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A Study and Performance Evaluation of Evolutionary Optimization Techniques for Multi-objective Master Production Scheduling Problems

S. Radhika, Ch. Srinivasa Rao, K. Karteeka Pavan

Abstract


Master production schedule (MPS) can effectively and efficiently synchronize the operations in any organization. MPS, which is posed as one of the multi-objective parameter optimization problems, is a plan that determines optimal values of products to be produced. For many engineering optimization problems, more competitive and optimal solutions can be obtained by using Heuristic evolutionary optimization algorithms. Among these, two main algorithms considered here are the differential evolution (DE) whose results are not greatly affected by parameters and teaching-learning-based optimization (TLBO), the recent algorithm which does not require any algorithm-specific parameters. This work presents the development and use of DE and TLBO to MPS problems. The results available for the existing algorithm are compared with those obtained from the proposed evolutionary algorithms. The research demonstrates that use of TLBO yields the most optimal solution for MPS problems with a minimum computational time.

Keywords


master production scheduling, multi-objective optimization, differential evolution, teaching-learning-based optimization

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References


Das S, Abraham A, Konar A. Metaheuristic Clustering. Berlin Heidelberg: Springer-Verlag: 2009; SCI 178:63–110p.

Das Swagatam, Nagaratnam Suganthan P. Differential Evolution: A Survey of the State-of-the-Art. IEEE Transactions on Evolutionary Computation 2011; 15(1):4–32p.

Price KV. Differential evolution vs. the functions of the 2nd ICEO. Proc. IEEE Int. Conf. Evol. Comput. 1997; 153–157p.

Price KV, Storn R. Differential evolution: A simple evolution strategy for fast optimization., Dr. Dobb’s J. 1997; 22(4):18–24p.

Storn R, Price KV. Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces; ICSI, USA, Tech. Rep. TR-95-012. 1995.

Rao RV, Patel V. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations 2012a; 3(4):535–560p.

Rao RV, Patel V. Multi-objective optimization of combined Brayton and inverse Brayton cycle using advanced optimization algorithms. Engineering Optimization 2012b; doi: 10.1080/0305215X.2011.624183.

Rao RV, Kalyankar VD. Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence 2012a; http://dx.doi.org/10.1016/j.engappai.2012.06.007.

Rao RV, Kalyankar VD. Multi-objective multi-parameter optimization of the industrial LBW process using a new

Journal of Production Research and Management

Volume 3, Issue 2, ISSN: 2249 - 4766

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optimization algorithm. Journal of Engineering Manufacture 2012b; doi: 10.1177/0954405411435865.

Rao RV, Kalyankar VD. Parameter optimization of machining processes using a new optimization algorithm. Materials and Manufacturing Processes 2012c; doi:10.1080/10426914.2011.602792.

Vollmann TE, Berry WL, Whybark DC. Manufacturing Planning and Control System, 4th Edn. New York: McGraw-Hill: 1997.

Plossl GW, Lankford RL. The direction of U.S. manufacturing. Production and Inventory Management Review 1984; 4(10):74–88p.

Proud JF. Master Scheduling, 2nd edn. John Wiley & Sons Inc.: 1999.

Slack N, Chambers S, Johnston R. Operations Management, 4th edn. 2004.

Garey M, Johnson D. Computer, Complexity and Intractability. A Guide to Theory of NP-Completeness. San Franscisco, USA, Freeman: 1979.

Higgins P, Browne J. Master production scheduling: a concurrent planning approach. Prod Plan Control 1992; 3(1):2–18p.

Kochhar AK, Ma X, Khan MN. Knowledge-based systems approach to the development of accurate and realistic master production schedules. Journal of Engineering Manufacture 1998; 212:453–460p.

Heizer JH, Render B. Operations Management. Upper Saddle River, New York: Pearson Prentice Hall: 2006.

Vieira GE, Ribas CP. A new multi-objective optimization method for master production scheduling problems using simulated annealing. International Journal of Production Research 2004; 42.

Soares MM, Vieira GE. A New multi-objective optimization method for master production scheduling problems based on genetic algorithm. International Journal of

Advanced Manufacturing Technology 2009; 41:549–567p.

Radhika S, Srinivasa Rao Ch, Karteeka Pavan K. Proceedings of the International Conference on Advanced Engineering Optimization through Intelligent Techniques (AEOTIT), S.V. National Institute of Technology, Surat–395007, Gujarat, India July 01–03, 2013.




DOI: https://doi.org/10.37591/joprm.v3i2.7143

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