J., Wenrui H. Multi-objective integrated optimization research on preventive maintenance planning and production scheduling for a single machine. Int. J. Adv. Manuf. Technol. 2008, 39:954-964.
22. Minner S. A comparison of simple heuristics for multi-product dynamic demand lot-sizing with limited warehouse capacity. Int. J. Prod. Econ. 2009, 118:305-310.
23. Jaber M.Y., Bonney M., Moualek I. Lot sizing with learning, forgetting and entropy cost. Int. J. Prod. Econ. 2009, 118:19-25.
24. San-Jose L.A., Sicilia J., Garcia-Laguna J. An economic lot-size model with partial backlogging hinging on waiting time and shortage period. Appl. Math. Model. 2007, 31:2149-2159.
25. Schaller J. Scheduling on a single machine with family setups to minimize total tardiness. Int. J. Prod. Econ. 2007, 105:329-344.
26. Tsou C.S. Multi-objective inventory planning using MOPSO and TOPSIS. Expert Syst. Appl. 2008, 35:136-142.
27. Wu C.C., Lee W.C. Single machine scheduling problems with a learning effect. Appl. Math. Model. 2008, 32:1191-1197.
28. Kovacs A., Brown K.N., Tarim S.A. An efficient MIP model for the capacitated lot-sizing and scheduling problem with sequence-dependent setups. Int. J. Prod. Econ. 2009, 118:282-291.
29. Liang T.F. Fuzzy multi-objective production/distribution planning decisions with multi-product and multi-time period in a supply chain. Comput. Indust. Eng. 2008, 55:676-694.
30. Zhou Y.W., Lau H.S., Yang S.L. A finite horizon lot-sizing problem with time-varying deterministic demand and waiting-time-dependent partial backlogging. Int. J. Prod. Econ. 2004, 91:109-119.
31. Chen K.K., Chang C.T. A seasonal demand inventory model with variable lead time and resource constraints. Appl. Math. Model. 2007, 31:2433-2445.
32. Teng J.T., Ouyang L.Y., Chang C.T. Deterministic economic production quantity models with time-varying demand and cost. Appl. Math. Model. 2005, 29:987-1003.
33. Choi S.K., Lim K.E., Lee E.Y. A partial replenishment model for an inventory with constant demand. Appl. Math. Model. 2008, 32:1790-1796.
34. Bylka S., Rempala R. Multiproduct lot sizing for finite production rate. Int. J. Prod. Econ. 2001, 71:305-315.
35. Merce C., Fontan G. MIP-based heuristics for capacitated lot sizing problems. Int. J. Prod. Econ. 2003, 85:97-111.
36. Biskup D., Jahnke H. Common due date assignment for scheduling on a single machine with jointly reducible processing times. Int. J. Prod. Econ. 2001, 69:317-322.
37. Ganesh K., Punniyamoorthy M. Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing. Int. J. Adv. Manuf. Technol. 2005, 26:148-154.
38. Hua Z., Zhang B., Xu X. A new variable reduction technique for convex integer quadratic programs. Appl. Math. Model. 2008, 32:224-231.
39. Tang O. Simulated annealing in lot sizing problems. Int. J. Prod. Econ. 2004, 88:173-181.
40. Hwang C.L., Masud A.S.Md. Multiple Objective Decision Making Methods and Applications: A State of the Art Survey 1979, Springer-Verlag, Berlin.
41. Ehrgott M., Gandibleux X. Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys 2003, Kluwer Academic Publishers, New York.
42. Abo-Sinna M.A., Amer A.H., Ibrahim A.S. Extensions of TOPSIS for large scale multi-objective non-linear programming problems with block angular structure. Appl. Math. Model. 2008, 32:292-302.
43. Chakraborti N., Kumar B.S., Babu V.S., Moitra S., Mukhopadhyay A. A new multi-objective genetic algorithm applied to hot-rolling process. Appl. Math. Model. 2008, 32:1781-1789.
44. Xu X., Dai X., Hao P., Wang W. Multi-objective model of mixed continuous/batch plants planning problem and its particle swarm optimization algorithm. Control Decis. Conf. 2009, 17:2521-2526.
45. T.W. Yung, S.G. Ponnambalam, M. Yogeswaran, Multi-objective ACO for integrated scheduling of machines and material handling equipment in flexible manufacturing systems, in: Fifth Annual IEEE Conference on Automation Science and Engineering, Bangalore, India, August 2009, pp. 22-25.
46. F. Zhao, A. Zhu, Z. Ren, Y. Yang, Integration of process planning and production scheduling based on a hybrid PSO and SA algorithm, in: Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation, Luoyang, China, June 2006, pp. 25-28.
47. Glover F., Kochenberger G. Handbook of metaheuristics 2005, Kluwer Academic Publishers, Norwell.
48. Foroughi A.A., Jafari Y. A modified method for constructing efficient solutions structure of MOLP. Appl. Math. Model. 2009, 33:2403-2410.
49. Hua Z., Huang F. An effective genetic algorithm approach to large scale mixed integer programming problems. Appl. Math. Comput. 2006, 174:897-909.
50. Deb K. Multi-objective Optimization using Evolutionary Algorithms 2001, John Wiley & Sons, Chichester, UK.
51. Jaszkiewicz A. Genetic local search for multi-objective combinatorial optimization. Eur. J. Oper. Res. 2002, 137:50-71.
52. Kassem M.A.E. Stability achievement scalarization function for multi-objective nonlinear programming problems. Appl. Math. Model. 2008, 32:1044-1055.
53. Kao G.K., Jacobson S.H. Finding preferred subsets of Pareto optimal solutions. Comput. Optim. Appl. 2008, 40:73-95.
54. Banos R., Gil C., Paechter B., Ortega J. Parallelization of population-based multi-objective metaheuristics: an empirical study. Appl. Math. Model. 2006, 30:578-592.