Product-line planning under uncertainty

Abstract

pagesThis study addresses the problem of multi-period mix of product-lines under a product-family, which incorporates launching decisions of new products, capacity expansion decisions and product interdependencies. The problem is modelled as a two-stage stochastic program with recourse in which price, demand, production cost and cannibalisation effect of new products are treated as uncertain parameters.The solution approach employs the Sample Average Approximation based on Monte Carlo bounding technique and multi-cut version of L-shaped method to solve approximate problems efficiently, which is tested on different cases considering VSS and EVPI performance measures. The data collected through two experimental studies is analysed using ANOVA and Random Forest methodology in order to understand which problem parameters are significant on the performance measures and to generate some rule-based inferences reflecting the relationship between significant parameters and the performance of the proposed stochastic model.

Multiobjective Lot Sizing and Scheduling of Multiproduct Switching Production in the Process Industry Considering Uncertain Market Information Under Mass Customization

“…For the production problem under stochastic uncertain demand, after considering the dependence of equipment conditions and production rate on output, Zhang et al [12] proposed an integrated decision-making strategy for single-machine production and maintenance under uncertain demand and solved it through a two-stage stochastic programming model. Karakaya, S and Koksal, G [13] studied a multiperiod product line mixing problem considering the interdependence between products and the destruction effect of new products in the context of uncertain price, demand and production cost and established a two-stage stochastic programming model for this purpose. Aidin Delgoshaei and Ahad Ali used the normal distribution in stochastic programming to solve uncertain market demand in dynamic unit production planning [14].…”