Product-line planning under uncertainty

Creative Commons License

Karakaya Ş., Köksal G.

Computers and Operations Research, vol.138, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 138
  • Publication Date: 2022
  • Doi Number: 10.1016/j.cor.2021.105565
  • Journal Name: Computers and Operations Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: Product planning, Capacity expansion, Stochastic programming, Sample average approximation, Random forest, AVERAGE APPROXIMATION METHOD, PROGRAMMING APPROACH, CAPACITY, MIX, PORTFOLIO, ROBUST, MODEL, OPTIMIZATION, TECHNOLOGY, DEMAND
  • TED University Affiliated: No


© 2021 Elsevier LtdThis study addresses a multi-period product-line-mix problem considering product interdependencies and uncertainties regarding price, demand, production cost, and the cannibalization effect of new products. The problem is modeled as a two-stage stochastic program. In the first-stage, decisions for the release times of new product-lines and capacity expansion are made without waiting for the realization of random events. Sales volumes are determined in the second-stage after more information about uncertainties is revealed. The solution approach employs a sample average approximation technique based on the Monte Carlo bounding, and multi-cut version of the L-shaped method to solve approximate problems efficiently. The model and solution approach are tested on different cases considering the value-of-stochastic-solution (VSS) and the expected-value-of-perfect-information (EVPI) as performance measures, under the assumption that the decision-maker is risk-neutral. Data collected through experimental studies are analyzed using ANOVA and Random Forest method to determine the impact of both deterministic and uncertain parameters on the performance of the stochastic approach, and to generate some rule-based inferences about the behavior of the proposed model. The results demonstrate the problem environments in which the stochastic model can generate the highest benefit to the business. It is mainly found the model can provide more expected profit than the mean-value solution in problems having uncertainties, particularly when selling prices and production costs are uncertain.