scholarly journals Least Cost Poultry Rations with Nutrient Variability: A Comparison of Linear Programming with a Margin of Safety and Stochastic Programming Models

1992 ◽  
Vol 71 (2) ◽  
pp. 255-262 ◽  
Author(s):  
THOMAS H. D'ALFONSO ◽  
WILLIAM B. ROUSH ◽  
JOSE A. VENTURA
Keyword(s):  
Linear Programming ◽  
Stochastic Programming ◽  
Programming Models ◽  
Margin Of Safety ◽  
Nutrient Variability

Mixed Integer Linear Programming Models for Selecting Ground-Level Ozone Control Strategies

2014 ◽  
Vol 19 (6) ◽  
pp. 503-514 ◽  
Author(s):  
Wei-Che Hsu ◽  
Jay M. Rosenberger ◽  
Neelesh V. Sule ◽  
Melanie L. Sattler ◽  
Victoria C. P. Chen
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Control Strategies ◽  
Ground Level ◽  
Programming Models ◽  
Mixed Integer ◽  
Ground Level Ozone ◽  
Ozone Control

scholarly journals Optimization of a k-covering of a bounded set with circles of two given radii

2021 ◽  
Vol 11 (1) ◽  
pp. 232-240
Author(s):  
Alexander V. Khorkov ◽  
Shamil I. Galiev
Keyword(s):  
Linear Programming ◽  
Numerical Method ◽  
Lower Bounds ◽  
Integer Linear Programming ◽  
Numerical Results ◽  
Programming Models ◽  
Bounded Set ◽  
The Given

Abstract A numerical method for investigating k-coverings of a convex bounded set with circles of two given radii is proposed. Cases with constraints on the distances between the covering circle centers are considered. An algorithm for finding an approximate number of such circles and the arrangement of their centers is described. For certain specific cases, approximate lower bounds of the density of the k-covering of the given domain are found. We use either 0–1 linear programming or general integer linear programming models. Numerical results demonstrating the effectiveness of the proposed methods are presented.

scholarly journals Applying Simulation Modelling in Quantifying Optimization Results

2021 ◽  
Vol 15 (4) ◽  
pp. 518-523
Author(s):  
Ratko Stanković ◽  
Diana Božić
Keyword(s):  
Linear Programming ◽  
Simulation Model ◽  
Input Data ◽  
Optimization Problems ◽  
Programming Model ◽  
Optimal Solution ◽  
Simulation Modelling ◽  
Simulation Software ◽  
Programming Models ◽  
Freight Forwarding

Improvements achieved by applying linear programming models in solving optimization problems in logistics cannot always be expressed by physically measurable values (dimensions), but in non-dimensional values. Therefore, it may be difficult to present the actual benefits of the improvements to the stake holders of the system being optimized. In this article, a possibility of applying simulation modelling in quantifying results of optimizing cross dock terminal gates allocation is outlined. Optimal solution is obtained on the linear programming model by using MS Excel spreadsheet optimizer, while the results are quantified on the simulation model, by using Rockwell Automation simulation software. Input data are collected from a freight forwarding company in Zagreb, specialized in groupage transport (Less Than Truckload - LTL).

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