Inventory Optimization Model Parameter Search Speed-Up Through Similarity Reduction

2020 ◽  
pp. 102-114
Author(s):  
Tomáš Martinovič ◽  
Kateřina Janurová ◽  
Jan Martinovič ◽  
Kateřina Slaninová
Keyword(s):  
Optimization Model ◽  
Similarity Reduction ◽  
Model Parameter ◽  
Inventory Optimization ◽  
Parameter Search ◽  
Search Speed ◽  
Speed Up

Inventory Optimization Model Applying the FIFO Method and the PHVA Methodology to Improve the Stock Levels of Olive Products in SMEs of the Agro-Industrial Sector in Peru

2021 ◽  
pp. 736-742
Author(s):  
Rosysella Izaguirre-Malasquez ◽  
Lucia Muñoz-Gonzales ◽  
Jhonatan Cabel-Pozo ◽  
Carlos Raymundo
Keyword(s):  
Optimization Model ◽  
Industrial Sector ◽  
Inventory Optimization

A Two-Echelon Inventory Optimization Model with Demand Time Window Considerations

2004 ◽  
Vol 30 (4) ◽  
pp. 347-366 ◽  
Author(s):  
Wikrom Jaruphongsa ◽  
Sila �etinkaya ◽  
Chung-Yee Lee
Keyword(s):  
Optimization Model ◽  
Time Window ◽  
Inventory Optimization

A bi-objective inventory optimization model under inflation and discount using tuned Pareto-based algorithms: NSGA-II, NRGA, and MOPSO

2016 ◽  
Vol 43 ◽  
pp. 57-72 ◽  
Author(s):  
Seyed Mohsen Mousavi ◽  
Javad Sadeghi ◽  
Seyed Taghi Akhavan Niaki ◽  
Madjid Tavana
Keyword(s):  
Optimization Model ◽  
Nsga Ii ◽  
Inventory Optimization

scholarly journals Exact Maximum Clique Algorithm for Different Graph Types Using Machine Learning

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 97
Author(s):  
Kristjan Reba ◽  
Matej Guid ◽  
Kati Rozman ◽  
Dušanka Janežič ◽  
Janez Konc
Keyword(s):  
Machine Learning ◽  
Maximum Clique ◽  
Dynamic Algorithm ◽  
Graph Theoretic ◽  
Research Areas ◽  
Novel Approach ◽  
Search Speed ◽  
Speed Up ◽  
Clique Algorithm ◽  
And Function

Finding a maximum clique is important in research areas such as computational chemistry, social network analysis, and bioinformatics. It is possible to compare the maximum clique size between protein graphs to determine their similarity and function. In this paper, improvements based on machine learning (ML) are added to a dynamic algorithm for finding the maximum clique in a protein graph, Maximum Clique Dynamic (MaxCliqueDyn; short: MCQD). This algorithm was published in 2007 and has been widely used in bioinformatics since then. It uses an empirically determined parameter, Tlimit, that determines the algorithm’s flow. We have extended the MCQD algorithm with an initial phase of a machine learning-based prediction of the Tlimit parameter that is best suited for each input graph. Such adaptability to graph types based on state-of-the-art machine learning is a novel approach that has not been used in most graph-theoretic algorithms. We show empirically that the resulting new algorithm MCQD-ML improves search speed on certain types of graphs, in particular molecular docking graphs used in drug design where they determine energetically favorable conformations of small molecules in a protein binding site. In such cases, the speed-up is twofold.

Inventory Optimization Model Applying the Holt-Winters Method to Improve Stock Levels in SMEs in the Sports Retail Sector

2020 ◽  
pp. 711-718
Author(s):  
Diego Amasifén-Pacheco ◽  
Angela Garay-Osorio ◽  
Maribel Perez-Paredes ◽  
Carlos Raymundo-Ibañez ◽  
Luis Rivera
Keyword(s):  
Optimization Model ◽  
Retail Sector ◽  
Inventory Optimization

An integrated inventory optimization model for facility location-allocation problem

2015 ◽  
Vol 54 (12) ◽  
pp. 3640-3658 ◽  
Author(s):  
R.P. Manatkar ◽  
Kondapaneni Karthik ◽  
Sri Krishna Kumar ◽  
Manoj Kumar Tiwari
Keyword(s):  
Facility Location ◽  
Optimization Model ◽  
Allocation Problem ◽  
Location Allocation ◽  
Inventory Optimization ◽  
Integrated Inventory ◽  
Location Allocation Problem

An Inventory Optimization Model To Support Operating Room Schedules

2011 ◽  
Vol 12 (1) ◽  
pp. 56-69 ◽  
Author(s):  
James Rappold ◽  
Ben Van Roo ◽  
Christine Di Martinelly ◽  
Fouad Riane
Keyword(s):  
Operating Room ◽  
Optimization Model ◽  
Inventory Optimization
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