scholarly journals Seasonality of Class I Price Differential Estimates for the Southeastern United States

2001 ◽  
Vol 33 (3) ◽  
pp. 591-604
Author(s):  
Carlos E. Testuri ◽  
Richard L. Kilmer ◽  
Thomas Spreen
Keyword(s):  
Network Flow ◽  
Flow Model ◽  
Seasonal Pattern ◽  
Southeastern United States ◽  
Dairy Industry ◽  
Minimum Cost ◽  
Class I ◽  
Price Differentials ◽  
Minimum Cost Network Flow ◽  
Insight Into

AbstractThis study provides insight into the seasonality of Class I price differentials in the southeastern dairy industry. This is accomplished by analyzing monthly estimates of Class I price differentials obtained from the imputed price solution or dual solution of a generalized capacitated minimum cost network flow model of the dairy industry. A smooth seasonal pattern emerges through the monthly sequence with the lowest and highest estimated Class I price differentials occurring in April and September respectively. Miami and Jacksonville areas reach $ 5.40 and $ 4.36 per hundredweight in April and $ 6.79 and $ 5.53 per hundredweight in September.

scholarly journals A crowdsourced dynamic repositioning strategy for public bike sharing systems

2022 ◽  
Vol 12 (1) ◽  
pp. 31
Author(s):  
I-Lin Wang ◽  
Chen-Tai Hou
Keyword(s):  
Network Flow ◽  
Flow Model ◽  
Demand Forecasting ◽  
Minimum Cost ◽  
Real World Data ◽  
Time Space ◽  
Time Period ◽  
Bike Sharing ◽  
Minimum Cost Network Flow

<p style='text-indent:20px;'>Public bike sharing systems have become the most popular shared economy application in transportation. The convenience of this system depends on the availability of bikes and empty racks. One of the major challenges in operating a bike sharing system is the repositioning of bikes between rental sites to maintain sufficient bike inventory in each station at all times. Most systems hire trucks to conduct dynamic repositioning of bikes among rental sites. We have analyzed a commonly used repositioning scheme and have demonstrated its ineffectiveness. To realize a higher quality of service, we proposed a crowdsourced dynamic repositioning strategy: first, we analyzed the historical rental data via the random forest algorithm and identified important factors for demand forecasting. Second, considering 30-minute periods, we calculated the optimal bike inventory via integer programming for each rental site in each time period with a sufficient crowd for repositioning bikes. Then, we proposed a minimum cost network flow model in a time-space network for calculating the optimal voluntary rider flows for each period based on the current bike inventory, which is adjusted according to the forecasted demands. The results of computational experiments on real-world data demonstrate that our crowdsourced repositioning strategy may reduce unmet rental demands by more than 30% during rush hours compared to conventional trucks.</p>

scholarly journals Minimum cost network flows: Problems, algorithms, and software

2013 ◽  
Vol 23 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Angelo Sifaleras
Keyword(s):  
Network Flow ◽  
Flow Problem ◽  
Network Flows ◽  
Minimum Cost ◽  
Network Flow Problem ◽  
Software Packages ◽  
Wide Range ◽  
Solution Methods ◽  
Minimum Cost Network Flow ◽  
Algorithmic Approaches

We present a wide range of problems concerning minimum cost network flows, and give an overview of the classic linear single-commodity Minimum Cost Network Flow Problem (MCNFP) and some other closely related problems, either tractable or intractable. We also discuss state-of-the-art algorithmic approaches and recent advances in the solution methods for the MCNFP. Finally, optimization software packages for the MCNFP are presented.

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