Using stochastic programming on ATP/CTP under uncertain ATO environment

Dechang Sun* Địa chỉ email này đã được bảo vệ từ spam bots, bạn cần kích hoạt Javascript để xem nó. *Corresponding author

Haibo Shi Địa chỉ email này đã được bảo vệ từ spam bots, bạn cần kích hoạt Javascript để xem nó.

Zhonghua Han   Địa chỉ email này đã được bảo vệ từ spam bots, bạn cần kích hoạt Javascript để xem nó.

Chang Liu Shenyang   Địa chỉ email này đã được bảo vệ từ spam bots, bạn cần kích hoạt Javascript để xem nó.

MRPII 41

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: SDCP; available-to-promise; ATP; capable-to-promise; CTP; assembly-to-order; ATO; uncertainty; hybrid genetic algorithm; HGA.