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Model for cost estimation in a finite-capacity stochastic environment based on shop floor optimization combined with simulation

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  • Eklin, Mark
  • Arzi, Yohanan
  • Shtub, Avraham

Abstract

In recent years several researchers suggested cost estimation models that consider limited capacity. These researchers ignored the stochastic nature of the shop floor. This paper presents a cost estimation model that takes into account the stochastic environment. It is based on marginal analysis - the difference between the total cost without the new order and the total cost with the new order. The proposed model is based on the integration of simulation and optimization. Data generated by the simulation is inserted into the optimization procedure that finds good feasible solutions quickly. A significant advantage of the proposed stochastic cost estimation over an existing deterministic approach is shown. A computational study is performed to test different factors affecting the proposed model.

Suggested Citation

  • Eklin, Mark & Arzi, Yohanan & Shtub, Avraham, 2009. "Model for cost estimation in a finite-capacity stochastic environment based on shop floor optimization combined with simulation," European Journal of Operational Research, Elsevier, vol. 194(1), pages 294-306, April.
    • Handle: RePEc:eee:ejores:v:194:y:2009:i:1:p:294-306
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    References listed on IDEAS

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    1. Ramji Balakrishnan & K. Sivaramakrishnan, 2001. "Sequential Solutions to Capacity†Planning and Pricing Decisions," Contemporary Accounting Research, John Wiley & Sons, vol. 18(1), pages 1-26, March.
    2. Shtub, Avraham & Versano, Ronen, 1999. "Estimating the cost of steel pipe bending, a comparison between neural networks and regression analysis," International Journal of Production Economics, Elsevier, vol. 62(3), pages 201-207, September.
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    Cited by:

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    2. Bordoloi, Tausif & Shapira, Philip & Mativenga, Paul, 2022. "Policy interactions with research trajectories: The case of cyber-physical convergence in manufacturing and industrials," Technological Forecasting and Social Change, Elsevier, vol. 175(C).

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