Solving multi-objective master production schedule problem using memetic algorithm

Shireen S. Sadiq, Adnan Mohsin Abdulazeez, Habibollah Haron

Abstract


A master production schedule (MPS) need find a good, perhaps optimal, plan for maximize service levels while minimizing inventory and resource usage. However, these are conflicting objectives and a tradeoff to reach acceptable values must be made. Therefore, several techniques have been proposed to perform optimization on production planning problems based on, for instance, linear and non-linear programming, dynamic-lot sizing and meta-heuristics. In particular, several meta- heuristics have been successfully used to solve MPS problems such as genetic algorithms (GA) and simulated annealing (SA). This paper proposes a memetic algorithm to solve multi-objective master production schedule (MOMPS). The proposed memetic algorithm combines the evolutionary operations of MA (such as mutation and Crossover) with local search operators (swap operator and inverse movement operator) to improve the solutions of MA and increase the diversity of the population). This algorithm has proved its efficiency in solving MOMPS problems compared with the genetic algorithm and simulated annealing. The results clearly showed the ability of the algorithm to evaluate properly how much, when and where extra capacities (overtime) are permitted so that the inventory can be lowered without influencing the level of service. 

Keywords


Memetic Algorithm, Master Production Schedule,Genetic Algorithm, Simulated Annealing ,Multi-Objective Optimization

References


Soares, M.M. & Vieira, G.E., 2008. A new multi-objective optimization method for master production scheduling problems based on genetic algorithm. International Journal of Advanced Manufacturing Technology, 41(5–6), pp.549–567. Available at: http://link.springer.com/10.1007/s00170-008-1481-x.

Garey, M.R. & Johnson, D.S., 1979. Computers and intractability: a guide to the theory of NP-hardness.

Radhika, S. et al., 2016. Multi-Objective Optimization of Master Production Scheduling Problems using Jaya Algorithm.,(December), pp.1729–1732.

Ahmed, J.A., Mohsin, A. & Brifcani, A., 2015. A New Internal Architecture Based on Feature Selection for Holonic Manufacturing System. , 9(8), pp.1549–1552.

Wang, Y., 2013. Constraint Cellular Ant Algorithm for the Multi-Objective Vehicle Routing Problem. JSW, 8(6), pp.1339–1345.

Guliashki, V., Toshev, H. & Korsemov, C., 2009. Survey of evolutionary algorithms used in multiobjective optimization. Problems of engineering cybernetics and robotics, 60(1), pp.42–54

Ernani Vieira*, G. & Ribas, P.C., 2004. A new multi-objective optimization method for master production scheduling problems using simulated annealing. International Journal of Production Research, 42(21), pp.4609–4622.

Vieira, G.E., Favaretto, F. & Ribas, P.C., 2004. Comparing genetic algorithms and simulated annealing in master production scheduling problems. In Proceeding of 17th International Conference on Production Research.

Radhika, S., Rao, C.S. & Pavan, K.K., 2013. A differential evolution-based optimization for master production scheduling problems. International Journal of Hybrid Information Technology, 6(5), pp.163–170.

Sajja, R. & Rao, C.S., 2014. A New Multi-Objective Optimization of Master Production Scheduling Problems Using Differential Evolution. International Journal of Applied Science and Engineering, 12(1), pp.75–86.

Wu, Z., Zhang, C. & Zhu, X., 2012. An ant colony algorithm for master production scheduling optimization. Computer Supported Cooperative Work in Design (CSCWD), 2012 IEEE 16th International Conference, pp.775–779

Bakar, M.R.A. et al., 2017. Solution for Multi-Objective Optimization Master Production Scheduling Problems Based on Swarm Intelligence Algorithms. Journal of Computational and Theoretical Nanoscience, 14(11), pp.5184–5194. Available at: http://www.ingentaconnect.com/content/10.1166/jctn.2017.6729.

Supriyanto, I. & Noche, B., 2011. Fuzzy multi-objective linear programming and simulation approach to the development of valid and realistic master production schedule. Logistics Journal, 7(1). Available at: http://www.logistics-journal.de/proceedings/2011/3103 [Accessed April 15, 2018].

Moscato, P., Cotta, C. & Mendes, A., 2004. Memetic algorithms. In New optimization techniques in engineering. Springer, pp. 53–85.

Moscato, P., 1989. On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, p.1989.

Cho, J. et al., 2017. A Survey on Modeling and Optimizing Multi-Objective Systems. , 19(3), pp.1867–1901.

Eesa, A.S. et al., 2015. Cuttlefish Algorithm – A Novel Bio-Inspired Optimization Algorithm. , (June 2013).

Decerle, J. et al., 2019. A memetic algorithm for multi-objective optimization of the home health care problem. Swarm and Evolutionary Computation, 44(August 2018), pp.712–727. Available at: https://doi.org/10.1016/j.swevo.2018.08.014.

Wang, J., Luo, P. & Zhou, J., 2017. A Memetic Algorithm for Constrainted Weapon Target Assignment Problems. , pp.182–188.

Moscato, P. & Cotta, C., 2003. A gentle introduction to memetic algorithms. In Handbook of metaheuristics. Springer, pp. 105–144.

Burke, E.K. & Silva, J.D.L., 2005. The design of memetic algorithms for scheduling and timetabling problems. In Recent Advances in Memetic Algorithms. Springer, pp. 289–311.

Luca, B., 2018. Local search algorithms for memetic algorithms: understanding behaviors using biological intelligence. In pp. 553–558.

Sultan, J.A., Jasim, O.R. & Salih, S.A., 2016. An improved Genetic Algorithm for Fuzzy Production Planning Problems with Application. University of Human Development, 3, pp.390–396.

Sultan, J.A., 2013. Proposed Hybrid Techniques for Solving Fuzzy Multi-Objective Linear Programming with Application. university of Mosul.

Ribas, P.C., 2003. Análise do uso de têmpera simulada na otimização do planejamento mestre da produção. Pontifícia Universidade Católica do Paraná, Curitiba

Wijayaningrum, V. N., & Mahmudy, W. F. (2016). Optimization of Ship’s Route Scheduling Using Genetic Algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 2(1), 180-186.




DOI: http://doi.org/10.11591/ijeecs.v18.i2.pp%25p
Total views : 8 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

shopify stats IJEECS visitor statistics