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

In this article two multi-stage stochastic linear programming models are developed, one applying the stochastic programming solver integrated by Lingo 17.0 optimization software that utilizes an approximation using an identical conditional sampling and Latin-hyper-square techniques to reduce the sam...

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Bibliographic Details
Published in:PloS one 2021-06, Vol.16 (6), p.e0252801-e0252801
Main Authors: Gómez-Rocha, José Emmanuel, Hernández-Gress, Eva Selene, Rivera-Gómez, Héctor
Format: Article
Language:English
Subjects:
Approximation
Computer and Information Sciences
Engineering
Engineering and Technology
Expected values
Furniture
Furniture industry
Industrial engineering
Industrial productivity
Linear programming
Literature reviews
Management
Manufacturing
Mathematical programming
Mean
Methods
Motivation
Normal distribution
Optimization
Physical Sciences
Probability
Probability distribution
Production capacity
Production costs
Production planning
Production processes
Random variables
Redesign
Research and Analysis Methods
Route planning
Sample variance
Sensitivity analysis
Standard deviation
Stochastic programming
Stochasticity
Supply chains
Vehicle routing
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Summary:In this article two multi-stage stochastic linear programming models are developed, one applying the stochastic programming solver integrated by Lingo 17.0 optimization software that utilizes an approximation using an identical conditional sampling and Latin-hyper-square techniques to reduce the sample variance, associating the probability distributions to normal distributions with defined mean and standard deviation; and a second proposed model with a discrete distribution with 3 values and their respective probabilities of occurrence. In both cases, a scenario tree is generated. The models developed are applied to an aggregate production plan (APP) for a furniture manufacturing company located in the state of Hidalgo, Mexico, which has important clients throughout the country. Production capacity and demand are defined as random variables of the model. The main purpose of this research is to determine a feasible solution to the aggregate production plan in a reasonable computational time. The developed models were compared and analyzed. Moreover, this work was complemented with a sensitivity analysis; varying the percentage of service level, also, varying the stochastic parameters (mean and standard deviation) to test how these variations impact in the solution and decision variables.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0252801