A minimum cost network flow model for the maximum covering and patrol routing problem

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.

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A crowdsourced dynamic repositioning strategy for public bike sharing systems

Numerical Algebra Control and Optimization ◽

10.3934/naco.2021049 ◽

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>

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Applying a Relax-and-Fix Approach to a Fixed Charge Network Flow Model of a Maritime Inventory Routing Problem

Lecture Notes in Computer Science - Computational Logistics ◽

10.1007/978-3-030-00898-7_1 ◽

2018 ◽

pp. 3-16 ◽

Cited By ~ 1

Author(s):

Marcelo W. Friske ◽

Luciana S. Buriol

Keyword(s):

Network Flow ◽

Flow Model ◽

Fixed Charge ◽

Inventory Routing ◽

Routing Problem ◽

Inventory Routing Problem ◽

Network Flow Model ◽

Maritime Inventory Routing

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Further results on the algebraic characterizations of a minimum cost network flow problem equivalent to the transportation problem

International Journal of Computer Mathematics ◽

10.1080/00207160512331331192 ◽

2005 ◽

Vol 82 (5) ◽

pp. 609-616

Author(s):

Mustafa özel ◽

Süleyman Şafak ◽

Hamza Bulut

Keyword(s):

Network Flow ◽

Transportation Problem ◽

Flow Problem ◽

Minimum Cost ◽

Network Flow Problem ◽

Minimum Cost Network Flow

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Minimum cost network flows: Problems, algorithms, and software

Yugoslav journal of operations research ◽

10.2298/yjor121120001s ◽

2013 ◽

Vol 23 (1) ◽

pp. 3-17 ◽

Cited By ~ 24

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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A dual network exterior point simplex-type algorithm for the minimum cost network flow problem

10.12681/eadd/28548 ◽

2012 ◽

Author(s):

Georgios Geranis

Keyword(s):

Network Flow ◽

Flow Problem ◽

Minimum Cost ◽

Network Flow Problem ◽

Type Algorithm ◽

Minimum Cost Network Flow

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Operation of a CCHP System Using an Optimal Energy Dispatch Algorithm

ASME 2008 2nd International Conference on Energy Sustainability, Volume 1 ◽

10.1115/es2008-54096 ◽

2008 ◽

Cited By ~ 4

Author(s):

Heejin Cho ◽

Sandra D. Eksioglu ◽

Rogelio Luck ◽

Louay M. Chamra

Keyword(s):

Energy Efficiency ◽

Energy Cost ◽

Network Flow ◽

Flow Model ◽

Energy Requirement ◽

Optimal Operation ◽

Operational Conditions ◽

Optimal Energy ◽

Network Flow Model ◽

The Cost

The Combined Cooling, Heating, and Power (CCHP) systems have been widely recognized as a key alternative for thermal and electric energy generation because of the outstanding energy efficiency, reduced environmental emissions, and relative independence from centralized power grids. Nevertheless, the total energy cost of CCHP systems can be highly dependent on the operation of individual components and load balancing. The latter refers to the process of fulfilling the thermal and electrical demand by partitioning or “balancing” the energy requirement between the available sources of energy supply. The energy cost can be optimized through an energy dispatch algorithm which provides operational/control signals for the optimal operation of the equipment. The algorithm provides optimal solutions on decisions regarding generating power locally or buying power from the grid. This paper presents an initial study on developing an optimal energy dispatch algorithm that minimizes the cost of energy (i.e., cost of electricity from the grid and cost of natural gas into the engine and boiler) based on energy efficiency constrains for each component. A deterministic network flow model of a typical CCHP system is developed as part of the algorithm. The advantage of using a network flow model is that the power flows and efficiency constraints throughout the CCHP components can be readily visualized to facilitate the interpretation of the results. A linear programming formulation of the network flow model is presented. In the algorithm, the inputs include the cost of the electricity and fuel and the constraints include the cooling, heating, and electric load demands and the efficiencies of the CCHP components. This algorithm has been used in simulations of several case studies on the operation of an existing micro-CHP system. Several scenarios with different operational conditions are presented in the paper to demonstrate the economical advantages resulting from optimal operation.

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