International Journal of Economics, Finance and Management Sciences

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New Combined Method for Solving the Single Level Capacitated Production Planning Model with Set up Cost, Finite Horizon and Discrete Stochastic Demand

Received: 25 May 2014    Accepted: 20 June 2014    Published: 30 June 2014
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Abstract

This paper studies the single level capacitated production planning problem with finite horizon (N periods). In each period, Set-up cost, variable cost and inventory cost exist. Also, it is assumed that the demand in each period is a discrete random variable with known probability function. In each period, if demand is bigger than inventory then we will have lost sales. In this case, we have to pay the cost of lost sales otherwise at the end of the period we will have extra products for the next period. At the end of horizon we have to sale the surplus products. In this case, price of one unit of products will be less than variable cost of production. An analytical method is proposed for solving this problem. This method can optimize the expected value of costs. In this method, expected value of costs is estimated by Monte Carlo simulation. Two examples have solved by using the proposed method. Comparison of the answers with solutions of other heuristic methods indicates the advantage of the proposed method.

DOI 10.11648/j.ijefm.20140203.14
Published in International Journal of Economics, Finance and Management Sciences (Volume 2, Issue 3, June 2014)
Page(s) 227-230
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Capacitated Production Planning, Stochastic Demand, Set Up Cost, Finite Horizon

References
[1] G.R. Bitran and H.H. Yanasse, “Deterministic approximations to stochastic production problems,” Operations Research, 32(5) (1984), 999-1018.
[2] D. Bose and A.K. Chatterjee, “Capacity planning under demand uncertainty: production postponement and production flexibility,” CLAIO 2008, September 9-12, Cartagena de Indias, Colombia.
[3] S.M.T. Fatemi Ghomi and S.S.Hashemin, “An analytical method for single level constrained resources production problem with constant setup cost,” Iranian Journal of Science and Technology, Transaction B, 26(B1) (2002), 69-82.
[4] A. Garcia and R.L. Smith, “Solving nonstationary infinite horizon stochastic pro-duction planning problems,” Operations research letters, 27(2000), 135-141.
[5] H. Gfrerer and G. Zapfel, “Hierarchical model for production planning in the case of uncertain demand,” Eu-ropean Journal of Operational, 86(1995), 142-161.
[6] K. Huang and S. Ahmed, “A stochastic programming approach for planning horizons of infinite horizon capacity planning problems,” European Journal of Operational Research, 200(1) (2010), 74-84.
[7] P. Kaminsky and J.M. Swaminathan, “Effective heuristics for capacitated production planning with multiperiod produc-tion and demand with forecast band refinement,” Manufacturing & Service Operations Manage-ment, 6(2) (2004), 184-194.
[8] R. Metters, “Production planning with stochastic seasonal demand and capacitated production,” IIE Transactions, 29(11) (1997), 1017-1029.
[9] M.Z. Meybodi and B.L. Foote, “Hierarchical production planning and scheduling with random demand and production failure,” Annals of Operations Research, 59(1) (2005), 259-280.
[10] J. Mula, R. Poler, J.P. Garcia-Sabater and F.C. Lario, “Models for production planning under uncertainty: A review,” International Journal of Production Economics, 103(2006), 271-285.
[11] S.S. Hashe-min, “Heuristic for single level capacitated production planning problem with stochastic demand and constant set-up cost, "The 2nd International Conference of Iranian Operation Research so-ciety, may 20-22. 2009.
Author Information
  • Department of Industrial Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran

  • Graduate Student in Industrial Engineering, Ministry of Science, Research and Technology ALGHADIR Non-Governmental and Private Higher Education Institution, Tabriz, Iran

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  • APA Style

    Seyed Saeid Hashemin, Elham Mohammadi. (2014). New Combined Method for Solving the Single Level Capacitated Production Planning Model with Set up Cost, Finite Horizon and Discrete Stochastic Demand. International Journal of Economics, Finance and Management Sciences, 2(3), 227-230. https://doi.org/10.11648/j.ijefm.20140203.14

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    ACS Style

    Seyed Saeid Hashemin; Elham Mohammadi. New Combined Method for Solving the Single Level Capacitated Production Planning Model with Set up Cost, Finite Horizon and Discrete Stochastic Demand. Int. J. Econ. Finance Manag. Sci. 2014, 2(3), 227-230. doi: 10.11648/j.ijefm.20140203.14

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    AMA Style

    Seyed Saeid Hashemin, Elham Mohammadi. New Combined Method for Solving the Single Level Capacitated Production Planning Model with Set up Cost, Finite Horizon and Discrete Stochastic Demand. Int J Econ Finance Manag Sci. 2014;2(3):227-230. doi: 10.11648/j.ijefm.20140203.14

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  • @article{10.11648/j.ijefm.20140203.14,
      author = {Seyed Saeid Hashemin and Elham Mohammadi},
      title = {New Combined Method for Solving the Single Level Capacitated Production Planning Model with Set up Cost, Finite Horizon and Discrete Stochastic Demand},
      journal = {International Journal of Economics, Finance and Management Sciences},
      volume = {2},
      number = {3},
      pages = {227-230},
      doi = {10.11648/j.ijefm.20140203.14},
      url = {https://doi.org/10.11648/j.ijefm.20140203.14},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijefm.20140203.14},
      abstract = {This paper studies the single level capacitated production planning problem with finite horizon (N periods). In each period, Set-up cost, variable cost and inventory cost exist. Also, it is assumed that the demand in each period is a discrete random variable with known probability function. In each period, if demand is bigger than inventory then we will have lost sales. In this case, we have to pay the cost of lost sales otherwise at the end of the period we will have extra products for the next period. At the end of horizon we have to sale the surplus products. In this case, price of one unit of products will be less than variable cost of production. An analytical method is proposed for solving this problem. This method can optimize the expected value of costs. In this method, expected value of costs is estimated by Monte Carlo simulation. Two examples have solved by using the proposed method. Comparison of the answers with solutions of other heuristic methods indicates the advantage of the proposed method.},
     year = {2014}
    }
    

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    T1  - New Combined Method for Solving the Single Level Capacitated Production Planning Model with Set up Cost, Finite Horizon and Discrete Stochastic Demand
    AU  - Seyed Saeid Hashemin
    AU  - Elham Mohammadi
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    DO  - 10.11648/j.ijefm.20140203.14
    T2  - International Journal of Economics, Finance and Management Sciences
    JF  - International Journal of Economics, Finance and Management Sciences
    JO  - International Journal of Economics, Finance and Management Sciences
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    PB  - Science Publishing Group
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    AB  - This paper studies the single level capacitated production planning problem with finite horizon (N periods). In each period, Set-up cost, variable cost and inventory cost exist. Also, it is assumed that the demand in each period is a discrete random variable with known probability function. In each period, if demand is bigger than inventory then we will have lost sales. In this case, we have to pay the cost of lost sales otherwise at the end of the period we will have extra products for the next period. At the end of horizon we have to sale the surplus products. In this case, price of one unit of products will be less than variable cost of production. An analytical method is proposed for solving this problem. This method can optimize the expected value of costs. In this method, expected value of costs is estimated by Monte Carlo simulation. Two examples have solved by using the proposed method. Comparison of the answers with solutions of other heuristic methods indicates the advantage of the proposed method.
    VL  - 2
    IS  - 3
    ER  - 

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