Title: A two-stage stochastic programming model for production lot-sizing and scheduling under demand and raw material quality uncertainties
Authors: Goutham Ramaraj; Zhengyang Hu; Guiping Hu
Addresses: Tesla Inc., 45500 Fremont Blvd., Fremont, CA 94538, USA ' Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50014, USA ' Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50014, USA
Abstract: Production planning and scheduling focus on efficient use of resources and are widely used in the manufacturing industry, especially when the system operates in an uncertain environment. The goal of this paper is to provide a two-stage stochastic programming framework for a multi-period, multi-product, lot-sizing and scheduling problem considering uncertainties in both demand and the quality of raw materials. The objectives are to determine the number of units to be produced and the production sequence so that the total production costs are minimised. The decisions made in the first stage include the basic production plan along with the production quantities and sequences, which are later updated with recourse decisions on overtime production made in the second-stage. To demonstrate the proposed decision-making framework, a case study for a manufacturing facility producing braking equipment for the automotive industry was conducted. The results show that the stochastic model is more effective in production planning under the uncertainties considered. The managerial insights derived from this study will facilitate the decision making for determining optimal production quantities and sequences under uncertainties.
Keywords: stochastic programming; production planning; uncertainty; scheduling; lot-sizing; scenario generation; scenario reduction.
International Journal of Planning and Scheduling, 2019 Vol.3 No.1, pp.1 - 27
Received: 17 Aug 2017
Accepted: 11 Jun 2018
Published online: 11 Oct 2019 *