scholarly journals Heuristic-based neural networks for stochastic dynamic lot sizing problem

2013 ◽  
Vol 13 (3) ◽  
pp. 1332-1339 ◽  
Author(s):  
Ercan Şenyiğit ◽  
Muharrem Düğenci ◽  
Mehmet E. Aydin ◽  
Mithat Zeydan
Keyword(s):  
Neural Networks ◽  
Lot Sizing ◽  
Stochastic Dynamic ◽  
Lot Sizing Problem ◽  
Dynamic Lot Sizing

Solving the stochastic dynamic lot-sizing problem through nature-inspired heuristics

2012 ◽  
Vol 39 (7) ◽  
pp. 1555-1565 ◽  
Author(s):  
G.S. Piperagkas ◽  
I. Konstantaras ◽  
K. Skouri ◽  
K.E. Parsopoulos
Keyword(s):  
Lot Sizing ◽  
Stochastic Dynamic ◽  
Lot Sizing Problem ◽  
Dynamic Lot Sizing

scholarly journals Dynamic lot sizing problem for a warm/cold process

2006 ◽  
Vol 38 (11) ◽  
pp. 1027-1044 ◽  
Author(s):  
Ayhan Özgür Toy ◽  
Emre Berk
Keyword(s):  
Lot Sizing ◽  
Lot Sizing Problem ◽  
Dynamic Lot Sizing

The dynamic lot-sizing problem with variable unit costs

1995 ◽  
Vol 26 (9) ◽  
pp. 1593-1600
Author(s):  
CHING-JONG LIAO ◽  
TSUNG-SHIN HSU
Keyword(s):  
Lot Sizing ◽  
Unit Costs ◽  
Lot Sizing Problem ◽  
Dynamic Lot Sizing

scholarly journals Dynamic lot sizing problem under uncertainty of returned products in remanufacturing industry

2018 ◽  
Vol 204 ◽  
pp. 07005
Author(s):  
Iman Setyoaji
Keyword(s):  
Bayesian Inference ◽  
Lot Sizing ◽  
Replacement Ratio ◽  
Total Cost ◽  
Lot Sizing Problem ◽  
Holding Cost ◽  
Dynamic Lot Sizing ◽  
Lot Sizes

Remanufacturing processes face uncertainty in the quality of the items being returned by customers, this significant variability complicates the control of inventories. Demands can be satisfied by procured new items, but also by remanufactured returned items. This paper develops dynamic lot sizing model for remanufacturing industry under uncertainty of returned items and proposes Bayesian Inference to estimate the replacement ratio of returned items that used to determine those lot sizes for new items. The objective of this paper is to minimize the total cost composed of holding cost and set-ups cost. A numerical example is provided based on case study. The result shows that total cost is reduced to be 45%.

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