Title: Using stochastic programming on ATP/CTP under uncertain ATO environment

Authors: Dechang Sun; Haibo Shi; Zhonghua Han; Chang Liu

Addresses: Shenyang Institute of Automation, Chinese Academy of Science, No. 114 Nanta Street, Shenyang 110016, China; University of the Chinese Academy of Science, No. 19A Yuquan Road, Beijing 100049, China ' Shenyang Institute of Automation, Chinese Academy of Science, No. 114 Nanta Street, Shenyang 110016, China ' Shenyang Institute of Automation, Chinese Academy of Science, No. 114 Nanta Street, Shenyang 110016, China; University of the Chinese Academy of Science, No. 19A Yuquan Road, Beijing 100049, China ' Shenyang Institute of Automation, Chinese Academy of Science, No. 114 Nanta Street, Shenyang 110016, China

Abstract: For the companies adopting assemble-to-order (ATO) production strategy, providing accurate and reliable order promising is an important issue especially subject to the uncertainty. The ATO production system has the following characteristics. There are no finished goods in the strict sense. Inventory is holding at component level. The material and capacity planning is driven by the sales forecasting. Assembly scheduling is driven by the customer orders. We reviewed the existing order-promising researches which are mostly determined by the environment. In the base of analysis the influence mechanism of uncertainty and available-to-promise/capable-to-promise (ATP/CTP) allocation approach, we establish a stochastic dependent-chance programming model considering the available resources which are fluctuant. The objective is to maximise the chance of acceptance of the orders as much as possible. This means higher utilisation of assembly capacity. The available amount of resources obeyed some kind of common distribution. We develop a hybrid genetic algorithm (HGA) for solving the stochastic programming model; the main steps are chance function construction, Monte Carlo simulation, neural network approach and genetic algorithm (GA). We implement the model and algorithm in an automobile manufacturer, the uncertainty variables are fitted using historical data. The experiment result verified that the model and solver are valid.

Keywords: order promising; SDCP; stochastic dependent chance programming; available-to-promise; ATP; capable-to-promise; CTP; assemble-to-order; ATO production systems; uncertainty; hybrid genetic algorithms; HGAs; stochastic modelling; assembly capacity; Monte Carlo simulation; neural networks; automobile industry; automotive manufacturing.

DOI: 10.1504/IJMIC.2013.057136

International Journal of Modelling, Identification and Control, 2013 Vol.20 No.3, pp.242 - 252

Published online: 27 Sep 2014 *

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