A Decision Support System for the Stochastic Aggregate Planning Problem

Abstract

An advanced decision support system is presented to answer aggregate planning questions regarding the trade-off between demand (product-mix) and supply (capacity) in a multi period stochastic setting. This tool improves the effectiveness and efficiency of sales and operation planning meetings by accounting for both revenues and costs that are relevant at the intermediate planning horizon. We develop a multi product, multi routing model, where a routing consists of a sequence of operations on different resources. Given customer demand in each time period, the model obtains the optimal production quantities in every period for each alternative routing, while explicitly taking into account the stochastic nature of both demand patterns and production lead times. This is the key difference between our approach and traditional aggregate planning models. At the same time, an optimal capacity level for each resource is obtained. We include trade-offs between level and chase strategies by

Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming

“…Using, nonlinear stochastic optimizatiom, Ning et al [ 26 ], Mirzapour Al-e-hashem et al [ 27 ] and Lieckens and Vandaele [ 28 ] developed mixed integer nonlinear mathematical programming methodologies to study the decision problem of an aggregate production plan and they consider demand and lead time as variables with uncertainty in their proposals. Nasiri et al [ 29 ] suggested a non-linear stochastic model for a production and distribution plan in a three-level supply chain (suppliers, production centers and customers).…”

Novel decision model based on mixed chase and level strategy for aggregate production planning under uncertainty: Case study in beverage industry

“…Various types of stochastic nonlinear programming models for APP subject to uncertainty were developed by Vörös (1999), Ning et al (2013), Mirzapour Al-e-hesham et al (2013 and Lieckens and Vandaele (2014). Ning et al (2013) presented a multi-product, nonlinear APP model by applying uncertainty theory where the market demand, production cost, and so on are characterised as uncertain variables.…”

“…Mirzapour Al-e-hesham et al (2013) and Lieckens and Vandaele (2014) both suggested nonlinear, mixed integer programming methodologies to study APP decision problem in presence of uncertainty. Mirzapour Al-e-hesham et al (2013) considered a multi-site APP problem in green supply chain with uncertain demand.…”

“…Mirzapour Al-e-hesham et al (2013) considered a multi-site APP problem in green supply chain with uncertain demand. Lieckens and Vandaele (2014) developed a multi-product, multi routing model where a routing consists of a sequence of operations on different resources. The uncertainty is associated with the stochastic nature of the both demand patterns and production lead times.…”