Analytical Models in Rail Transportation: An Annotated Bibliography

Not AvailableThis research has been supported, in part, by the U.S. Department of Transportation under contract DOT-TSC-1058, Transportation Advanced Research Program (TARP)

A unified framework for primal-dual methods in minimum cost network flow problems

"October 1982."Bibliography: p. 32.National Science Foundation Contract NSF/ECS 79-20834by Dimitri P. Bertsekas

A Model for Solving the Optimal Water Allocation Problem in River Basins with Network Flow Programming When Introducing Non-Linearities

[EN] The allocation of water resources between different users is a traditional problem in many river basins. The objective is to obtain the optimal resource distribution and the associated circulating flows through the system. Network flow programming is a common technique for solving this problem. This optimisation procedure has been used many times for developing applications for concrete water systems, as well as for developing complete decision support systems. As long as many aspects of a river basin are not purely linear, the study of non-linearities will also be of great importance in water resources systems optimisation. This paper presents a generalised model for solving the optimal allocation of water resources in schemes where the objectives are minimising the demand deficits, complying with the required flows in the river and storing water in reservoirs. Evaporation from reservoirs and returns from demands are considered, and an iterative methodology is followed to solve these two non-network constraints. The model was applied to the Duero River basin (Spain). Three different network flow algorithms (Out-of-Kilter, RELAX-IVand NETFLO) were used to solve the allocation problem. Certain convergence issues were detected during the iterative process. There is a need to relate the data from the studied systems with the convergence criterion to be able to find the convergence criterion which yields the best results possible without requiring a long calculation time.We thank the Spanish Ministry of Economy and Competitivity (Comision Interministerial de Ciencia y Tecnologia, CICYT) for funding the projects INTEGRAME (contract CGL2009-11798) and SCARCE (program Consolider-Ingenio 2010, project CSD2009-00065). We also thank the European Commission (Directorate-General for Research & Innovation) for funding the project DROUGHT-R&SPI (program FP7-ENV-2011, project 282769). And last, but not least, to the Fundacion Instituto Euromediterraneo del Agua with the project "Estudio de Adaptaciones varias del modelo de optimizacion de gestiones de recursos hidricos Optiges".Haro Monteagudo, D.; Paredes Arquiola, J.; Solera Solera, A.; Andreu Álvarez, J. (2012). A Model for Solving the Optimal Water Allocation Problem in River Basins with Network Flow Programming When Introducing Non-Linearities. Water Resources Management. 26(14):4059-4071. https://doi.org/10.1007/s11269-012-0129-7S405940712614Ahuja R, Magnanti T, Orlin J (1993) Network flows: theory, algorithms and applications. Prentice Hall, New YorkAndreu J, Capilla J, Sanchís E (1996) AQUATOOL, a generalized decision-support system for water resources planning and operational management. J Hydrol 177:269–291Bersetkas D (1985) A unified framework for primal-dual methods in minimum cost network flows problems. Math Program 32:125–145Bersetkas D, Tseng P (1988) The relax codes for linear minimum cost network flow problems. Ann Oper Res 13:125–190Bersetkas D, Tseng P (1994) RELAX-IV: A faster version of the RELAX code for solving minimum cost flow problems. Completion Report under NSFGrant CCR-9103804. Dept. of Electrical Engineering and Computer Science, MIT, BostonChou F, Wu C, Lin C (2006) Simulating multi-reservoir operation rules by network flow model. ASCE Conf Proc 212:33Chung F, Archer M, DeVries J (1989) Network flow algorithm applied to California aqueduct simulation. J Water Resour Plan Manag 115:131–147Ford L, Fulkerson D (1962) Flows in networks. Princeton University Press, PrincetonFredericks J, Labadie J, Altenhofen J (1998) Decision support system for conjunctive stream-aquifer management. J Water Resour Plan Manag 124:69–78Harou JJ, Medellín-Azuara J, Zhu T et al (2010) Economic consequences of optimized water management for a prolonged, severe drought in California. Water Resour Res 46:W05522Hsu N, Cheng K (2002) Network Flow Optimization Model for Basin-Scale Water Supply Planning. J Water Resour Plan Manag 128:102–112Ilich N (1993) Improvement of the return flow allocation in the Water Resources Management Model of Alberta Environment. Can J Civ Eng 20:613–621Ilich N (2009) Limitations of network flow algorithms in river basin modeling. J Water Resour Plan Manag 135:48–55Kennington JL, Helgason RV (1980) Algorithms for network programming. John Wiley and Sons, New YorkKhaliquzzaman, Chander S (1997) Network flow programming model for multireservoir sizing. J Water Resour Plan Manag 123:15–21Kuczera G (1989) Fast Multireservoir Mulltiperiod Linear Programming Models. Water Resour Res 25:169–176Kuczera G (1993) Network linear programming codes for water-supply headworks modeling. J Water Resour Plan Manag 119:412–417Labadie J (2004) Optimal operation of multireservoir systems: state-of-the-art review. J Water Resour Plan Manag 130:93–111Labadie J (2006) MODSIM: river basin management decision support system. In: Singh W, Frevert D (eds) Watershed models. CRC, Boca Raton, pp 569–592Labadie J, Baldo M, Larson R (2000) MODSIM: decision support system for river basin management. Documentation and user manual. Dept. Of Civil Engineering, CSU, Fort CollinsManca A, Sechi G, Zuddas P (2010) Water supply network optimisation using equal flow algorithms. Water Resour Manag 24:3665–3678MMA (2000) Libro blanco del agua en España. Ministerio de Medio Ambiente, Secretaría general Técnica, Centro de PublicacionesMMA (2008) Confederación Hidrográfica del Duero. Memoria 2008. http://www.chduero.es/Inicio/Publicaciones/tabid/159/Default.aspx . Last accessed 25 June 2012Perera B, James B, Kularathna M (2005) computer software tool REALM for sustainable water allocation and management. J Environ Manag 77:291–300Rani D, Moreira M (2010) Simulation-optimization modeling: a survey and potential application in reservoir systems operation. Water Resour Manag 24:1107–1138Reca J, Roldán J, Alcaide M, López R, Camacho E (2001a) Optimisation model for water allocation in deficit irrigation systems I. Description of the model. Agric Water Manag 48:103–116Reca J, Roldán J, Alcaide M, López R, Camacho E (2001b) Optimisation model for water allocation in deficit irrigation systems II. Application to the Bembézar irrigation system. Agric Water Manag 48:117–132Sechi G, Zuddas P (2008) Multiperiod hypergraph models for water systems optimization. Water Resour Manag 22:307–320Sun H, Yeh W, Hsu N, Louie P (1995) Generalized network algorithm for water-supply-system optimization. J Water Resour Plan Manag 121:392–398Wurbs R (1993) Reservoir-system simulation and optimization models. J Water Resour Plan Manag 119:455–472Wurbs R (2005) Modeling river/reservoir system management, water allocation, and supply reliability. J Hydrol 300:100–113Yamout G, El-Fadel M (2005) An optimization approach for multi-sectoral water supply management in the greater Beirut area. Water Resour Manag 19:791–812Yates D, Sieber J, Purkey D, Hubert-Lee A (2005) WEAP21 – a demand-, priority-, and preference-driven water planning model. Part 1: model characteristics. Water Int 30:487–500Zoltay V, Vogel R, Kirshen P, Westphal K (2010) Integrated watershed management modeling: generic optimization model applied to the Ipswich river basin. J Water Resour Plan Manag 136:566–57

An O(n2(m+n Log N)log N) Min-cost Flow Algorithm

The minimum-cost flow problem is: Given a network with n vertices and m edges, find a maximum flow of minimum cost. Many network problems are easily reducible to this problem. A polynomial-time algorithm for the problem has been known for some time, but only recently a strongly polynomial algorithm was discovered. In this paper an O(n2(m + n log n)log n) algorithm is designed. The previous best algorithm, due to Fujishige and Orlin, had an O(m2(m + nlogn)logn) time bound. Thus, for dense graphs an improvement of two orders of magnitude is obtained. The algorithm in this paper is based on Fujishige's algorithm (which is based on Tardos's algorithm). Fujishige's algorithm consists of up to m iterations, each consisting of O(m log n) steps. Each step solves a single source shortest path problem with nonnegative edge lengths. This algorithm is modified in order to make an improved analysis possible. The new algorithm may still consist of up to m iterations, and an iteration may still consist of up to O(mlogn) steps, but it can still be shown that the total number of steps is bounded by O(n2logn. The improvement is due to a new technique that relates the time spent to the progress achieved

Short-term generation scheduling in a hydrothermal power system.

SIGLEAvailable from British Library Document Supply Centre- DSC:D173872 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

The development of a mathematical programming technique as a design tool for traffic management

In urban areas, competition for road space at junctions is one of the major causes of congestion and accidents. Routes chosen to avoid conflict at junctions have a mutually beneficial effect which should improve circulation and reduce accidents. A prototype design tool has been developed to provide for traffic management based on such routes. The mathematical model behind the design tool works with a given road network and a given O-D demand matrix to produce feasible routes for all drivers in such a way that the weighted sum of potential conflicts is minimised. The result is a route selection in which all journeys from origin i to destination j follow the same route. The method which works best splits the problem into single commodity problems and solves these repeatedly by the Out-of-Kilter algorithm. Good locally optimal solutions can be produced by this method, even though global optimality cannot be guaranteed. Software for a microcomputer presented here as part of the design tool is capable of solving problems on realistic networks in a reasonable time. This method is embedded in a suite of computer programs which makes the input and output straightforward. Used as a design tool in the early stages of network design it gives a network-wide view of the possibilities for reducing conflict and indicates a coherent set of traffic management measures. The ideal measure would be automatic route guidance, such as the pilot scheme currently being developed for London. Other measures include a set of one-way streets and banned turns. The resulting turning flows could be used as input to the signal optimiser TRANSYT to determine signal settings favouring the routeing pattern. The project was funded by the S. E. R. C. and carried out at Middlesex Polytechnic in collaboration with MVA Systematica

Integer programming for imbedded unimodular constraint matrices with application to a course-time scheduling problem

Ph.D.V. E. Unge

Smoothed Analysis of the Minimum-Mean Cycle Canceling Algorithm and the Network Simplex Algorithm

The minimum-cost flow (MCF) problem is a fundamental optimization problem with many applications and seems to be well understood. Over the last half century many algorithms have been developed to solve the MCF problem and these algorithms have varying worst-case bounds on their running time. However, these worst-case bounds are not always a good indication of the algorithms' performance in practice. The Network Simplex (NS) algorithm needs an exponential number of iterations for some instances, but it is considered the best algorithm in practice and performs best in experimental studies. On the other hand, the Minimum-Mean Cycle Canceling (MMCC) algorithm is strongly polynomial, but performs badly in experimental studies. To explain these differences in performance in practice we apply the framework of smoothed analysis. We show an upper bound of $O(mn^2\log(n)\log(\phi))$ for the number of iterations of the MMCC algorithm. Here $n$ is the number of nodes, $m$ is the number of edges, and $\phi$ is a parameter limiting the degree to which the edge costs are perturbed. We also show a lower bound of $\Omega(m\log(\phi))$ for the number of iterations of the MMCC algorithm, which can be strengthened to $\Omega(mn)$ when $\phi=\Theta(n^2)$. For the number of iterations of the NS algorithm we show a smoothed lower bound of $\Omega(m \cdot \min \{ n, \phi \} \cdot \phi)$.Comment: Extended abstract to appear in the proceedings of COCOON 201

Development of heuristic procedures for flight rescheduling in the aftermath of irregular airline operations

Includes bibliographical references (p. [151]-158)Airlines are constantly faced with operational problems which develop from severe weather patterns and unexpected aircraft or personnel failures. However, very little research has been done on the problem of addressing the impact of irregular operations, and developing potential decision systems which could aid in aircraft re-scheduling. The primary goal of this research project has been to develop and validate algorithms, procedures and new methodologies to be used to reschedule planned activities (flights) in the event of irregular operations in large scale scheduled transportation systems, such as airline networks. A mathematical formulation of the Airline Schedule Recovery Problem is given, along with a decision framework which is used to develop efficient solution methodologies. These heuristic procedures and algorithms have been developed for potential use in a comprehensive real-time decision support systems (DSS), incorporating several aspects of the tactical operations of the transport system. These include yield management, vehicle routing, maintenance scheduling, and crew scheduling. The heuristic procedures developed will enable the carrier to recover from an irregular operation and maintain an efficient schedule for the remainder of a given resolution horizon. The algorithms are validated using real-world operational data from a major US domestic carrier, and data from an international carrier based in the Asia Pacific region. A comprehensive case study was conducted on historical operational data to compare the output of the algorithms to what actually occurred at the airline operation control center in the aftermath of an irregularity. Some of the issues considered include the percentage of flights delayed, percentage of flights cancelled, and the overall loss in operating revenue. From these analyses, it was possible to assess the potential benefits of such algorithms on the operations of an airline