With the aim of increasing capacity to smooth production planning and coping with existence of random scrap in real fabrication processes, this paper explores an economic production quantity (EPQ)-based inventory system with randomscrap and adjustable production rate. Mathematical modeling is used to carefully portray and analyze the problem, and the expected system cost function is derived and proved to be a convex function. Then, differential calculus is employed to help determine the optimal batch size for the proposed system. Numerical example along with sensitivity analysis is provided to demonstrate applicability of the obtained results. Analytical outcomes pointed out thatthis in-depth exploration to the problem reveals diverse important managerial decision-making required information.
This study was supported by Ministry of Science and Technology of Taiwan under grant number: MOST-104-2410-H-324-008-MY2.
Taft, E.W. (1918) The most economical production lot, Iron Age, 101, pp. 1410–1412.
Mak, K.L. (1985) Inventory control of defective products when the demand is partially captive, International Journal of Production Research, 23(3), pp. 533-542.
Hariga, M., Ben-Daya, M. (1988) Note: the economic manufacturing lot-sizing problem with imperfect production processes: bounds and optimal solutions, Naval Research Logistics, 45, pp. 423-433.
Jaber, M.Y. (2006) Lot sizing for an imperfect production process with quality corrective interruptions and improvements, and reduction in setups, Computers & Industrial Engineering, 51(4), pp. 781-790.
Chakraborty, T., Chauhan, S.S., Giri, B.C. (2013) Joint effect of stock threshold level and production policy on an unreliable production environment, Applied Mathematical Modelling, 37(10-11), pp. 6593-6608.
Kundu, S., Chakrabarti, T. (2015) An integrated multistage supply chain inventory model with imperfect production process, International Journal of Industrial Engineering Computations, 6(4), pp. 565-580.
Chiu, Y-S.P., Liang, G-M., Chiu, S.W. (2016) Solving a fabrication lot-size and shipping frequency problem with an outsourcing policy and random scrap, Mathematical and Computational Applications 21(4), art. no. 45.
Romero-Jabalquinto, A. Velasco-Téllez, A., Zambrano-Robledo, P., Bermúdez-Reyes, B. (2016) Feasibility of manufacturing combustion chambers for aeronautical use in Mexico, Journal of Applied Research and Technology, 14(3), pp. 167-172.
Zhang, D., Zhang, Y., Yu, M. (2016) A machining process oriented modeling approach for reliability optimization of failure-prone manufacturing systems, Journal of Engineering Research, 4(3), pp. 128-143.
Chiu, S.W., Chen, S.W., Chiu, Y-S.P., Li, T-W. (2016) Producer–retailer integrated EMQ system with machine breakdown, rework failures, and a discontinuous inventory issuing policy, SpringerPlus, 5(1), art. no. 339.
Zhang, R., Li, J., Wu, S., Meng, D. (2016) Learning to select supplier portfolios for service supply chain. PLoS ONE, 11(5), art. no. e0155672.
Chiu, Y-S.P., Chiang, K-W., Chiu, S.W., Song, M-S. (2016) Simultaneous determination of production and shipment decisions for a multi-product inventory system with a rework process, Advances in Production Engineering & Management 11(2), pp. 141-151.
Kaylani, H., Almuhtady, A., Atieh, A.M. (2016) Novel approach to enhance the performance of production systems using lean tools, Jordan Journal of Mechanical and Industrial Engineering, 10(3), pp. 215-229.
de Kok, A.G. (1987) Approximations for operating characteristics in a production-inventory model with variable production rate, European Journal of Operational Research, 29(3), pp. 286-297.
Moon, I., Gallego, G., Simchi-Levi, D. (1991) Controllable production rates in a family production context, International Journal of Production Research, 29(12), pp. 2459-2470.
Eiamkanchanalai, S., Banerjee, A. (1999) Production lot sizing with variable production rate and explicit idle capacity cost, International Journal of Production Economics, 59(1), pp. 251-259.
Gharbi, A., Pellerin, R., Sadr, J. (2008) Production rate control for stochastic remanufacturing systems, International Journal of Production Economics, 112(1), pp. 37-47.
Sicilia, J., González-De-La-Rosa, M., Febles-Acosta, J., Alcaide-López-De-Pablo, D. (2014) Optimal policy for an inventory system with power demand, backlogged shortages and production rate proportional to demand rate, International Journal of Production Economics, 155, pp. 163-171.
Wolisz, A. (1984) Production rate optimization in a two-stage system with fi nite intermediate storage, European Journal of Operational Research, 18(3), pp. 369-376.
Arcelus, F.J., Srinivasan, G. (1987) Inventory policies under various optimizing criteria and variable markup rates, Management Science, 33(6), pp. 756-762.
Eynan, A. (2003) The benefi ts of fl exible production rates in the economic lot scheduling problem, IIE Transactions, 35(11), pp. 1057-1064.
Neidigh, R.O., Harrison, T.P. (2010) Optimising lot sizing and order scheduling with non-linear production rates, International Journal of Production Research, 48(8), pp. 2279-2295.
Biel, K., Glock, C.H. (2016) On the use of waste heat in a two-stage production system with controllable production rates, International Journal of Production Economics, 181, pp. 174-190.
Sajadi, S.M., Rad, M.F. (2016) Optimal production rate in production planning problem with simulation optimisation approach by simulated annealing, International Journal of Industrial and Systems Engineering, 22(3), pp. 262-280.
Dinesh, K.S., Ashwanth, A.R., Karthikeyan, S., Nithyanandam, G.K. (2016) Investigation to improve the new product development process of mono block pump using additive manufacturing. International Journal of Mechanical and Production Engineering Research and Development, 6(2), pp. 77-88.
Mičieta, B., Herčko, J., Botka, M., Zrnić, N. (2016) Concept of intelligent logistic for automotive industry. Journal of Applied Engineering Science, 14(2), pp. 233-238.
Oblak, L., Kuzman, M.K., Grošelj, P. (2017) A fuzzy logic-based model for analysis and evaluation of services in a Manufacturing company. Journal of Applied Engineering Science, 15(3), pp. 258-271.
Nahmias, S. (2009) Production & Operations Analysis. McGraw-Hill: New York, USA.