scholarly journals Corrigendum to “A Novel MILP Model for the Production, Lot Sizing, and Scheduling of Automotive Plastic Components on Parallel Flexible Injection Machines with Setup Common Operators”

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Beatriz Andres ◽  
Eduardo Guzman ◽  
Raul Poler
Keyword(s):  
Lot Sizing ◽  
Milp Model ◽  
Plastic Components ◽  
Lot Sizing And Scheduling

scholarly journals A Novel MILP Model for the Production, Lot Sizing, and Scheduling of Automotive Plastic Components on Parallel Flexible Injection Machines with Setup Common Operators

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Beatriz Andres ◽  
Eduardo Guzman ◽  
Raul Poler
Keyword(s):  
Supply Chain ◽  
Lot Sizing ◽  
Mixed Integer ◽  
Computational Time ◽  
Scheduling Problem ◽  
Automotive Supply Chain ◽  
Milp Model ◽  
Plastic Components ◽  
Automotive Supply ◽  
Lot Sizing And Scheduling

In this article, a mixed integer linear program (MILP) model is proposed for the production, lot sizing, and scheduling of automotive plastic components to minimize the setup, inventory, stockout, and backorder costs, by taking into account injection molds as the main index to schedule on parallel flexible injection machines. The proposed MILP considers the minimum and maximum inventory capacities and penalizes stockout. A relevant characteristic of the modeled problem is the dependence between mold setups to produce plastic components. The lot sizing and scheduling problem solution results in the assignment of molds to machines during a specific time period and in the calculation of the number of components to be produced, which is often called lot size, following a sequence-dependent setup time. Depending on the machine on which the mold is setup, the number of units to be produced will be distinct because machines differ from one another. The stock coverage, defined in demand days, is also included in the MILP to avoid backorders, which is highly penalized in the automotive supply chain. Added to this, the proposed model is extended by considering setup common operators to respond to and fulfill the constraints that appear in automotive plastic enterprises. In this regard, the MILP presented solves a lot-sizing and scheduling problem, emerged in a second-tier supplier of a real automotive supply chain. Finally, this article validates the MILP by performing experiments with different sized instances, including small, medium, and large. The large-sized dataset is characterized by replicating the amount of data used in the real enterprise, which is the object of this study. The goodness of the model is evaluated with the computational time and the deviation of the obtained results as regards to the optimal solution.

Approaches for the joint resolution of lot-sizing and scheduling with infeasibilities occurrences

2021 ◽  
pp. 107176
Author(s):  
Fernanda F. Alves ◽  
Thiago H. Nogueira ◽  
Mauricio C. de Souza ◽  
Martín G. Ravetti
Keyword(s):  
Lot Sizing ◽  
Lot Sizing And Scheduling ◽  
Joint Resolution

scholarly journals Discrete lot sizing and scheduling using product decomposition into attributes

2009 ◽  
Vol 36 (9) ◽  
pp. 2690-2698 ◽  
Author(s):  
C. Gicquel ◽  
N. Miègeville ◽  
M. Minoux ◽  
Y. Dallery
Keyword(s):  
Lot Sizing ◽  
Product Decomposition ◽  
Lot Sizing And Scheduling

scholarly journals Practical Tips for Modelling Lot-Sizing and Scheduling Problems

2009 ◽  
Vol 3 (2) ◽  
pp. 37-48
Author(s):  
Waldemar Kaczmarczyk
Keyword(s):  
Lot Sizing ◽  
Valid Inequalities ◽  
A Priori ◽  
Computational Effort ◽  
Scheduling Problems ◽  
Good Balance ◽  
Validation Of Models ◽  
Short Time ◽  
Lot Sizing And Scheduling

This paper presents some important alternatives for modelling Lot-Sizing and Scheduling Problems. First, the accuracy of models can improved by using short time buckets, which allow more detailed planning but lead to higher computational effort. Next, valid inequalities make the models tighter but increase their size. Sometimes it is possible to find a good balance between the size and tightness of a model by limiting a priori the number of valid inequalities. Finally, a special normalization of the variables simplifies the presentation of results and validation of models.

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