A New Bi-objective Mathematical Model to Optimize Reliability and Cost of Aggregate Production Planning System in a Paper and Wood Company
الموضوعات :
Mohammad Ramyar
1
,
Esmaeil Mehdizadeh
2
,
Seyyed Mohammad Hadji Molana
3
1 - Department of Industrial Engineering, College of Engineering, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
3 - Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
الکلمات المفتاحية: Harmony search, Supply Chain, reliability, NSGA-II, Multi-objective, Aggregate Production Planning,
ملخص المقالة :
In this research, a bi-objective model is developed to deal with a supply chain including multiple suppliers, multiple manufacturers, and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem. This bi-objective model aims to minimize the total cost of supply chain including inventory costs, manufacturing costs, work force costs, hiring, and firing costs, and maximize the minimum of suppliers' and producers' reliability by the considering probabilistic lead times, to improve the performance of the system and achieve a more reliable production plan. To solve the model in small sizes, a ε-constraint method is used. A numerical example utilizing the real data from a paper and wood industry is designed and the model performance is assessed. With regard to the fact that the proposed bi-objective model is NP-Hard, for large-scale problems one multi-objective harmony search algorithm is used and its results are compared with the NSGA-II algorithm. The results demonstrate the capability and efficiency of the proposed algorithm in finding Pareto solutions.
Baykasoglu, A. (2001). Aggregate production planning using the multiple-objective tabu search. International Journal of Production,3(16), 3685–3702.
Blanchard, Benjamin S. (2004). Logistics Engineering and Management, 6th ed., USA: Pearson. Prentice Hall.
Chakraborthty, R.K., AkhtarHasin, A. (2013). Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach. International Journal of Industrial Engineering Computations, 4 (1), 1-12.
Chakrabortty R.K., Hasin M.A. (2013). Solving an aggregate production planning problem by using multi-objective genetic algorithm(MOGA) approach. International Journal of Industrial Engineering Computations,4, 1–12.
Chambari, A., Rahmati, S.H.R., Najafi, A.A. and Karimi, A. (2012). A bi-objective model to optimize reliability and cost of system with a choice of redundancy strategies. Computers & Industrial Engineering,63, 109–119.
Chunghum, H., Hong-Bae, J. and Changsoo O. (2018). A mathematical definition and basic structures for supply chain reliability: A procurement capability perspective. Computers &Industrial Engineering, 120, 334-345
Coello, C.A., Lamont G.B. and Van Veldhuizen, D.A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. 2nd Ed.,Springer,Berlin.
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions onEvolutionary Computation,6, 182–197.
Fahimnia, B., Luong, L.H.S., and Marian, R.M. (2006). Modeling and optimization of aggregate production planning–A genetic algorithm approach. International Journal of Applied Mathematics and Computer Sciences,1, 1-6.
Geem, Z.W. (2007). Harmony search algorithm for solving Sudoku. In Knowledge-Based Intelligent Information and Engineering SystemsB. Apolloni, R.J. Howlett and L. Jain, Eds., KES (2007), Part I. LNCS (LNAI), 4692, 371–378, Springer, Heidelberg.
Geem, Z.W., Kim, J-H and Loganathan, G.V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2),60–68.
Gholamian, N., Mahdavi, I., Tavakkoli-Moghaddam, R. and NezamMahdavi-Amiri, N. (2015). Comprehensive fuzzy multi-objective multi-product multi-site aggregate productionplanningdecisions in a supply chain under uncertainty. Applied Soft Computing, 37, 585-607.
Guillen, G., Bagajewicz, M., Sequeira, S.E., Espuna, A. and Puigjaner, L. (2005). Management of pricing policies and financial risk as a keyelement for short-term scheduling optimization. IndEngChem Res, 44, 557–575.
Hajipour, V., Mehdizadeh, E. and Tavakkoli- Moghaddam, R. (2014). A novel Pareto-based multi-objective vibration damping optimization algorithm to solve multi-objective optimization problems. ScientiaIranica: Transactions E, 21(6), 2368-2378.
Hanssman, F., Hess, S. (1960). A linear programming approach to production and employment scheduling. Management Technology, 1 (1), 46–51.
Haupt, R.L. and Haupt S.E. (2004). Practical genetic algorithms. 2nd Ed., John Wiley & Sons.
Holt, C. and Modigliani, F.Simon H. (1955). A linear decision rule for production and employment scheduling. Management Science, 2(1), 1–30.
Jiang, G., Kong, J., Li, G. (2008). Aggregate Production Planning Model of Production Line in Iron and Steel Enterprise Based on Genetic Algorithm. Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China.
Lanzenauer, C.H. (1970). Production and employment scheduling in multi-stage production systems. Naval Research Logistics Quarterly, 17(2), 193–198.
Leung, S.C.H. and Chan, S.S.W. (2009). A goal programming model for aggregate production planning with resource utilization constraint. Computers & Industrial Engineering, 56, 1053–1064.
Logendran, R., Nam, S.J. (1992). Aggregate production planning –A survey of models and methodologies. European Journal of Operational Research,61,255–272.
Masud, A.S.M., Hwang, C.L. (1980). An aggregate production planning model and application of three multiple objective decision methods. International Journal of Production Research, 18, 741–752.
Mirzapour Al-e-Hashem, S.M.J., Malekly, H. and Aryanezhad, M.B. (2011). A multi-objective robust optimization model for multi-productmulti-site aggregate production planning in a supply chain under uncertainty. In. J. Production Economics, 134, 28–42.
Ozdamar, L., Bozyel, M.A. and Birbil S. (1998). LA hierarchical decision support system forproduction planning (with case study). European Journal of Operational Research, 104, 403–422.
Pasandideh, S.H.R., AkhavanNiaki, S.T. and Asadi, K. (2015). Optimizing a bi-objective multi-period three echelon supply chain network with warehouse reliability. Expert Systems with Applications, 42, 2615–2623.
Rahmani, D., Mahoodian, V. (2017). Strategic and operational supply chain network design to reduce carbon emission considering reliability and robustness. Journal of Cleaner Production, 149, 607-620.
Rahmati, S.H.A., Hajipour, V. and Niaki, S.T.A. (2013). A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Applied Soft Computing, 13(4), 1728–1740.
Ramezanian, R., Rahmani, D., Barzinpour, F. (2012). An aggregate production planning model for two-phase production systems: Solving with genetic algorithm and tabu search. Expert System Application, 39(1), 1256–1263.
Ramyar, M., Mehdizadeh, E., and Hadji Molana, S.M. (2017). Optimizing reliability and cost of system for aggregate production planning in supply chain. Scientia Iranica, 24(6), 3394-3408.
Sadeghi, M., Hajiagha, S.H.R. and Hashemi S.S. (2013). A fuzzy grey goal programming approach for aggregate production planning. International Journal of Advanced Manufacturing Technology, 64, 1715–1727.
Sivasubramani, S. and Swarup, K. S. (2011). Multi-objective harmony search algorithm for optimal power flow problem.Electrical Power and Energy Systems, 33, 745–752.
Srinivas, N. and Deb, K. (1995). Multi-objective function optimization using non-dominated sorting genetic algorithms. E vol. Comput, 2 (3), 221–248.
Vahdani, B. (2015). An optimization model for multi-objective closed-loop supply chain network under uncertainty:stochastic programming method. Iranian Journal of Fuzzy Systems, 12(4),33-57
Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., Baboli, A. (2012).Reliabledesign of a forward/reverse logistics network under uncertainty: a robust-M/M/c queuing model.Transportation Research, 48 (6), 1152–1168
Wang S.C., Yeh M.F. (2014). A modified particle swarm optimization for aggregate production planning. Expert Systems with Applications,1(6), 3069-3077.
Wang, R.C. and Liang T.F. (2004). Application of fuzzy multi-objective linear programming to aggregate production planning. Computers & Industrial Engineering, 46(1), 17–41.
Wang, R.C. and Liang, T.T. (2005). Aggregate production planning with multiple fuzzy goals. International Journal of Advanced Manufacturing Technology,25, 589–597.
Yeniay, O. and Ankare, B. (2005). Penalty function methods for constrained optimization with genetic algorithms. Mathematical and Computational Application, 10, 45-56.
Zitzler, E., Thiele, L. (1998). Multi-objective optimization using evolutionary algorithms: a comparative case study. In A.E. Eiben, T. Back,M.Schoenauer and H.P. Schwefel, Eds., Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), 292-301.
