Optimization of an economic production quantity-based system with random scrap and adjustable production rate

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 random scrap 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 that this in-depth exploration to the problem reveals diverse important managerial decision-making required information.

[1]  D. Simchi-Levi,et al.  Controllable production rates in a family production context , 1991 .

[2]  Singa Wang Chiu,et al.  Producer–retailer integrated EMQ system with machine breakdown, rework failures, and a discontinuous inventory issuing policy , 2016, SpringerPlus.

[3]  A. Banerjee,et al.  Production lot sizing with variable production rate and explicit idle capacity cost , 1999 .

[4]  Yuan-Shyi Peter Chiu,et al.  Solving a Fabrication Lot-Size and Shipping Frequency Problem with an Outsourcing Policy and Random Scrap , 2016 .

[5]  Joaquín Sicilia,et al.  Optimal policy for an inventory system with power demand, backlogged shortages and production rate proportional to demand rate , 2014 .

[6]  Amit Eynan,et al.  The Benefits of Flexible Production Rates in the Economic Lot Scheduling Problem , 2003 .

[7]  Tripti Chakrabarti,et al.  An integrated multi-stage supply chain inventory model with imperfect production process , 2015 .

[8]  Seyed Mojtaba Sajadi,et al.  Optimal production rate in production planning problem with simulation optimisation approach by simulated annealing , 2016 .

[9]  Leon Oblak,et al.  A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company , 2017 .

[10]  F. Arcelus,et al.  Inventory policies under various optimizing criteria and variable markup rates , 1987 .

[11]  Christoph H. Glock,et al.  On the use of waste heat in a two-stage production system with controllable production rates , 2016 .

[12]  Terry P. Harrison,et al.  Optimising lot sizing and order scheduling with non-linear production rates , 2010 .

[13]  Kai-Ling Mak.  Inventory control of defective products when the demand is partially captive , 1985 .

[14]  Yingjie Zhang,et al.  A machining process oriented modeling approach for reliability optimization of failure-prone manufacturing systems , 2016 .

[15]  de Ag Ton Kok.  Approximations for operating characteristics in a production-inventory model with variable production rate , 1987 .

[16]  Mohamad Y. Jaber,et al.  Lot sizing for an imperfect production process with quality corrective interruptions and improvements, and reduction in setups , 2006, Comput. Ind. Eng..

[17]  Bibhas C. Giri,et al.  Joint effect of stock threshold level and production policy on an unreliable production environment , 2013 .

[18]  P. Zambrano-Robledo,et al.  Feasibility of manufacturing combustion chambers for aeronautical use in Mexico , 2016 .

[19]  Mohamed Ben-Daya,et al.  Note: The economic manufacturing lot-sizing problem with imperfect production processes: Bounds and optimal solutions , 1998 .

[20]  Adam Wolisz,et al.  Production rate optimization in a two-stage system with finite intermediate storage , 1984 .

[21]  Nenad Zrnić,et al.  Concept of intelligent logistic for automotive industry , 2016 .

[22]  Rui Zhang,et al.  Learning to Select Supplier Portfolios for Service Supply Chain , 2016, PloS one.

[23]  Robert Pellerin,et al.  Production rate control for stochastic remanufacturing systems , 2008 .

[24]  Y. P. Chiu,et al.  Simultaneous determination of production and shipment decisions for a multi-product inventory system with a rework process , 2016 .