Relevant bibliographies by topics / Transportation Network analysis (Planning) Mathematical optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Transportation Network analysis (Planning) Mathematical optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Transportation Network analysis (Planning) Mathematical optimization"

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Zeng, Mingbin, Xu Yang, Mengxing Wang, and Bangjiang Xu. "Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm." Algorithms 11, no. 8 (August 15, 2018): 127. http://dx.doi.org/10.3390/a11080127.

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In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.
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Sun, Yan, Xia Liang, Xinya Li, and Chen Zhang. "A Fuzzy Programming Method for Modeling Demand Uncertainty in the Capacitated Road–Rail Multimodal Routing Problem with Time Windows." Symmetry 11, no. 1 (January 15, 2019): 91. http://dx.doi.org/10.3390/sym11010091.

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Demand uncertainty is an important issue that influences the strategic, tactical, and operational-level decision making in the transportation/logistics/supply chain planning. In this study, we explore the effect of demand uncertainty on the operational-level freight routing problem in the capacitated multimodal transportation network that consists of schedule-based rail transportation and time-flexible road transportation. Considering the imprecise characteristic of the demand, we adopt fuzzy set theory to model its uncertainty and use trapezoidal fuzzy numbers to represent the fuzzy demands. We set multiple transportation orders as the optimization object and employ soft time windows to reflect the customer requirement on on-time transportation. Under the above situation, we establish a fuzzy mixed integer nonlinear programming (FMINLP) model to formulate the capacitated road–rail multimodal routing problem with demand uncertainty and time windows. We first use the fuzzy expected value model and credibility measure based fuzzy chance-constrained programming to realize the defuzziness of the model and then adopt linearization technique to reformulate the crisp model to finally generate an equivalent mixed integer linear programming (MILP) model that can be solved by standard mathematical programming software. Finally, a numerical case is presented to demonstrate the feasibility of the proposed method. Sensitivity analysis and fuzzy simulation are combined to quantify the effect of demand uncertainty on the routing problem and also reveal some helpful insights and managerial implications.
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Grudz, Volodymyr, Yaroslav Grudz, Vasyl Zapukhliak, Ihor Chudyk, Lubomyr Poberezhny, Nazar Slobodyan, and Vitaliy Bodnar. "Optimal Gas Transport Management Taking into Account Reliability Factor." Management Systems in Production Engineering 28, no. 3 (September 1, 2020): 202–8. http://dx.doi.org/10.2478/mspe-2020-0030.

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AbstractIn the period of shortage of gas supply, special attention is given to reducing the supply of gas to its consumers, that is, their complete and uninterrupted gas supply. Increasing gas losses associated with technological transportation costs, in particular caused by gas flow instability and frequent changes in gas transmission network operating modes. Considering losses due to unreliability of gas pumping is one of the important tasks of gas supply optimization. The purpose of the study is to develop an optimization mathematical model that will simultaneously take into account the factors of reliability and minimum lossesIn the general case, the optimization calculations of the modes of operation of the main gas pipelines are intended to solve three main problems: determining the maximum productivity, calculating the optimal mode with a given productivity and choosing the optimal strategy, the development of the pipeline. On the basis of approaches of simulation modeling of complex systems, a multi-parameter mathematical model of gas supply process optimization was developed. It is shown that a comparative analysis of the forecast and actual indicators of the operating modes of the plunger gas pumping unit shows their satisfactory convergence. The performance of the compressor operation period in the process of injection according to the forecast deviates from the actual value for the whole period of operation of the plunger gas pumping unit in 2016 by 2.98%. The optimization problem of gas pumping planning is considered, taking into account the expected losses, on the basis of which the transfer of the controlled system from the initial state to the final one is carried out by such a sequence of states that minimizes the total cost of the system evolution.
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Movaghar, Sara, Mahmoud Mesbah, and Meeghat Habibian. "Optimum Location of Autonomous Vehicle Lanes: A Model Considering Capacity Variation." Mathematical Problems in Engineering 2020 (May 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/5782072.

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This paper proposes a model to find the optimal location of autonomous vehicle lanes in a transportation network consisting of both Autonomous Vehicles (AVs) and Human-Driven Vehicles (HDVs) while accounting for the roadway capacity variation. The main contribution of the model is considering a generalized definition of capacity as a function of AV proportion on a link and incorporating it into the network design problem. A bilevel optimization model is proposed with total travel time as the objective function to be minimized. At the upper-level problem, the optimal locations of AV lanes are determined, and at the lower level which is a multiclass equilibrium assignment, road users including both AVs and HDVs seek to minimize their individual travel times. It is shown that if capacity variation is ignored, the effect of AV lane deployment can be misleading. Since there will be a long transition period during which both AVs and HDVs will coexist in the network, this model can help the network managers to optimally reallocate the valuable road space and better understand the effects of AV lane deployment at the planning horizon as well as during the transition period. Employing this model as a planning tool presents how the proposed AV lane deployment plan could consider the AV market penetration growth during the transition period. Numerical analysis based on the Sioux Falls network is presented in two cases with and without variable capacity to illustrate the application of this model. At the 60% penetration rate of AVs, the improvement in total travel time was 3.85% with a fix capacity while this improvement was 9.88% with a variable capacity.
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Butko, T. V., V. M. Prokhorov, L. O. Parkhomenko, and A. O. Prokopov. "Improvement of Technology of Passenger Intermodal Transportation with Involvement of Railway Transport in the Conditions of Tourism Development." Science and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport, no. 1(91) (February 15, 2021): 37–50. http://dx.doi.org/10.15802/stp2021/228106.

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Purpose. The main purpose of the authors is to define and methodically substantiate the ways to increase the efficiency of intermodal passenger transportations with the involvement of high-speed trains as an auxiliary mode of transport in terms of sea and river tourism. Methodology. In the process of research the following was used: the method of factor analysis – to determine the factors influencing the attractiveness of tourist travel using high-speed trains as ancillary transport; method of skipping stops – to increase the efficiency of using high-speed trains as an auxiliary mode of transport when making tourist trips; methods of construction and training of generative-adversarial networks for the formation of model of passenger flows forecasting, on the basis of historical data of multivariate time series; method of genetic algorithms – to optimize the model of mixed-integer programming, which allows obtaining the optimal scheme of high-speed trains on the line. Findings. In order to preserve the attractiveness of tourist travels and increase the route speed of trains, it is proposed to improve the technology of planning their work based on the method of skipping stops. A mathematical model of mixed-integer programming has been formed, which simultaneously provides the attractiveness of tourist travel and profitability for railway operators. To prepare the initial data, a method for forecasting passenger flows based on multivariate time series has been developed. The optimization procedure of the generated model was implemented in the form of software in the Matlab language. Originality. The method of skipping stops, which was first used to improve the technology of intermodal passenger traffic, was further developed in the work. An original method for predicting passenger flows based on multivariate time series using a modern model of generative-competitive neural networks is proposed. Practical value. The obtained results are aimed at improving the methodological approaches to the formation of modern technologies of intermodal passenger transportation and the realization of the potential of high-speed rail transportations as a basis for the comprehensive development of tourism.
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Mnif, Mouna Gargouri, and Sadok Bouamama. "Multi-Objective Optimization Methods for Transportation Network Problems." International Journal of Operations Research and Information Systems 11, no. 1 (January 2020): 1–36. http://dx.doi.org/10.4018/ijoris.2020010101.

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This article recapitulates literature research solving transportation problems and these variants, notably the multimodal transportation problems variants. Moreover, the existing optimization methods critiqued and synthesized their efficiency to solve the transportation problem. This problem can be identified by various criteria and objectives functions that distinguished according to the case study. Based on the existing literature research, a taxonomy is proposed to distinguish different factors and criteria that perform and influence the multi-objective optimization on the transportation network planning problems. The transportation problems are cited according to these objective functions, and the variant of the problem by referring to the previous studies. In this article, the authors have focused their attention on a recent multi-objective mathematical model to solve the planning network of the multimodal transportation problem.
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Liu, Fengxian, and Kelvin C. P. Wang. "Pavement Performance-Oriented Network Optimization System." Transportation Research Record: Journal of the Transportation Research Board 1524, no. 1 (January 1996): 86–93. http://dx.doi.org/10.1177/0361198196152400110.

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The objective of the current network optimization system (NOS) model used by the Arizona Department of Transportation is to minimize the annual cost over the planning period. An NOS model with the objective of maximizing a network's pavement performance is presented. In this performance-oriented NOS, the annual budget is given as a constraint that is distributed evenly over the planning period. This NOS model aims to provide an approach simulating a possible scenario in a state department of transportation by effectively using a given budget in the planning period. At the same time the performance of the pavement network is also maximized. In relation to annual budget and pavement condition, two types of analysis are conducted: in the first analysis, only annual budget is applied as the constraint for this model; in the second, both annual budget and pavement performance requirements are introduced as the constraints. The necessary minimum annual budgets for various pavement performance requirements are compared; using the results, a rational annual budget for the needed pavement performance standards can be obtained, which may be used as decision support data for future budget allocation.
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Bajracharya, Aprin, and Amrit Man Nakarmi. "Public Transportation Energy Planning by Network Analysis-A Case Study of Kathmandu Valley." Journal of Advanced College of Engineering and Management 6 (July 6, 2021): 9–21. http://dx.doi.org/10.3126/jacem.v6i0.38273.

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This paper is an attempt to find out the required optimum number of vehicles in the Top Ten Routes of Kathmandu Valley which was found from the 163 number of routes of our study on the basis of total travel demand measured in passenger-km per year. The transportation optimization model has been prepared on the Microsoft-Excel Spreadsheet & the optimization of distribution of vehicles is done by using Premium Solver. The results clearly show that the requirement of buses at some routes was less than the available buses plying on the route & the requirement of buses at some routes was more than the available buses plying on the route. The optimization is done on the basis of least cost methods fulfilling the travel demands of flow of passengers at different interval of time in a day at each route of our study. More number of required vehicles in the optimized scenario in the route means more transportation cost, more energy consumption & more environmental emissions than the present scenario & Lesser number of required vehicles in the optimized scenario in the route means lesser transportation cost, lesser energy consumption & lesser environmental emissions than the present scenario.
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Aleksejevs, Ruslans, Raufs Guseinovs, Alexander N. Medvedev, and Sharif E. Guseynov. "Groupage Cargo Transportation Model." Transport and Telecommunication Journal 17, no. 1 (March 1, 2016): 60–72. http://dx.doi.org/10.1515/ttj-2016-0007.

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Abstract In this work we consider a specific problem of optimal planning of maritime transportation of multiproduct cargo by ships of one (corporate strategy) or several (partially corporate strategy) companies: the core of the problem consists of the existence of the network of intermediate seaports (i.e. transitional seaports), where for every ship arrived the cargo handling is done, and which are situated between the starting and the finishing seaports. In this work, there are mathematical models built from scratch in the form of multicriteria optimization problem; then the goal attainment method of Gembicki is used for reducing the built models to a one-criterion problem of linear programming.
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Vorobyov, Valery, Aleksey Manakov, Anatoly Reger, and Iuliia Tanaino. "Optimization of processes in railways based on the cost management model." MATEC Web of Conferences 216 (2018): 02009. http://dx.doi.org/10.1051/matecconf/201821602009.

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The main aim of activities of the branches of vertically integrated Russian Railways JSC is enhancing of manageability and efficiency of business operations. At the current stage, the issue of optimization of costs for product transportation and facilities maintenance in the system of production and technological processes of the railway network has gained relevance. The purpose of this study is development of a mathematical model for the logistic system of transportation, storage, cargo-handling operations, facilities maintenance and reconstruction. Methods of spatial and dynamic modeling in the structure of end-to-end planning of operations were used in the study. Optimization criterion is aggregate cost minimization. Cost function is proposed in the form of local functions of multiple sections along the railway network. The modeling result consists in determining the plans of transportation and supply with minimum costs.
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Dissertations / Theses on the topic "Transportation Network analysis (Planning) Mathematical optimization"

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Guan, Junfei. "Simultaneous optimization of transit line configuration and passenger line assignment /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?CIVL%202004%20GUAN.

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Yim, Ka-wing, and 嚴家榮. "A reliability-based land use and transportation optimization model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34618879.

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Wang, Shentao. "Supply chain planning using network flow optimization." online access from Digital Dissertation Consortium access full-text, 2003. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?MQ89164.

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Yim, Ka-wing. "A reliability-based land use and transportation optimization model." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B34618879.

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Hojnacki, Susan M. "Optimizing algorithms for shortest path analysis /." Online version of thesis, 1991. http://hdl.handle.net/1850/11143.

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Haiba, Mohamed Salem. "A study and implementation of the network flow problem and edge integrity of networks." Virtual Press, 1991. http://liblink.bsu.edu/uhtbin/catkey/834644.

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Fundamental problems in graph theory are of four types existence, construction, enumeration and optimization problems. Optimization problems lie at the interface between computer science and the field of operations research and are of primary importance in decision-making. In this thesis, two optimization problems are studied: the edge-integrity of networks and the network flow problem. An implementation of the corresponding algorithms is also realized.The edge integrity of a communication network provides a way to assess the vulnerability of the network to disruption through the destruction or failure of some of its links. While the computation of the edge-integrity of graphs in general has been proven to be NPcomplete, a recently published paper was devoted to a good algorithm using a technique of edge separation sequence for computing the edge integrity of trees. The main results of this paper will be presented and an implementation of this algorithm is achieved.The network flow problem models a distribution system in which commodities are flowing through an interconnected network. The goal is to find a maximum feasible flow and its value, given the capacity constraints for each edge. The three majors algorithms for this problem (Ford -Fulkerso n, Edmonds-Karp method, MPKM algorithm) are discussed, their complexities compared and an implementation of the Ford-Fulkerson and the MPKM algorithms is presented.
Department of Computer Science
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Brayman, Vladimir. "Hierarchical distributed algorithm for optimization of flows and prices in logistics distribution networks /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/5876.

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Boginski, Vladimir L. "Optimization and information retrieval techniques for complex networks." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011379.

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Wong, Chi-Fai. "CACAO : client-assisted channel assignment optimization for uncoordinated home WLANs /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20WONG.

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Wan, Yen-Tai. "Material transport system design in manufacturing." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-03282006-231022/.

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Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2006.
Dr. Yih-Long Chang, Committee Member ; Dr. Martin Savelsbergh, Committee Member ; Dr. Leon McGinnis, Committee Co-Chair ; Dr. Gunter Sharp, Committee Chair ; Dr. Doug Bodner, Committee Member ; Dr. Joel Sokol, Committee Member.
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Books on the topic "Transportation Network analysis (Planning) Mathematical optimization"

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Network optimization. London: Chapman & Hall, 1995.

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Network optimization: Continuous and discrete methods. Belmont, Mass: Athena Scientific, 1998.

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L, Magnanti Thomas, and Orlin James B, eds. Network flows: Theory, algorithms, and applications. Englewood Cliffs, NJ: Prentice Hall, 1993.

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Ahuja, Ravindra K. Network flows. Cambridge, Mass: Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1988.

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Linear network optimization: Algorithms and codes. Cambridge, Mass: MIT Press, 1991.

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Orlin, James B. Polynomial dual network simplex algorithms. Cambridge, Mass: Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1991.

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Orlin, James B. Polynomial dual network simplex algorithms. Stanford, Calif: Dept. of Computer Science, Stanford University, 1991.

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Ran, Bin. Modeling dynamic transportation networks: An intelligent transportation system oriented approach. 2nd ed. Berlin: Springer, 1996.

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Teodorović, D. Transportation networks: A quantitative treatment. New York: Gordon and Breach Science Publishers, 1986.

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Teodorovic, D. Transportation networks: A quantitative treatment. New York: Gordon and Breach Science Publishers, 1986.

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Book chapters on the topic "Transportation Network analysis (Planning) Mathematical optimization"

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Dimitriou, Loukas, Antonios Kaltsounis, and Antony Stathopoulos. "Pareto Evolutionary Optimization of Joint Network Design and Pricing Strategies Related to Emissions in Urban Networks." In Transportation Systems and Engineering, 221–49. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8473-7.ch011.

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In the current social, physical, economical and environmental circumstances of scarcity, the design and management of vital lifelines like transportation systems, especially within the metropolitan context, are subject to the implementation of multiple objectives in a unified framework. Thus, one of the most important issues is the identification of optimal trade-offs among crucial objectives both from the designer's as well as from the users' perspective. In the current study a comprehensive framework for estimating optimal interrelations and dilemmas among emissions-related carbon footprint and other (social- and economic-related) features of urban road networks design and operation are presented and analyzed, based on techniques of multi-objective and hierarchical mathematical programming with equilibrium constraints, solved by suitable hybridization of evolutionary algorithms. The results of the proposed optimization methodological approach provide the Pareto Frontier of solutions, which corresponds to optimal trade-offs amongst multiple objectives. The computational experience from the application of the proposed methodological approach on a part of a realistic urban network is presented, providing evidence on the applicability as well as on the computational burden of such transportation design paradigms, but most importantly, on the dilemmas emerging in sustainable design and planning of transportation systems.
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Dimitriou, Loukas, Antonios Kaltsounis, and Antony Stathopoulos. "Pareto Evolutionary Optimization of Joint Network Design and Pricing Strategies Related to Emissions in Urban Networks." In Civil and Environmental Engineering, 504–34. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9619-8.ch021.

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In the current social, physical, economical and environmental circumstances of scarcity, the design and management of vital lifelines like transportation systems, especially within the metropolitan context, are subject to the implementation of multiple objectives in a unified framework. Thus, one of the most important issues is the identification of optimal trade-offs among crucial objectives both from the designer's as well as from the users' perspective. In the current study a comprehensive framework for estimating optimal interrelations and dilemmas among emissions-related carbon footprint and other (social- and economic-related) features of urban road networks design and operation are presented and analyzed, based on techniques of multi-objective and hierarchical mathematical programming with equilibrium constraints, solved by suitable hybridization of evolutionary algorithms. The results of the proposed optimization methodological approach provide the Pareto Frontier of solutions, which corresponds to optimal trade-offs amongst multiple objectives. The computational experience from the application of the proposed methodological approach on a part of a realistic urban network is presented, providing evidence on the applicability as well as on the computational burden of such transportation design paradigms, but most importantly, on the dilemmas emerging in sustainable design and planning of transportation systems.
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Trestian, Ramona, Faisal Zaman, and Gabriel-Miro Muntean. "Spotted." In Handbook of Research on Innovations in Information Retrieval, Analysis, and Management, 1–40. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8833-9.ch001.

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Being able to react fast to exceptional events such as riots protests or disaster preventions is of paramount importance, especially when trying to ensure peoples' safety and security, or even save lives. This chapter presents a study on the use of fully anonymized and highly aggregated cellular network data, like Call Detail Records (CDRs) in order to connect people, locations and events. The goal of the study is to see if the CDR data can be used to detect exceptional spatio-temporal patterns of the collective human mobile data usage and correlate these ‘anomalies' with real-world events (e.g., parades, public concerts, soccer match, traffic congestion, riots protests etc.). These observations could be further used to develop an intelligent system that detects exceptional events in real-time from CDRs data monitoring. Such system could be used in intelligent transportation management, urban planning, emergency situations, network resource allocation and performance optimization, etc.
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Floudas, Christodoulos A. "Mixed-Integer Linear Optimization." In Nonlinear and Mixed-Integer Optimization. Oxford University Press, 1995. http://dx.doi.org/10.1093/oso/9780195100563.003.0010.

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This chapter provides an introduction to the basic notions in Mixed-Integer Linear Optimization. Sections 5.1 and 5.2 present the motivation, formulation, and outline of methods. Section 5.3 discusses the key ideas in a branch and bound framework for mixed-integer linear programming problems. A large number of optimization models have continuous and integer variables which appear linearly, and hence separably, in the objective function and constraints. These mathematical models are denoted as Mixed-Integer Linear Programming MILP problems. In many applications of MILP models the integer variables are 0 — 1 variables (i.e., binary variables), and in this chapter we will focus on this sub-class of MILP problems. A wide range of applications can be modeled as mixed-integer linear programming MILP problems. These applications have attracted a lot of attention in the field of Operations Research and include facility location and allocation problems, scheduling problems, and fixed-charge network problems. The excellent books of Nemhauser and Wolsey (1988), and Parker and Rardin (1988) provide not only an exposition to such applications but also very thorough presentation of the theory of discrete optimization. Applications of MILP models in Chemical Engineering have also received significant attention particularly in the areas of Process Synthesis, Design, and Control. These applications include (i) the minimum number of matches in heat exchanger synthesis (Papoulias and Grossmann, 1983; see also chapter 8) (ii) heat integration of sharp distillation sequences (Andrecovich and Westerberg, 1985); (iii) multicomponent multiproduct distillation column synthesis (Floudas and Anastasiadis, 1988); (iv) multiperiod heat exchanger network, and distillation system synthesis (Floudas and Grossmann, 1986; Paules and Floudas, 1988); flexibility analysis of chemical processes (Grossmann and Floudas, 1987); (v) structural properties of control systems (Georgiou and Floudas, 1989, 1990); (vi) scheduling of batch processes (e.g., Rich and Prokapakis, 1986, 1986; Kondili et al., 1993; Shah et al, 1993; Voudouris and Grossmann, 1992, 1993); and (vii) planning and scheduling of batch processes (Shah and Pantelides, 1991, Sahinidis et al, 1989, Sahinidis and Grossmann, 1991). In addition to the above applications, MILP models are employed as subproblems in the mixed-integer nonlinear optimization approaches which we will discuss in the next chapter. In this section, we will present the formulation of Mixed-Integer Linear Programming MILP problems, discuss the complexity issues, and provide a brief overview of the solution methodologies proposed for MILP models.
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Conference papers on the topic "Transportation Network analysis (Planning) Mathematical optimization"

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Zhang, Yue, Yunke Zhang, and Wanlong Xu. "Network planning model analysis based on mathematical optimization theory." In 2016 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2016. http://dx.doi.org/10.1109/cesys.2016.7889914.

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Du, Guixin, and Junjie Li. "Application of Mathematical Statistical Analysis in Neural Network Model Optimization." In 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2021. http://dx.doi.org/10.1109/icitbs53129.2021.00192.

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Li, Kengjian, Yuchuan Chen, and Yuxin Chen. "Network power planning model analysis for large-scale transmission expansion based on mathematical optimization theory." In 2016 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2016. http://dx.doi.org/10.1109/cesys.2016.7889909.

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Kulkarni, Sukrut Shridhar, and Marliana Bt Mohammad. "Improvising Integrated Gas Planning Through Network Optimization for End-To-End Value Chain Enhancement." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21235-ms.

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Abstract This paper describes a suggestion to improvise an integrated gas planning process through network optimization. As a prudent operator it is imperative to formulate long-term gas supply outlook and scenarios to ensure efficient and effective resource management with due considerations of growth strategies while maximizing value for purpose of production-focused conversations, technical assessment of forthcoming developments, commercial arrangement policy and strategic expansions. Also, it necessitates to develop and implement resolution plan arising from supply planning areas i.e. shortfall mitigation, facilities ullage, constraint and complying specifications commitment. It thereby imposes to implement robust network optimization workflow in place to improvise the integrated gas management cycle to manage the current existing gas supply and to also regulate strategy in terms of line-ups, evacuation path of forthcoming fields economically. Precedingly integrated gas planning exercise was executed via stacking up list of production sources with forecasted demands. The precedent approach was emphasized purely on mathematical and statistical method of capitalizing the production profile and geographical traits of the production sources. Notwithstanding, the approach usually linked with identified challenges and pain points throughout the planning cycle. Challenges and pain points in integrated gas planning were briefly outlined to understand limitations of existing work process as well as the need of improvising the same by embedding network optimization by simulation modeling. It was observed that multiple challenges did occur during planning preparation until post planning implementation. Workflow for strategic integrated gas planning was established to include step by step process to illustrate the ideal case otherwise known as base case scenario. The work process for constructing a mathematical model for integrated gas planning was demonstrated to reflect the complexity of the process and landscape network. For each process, expectations were clarified to ensure robustness of the analysis. The limitations in the mathematical/statistical model workflow process was complimented by the enhancing method through network optimization. Network optimization was evaluated by leveraging on the development of holistic integrated modelling for current complex offshore facilities to empower and safeguard the proposed line up of new fields meeting technical allowances such as ullage, pressure balancing & supply/demand requirements, contaminant management in accordance with strategic planning & operations. Novel idea was established to create physical prototype (network model) of offshore supply network with building components such as source (fields), connectors (export pipelines and highways), sinks (multiple terminals), and pressure boosters (pump/compressor) were embedded in model for landscape along with multiple receiving end terminals. Network simulation model was also validated with Plant information PI data to yield representative results prior deployment. Situational analysis (what-if scenarios) were conducted to evaluate to root cause analysis and troubleshooting at several nodes in the network to cater for harmonic balance. Gap analysis was also executed to identify the necessary alterations to operating philosophy, partial segregation of system to cater for product demand and quality. Simulation network model was also utilized to explore different evacuation routes that could adhere to business rules/standards to optimize the work process and boost up the efficiency of current network. The above approach of improvising the integrated gas planning through network optimization truly enhance the end to end value chain by constituting result matter in validating the mathematical planning model with technical simulation to ensure robustness in management decision of certain strategies for the planning scenarios. It could also advocate the planning numbers by ensuring the do-ability and steer optimal solution for value maximization by deciphering the impediments and strengthening the analysis.
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5

Vilela, Plínio, Mônica Cachoni, Anderson Vieira, and Luciano Christofoletti. "Train Circulation Planning: Quantitative Approaches." In 2017 Joint Rail Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/jrc2017-2223.

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The railway traffic system is an important player in passenger and freight transportation. This paper aims to present a survey of optimization models for the most commonly studied rail transportation problems related to train scheduling. We propose a classification of models and describe their characteristics by focusing on model structure and algorithmic aspects. Most reviewed papers have been proposed during the last decades. Apart from a few exceptions, the survey concentrates on published and easily accessible material. We have also elected to limit ourselves to contributions dealing specifically with rail transportation planning in single and double tracks. Each model has different goals, such as, to minimize service delays, to reduce the unscheduled train stops or to minimize the total time a train has to remain motionless, specially to allow crossings. For each group of problems, we propose a classification of models and describe their important characteristics by focusing on model structure and algorithmic aspects. The literature review involve papers published since the 1970s, but recent publications suggest that the problem is still heavily investigated. The main approaches considered are those that focus on Mathematical Optimization and Simulation. The review also considers the approach used to generate the solution, the type of railroad (real or hypothetical), and the infrastructure characteristics used to represent the railroad model. Our analysis focuses on showing an overview of those planning models.
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He, Guoxi, Yongtu Liang, Limin Fang, Qi Zheng, and Liying Sun. "Optimization of Planning and Scheduling of Refinery Product Based on Downstream Requirements." In 2016 11th International Pipeline Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ipc2016-64150.

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The disconnect between the optimization systems of upstream production and downstream demand poses a legitimate problem for China’s refined oil industry in terms of overproduction waste. Established methods only partially model the refinery system and are unable to integrate detailed production plans or meet market demands. Therefore, the research on production scheduling optimization combined with the demand of downstream pipeline network has very real applications that not only reduce the consumption of human/material resources, but also increase economic efficiency. This paper aims to optimize the production scheduling of refined oil transportation based on the demand of downstream product pipelines by analyzing the relationships between crude oil supply, refinery facility capacities and refinery tanks storage. The new model will minimize the refined production surplus therefore minimizing refinery costs and wastage. This is done by implementing models custom designed to optimize the three subsystems of the overall process: oil product blending scheduling optimization, producing and processing equipment scheduling optimization, and mixed crude oil scheduling optimization. We first analyzed the relationship between all the production units from the crude oil to the distributional destinations of oil products. A mathematical model of the refinery production scheduling was then built with minimum total surplus inventory as the objective function. We assumed a known downstream demand and used a step by step model to optimize oil stocks. The oil blending plan, production scheduling, amount of crude oil, and refined oil mixing ratios were all derived from the model using three methods: a nonlinear method called Particle Swarm Optimization (PSO), the simplex method and the enumeration method. The evidence laid out in this paper verifies our models functionality and suggests that systems can be significantly optimized by using these methods which can provide solutions for industries with similar challenges. Optimization of the refinery’s overall production process is achieved by implementing models for each of the three distinguished subsystems: oil blending model, plant scheduling model, and the mixed crude oil refining model. The demand dictates the final production quantities. From those figures we are able to place constraining limits on the input crude oil. The refined oil production scheme is continuously enhanced by determining the amount of constituent feed on the production equipment according to the results of previous production cycle. After optimization, the minimum surplus inventory of the five oil components approach their lower limits that were calculated using our models. We compare the literature on scheduling optimization challenges both in China and abroad while providing a detailed discussion of the present situation of Chinese refineries. The interrelationships of production processes on each other are revealed by analyzing the system and breaking it down to three fundamental parts. Basing the final production predictions on the downstream demand, we are able to achieve a minimum refinery surplus inventory by utilizing a comprehensive refinery scheduling model composed of three sub-models.
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Kulkarni, Sukrut Shridhar, Marliana Bt Mohammad, Sharifah Nooraini Bt Syed Tahir, Frankie Kia Yong Tan, and Masnizah Bt Supu. "Situational Analysis of Complex Offshore Network for Strategizing Sequence for Green Field Development." In SPE Trinidad and Tobago Section Energy Resources Conference. SPE, 2021. http://dx.doi.org/10.2118/200933-ms.

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Abstract This paper establishes the approach to strategize the appropriate sequencing and monetization of the green field development through performing situational analysis for the complex offshore facilities to recognize new hydrocarbon molecules. As prudent operator for the complex network its crucial to pursue strategic ideas and innovative concepts to optimize supply demand balance, fulfill contractual obligations to optimize resources to maximize value creation, whilst protecting investment decisions for hydrocarbon monetization for the green field development. It is therefore necessary to implement successful business plans with appropriate sequencing of new fields by robust assessment to decipher the pain points to achieve optimal solution by gaining better understanding of network characteristic, supply distribution and operating envelope for line ups of new green field development. Situational Analysis for the complex offshore system is defined as robust investigation of the surrounding facts/realities to scrutinize the unique features in terms of capabilities, risks, uncertainty, opportunity, and exposures. The approach followed in the paper is the creation of mathematical model for the network/infrastructure embedded with business rules and deployment for evaluation and optimization. This approach is to timely deliver the management decisions for developing sequencing strategy, establishing priority of supply guidelines and allocation principles. This paper describes that a state of art approach which was followed by developing end to end network model by simulation engineers with close collaboration with strategic planning, portfolio optimization and including operations in single platform. The simulation model was further validated and deployed to analyze current network impediments in terms of technical and commercial allocation principles. The modelling approach was kept straightforward and scalable to allow for the future development if any. Analytics of the modelling could assist in gauging the potentials for enhancing system capacity by implementing appropriate reforms to optimize evacuation strategies. Obstacles across system architecture could be estimated and its reconfiguration was planned by means of variations in operating philosophy, alterations in the network assembly with appropriate debottlenecking recommendation. The allocation principles applied during business plans consider the commercial element on initial basis, before instead the physical and technical constraints were evaluated. The results of the allocation were then simulated and reallocated back to relevant demand center with relevant technical constraints of the network. This enabled team to identify the gap for supply/demand and propose solution to address the gap at an enterprise level to be substantial, to build a case whereby monetization of green fields will be necessary. Above methodology describes how by developing an end to end mathematical model that summarizes the microscopic details of a complex offshore system to facilitate on the way to analyze and strategize new field development line-ups. The novelty is with the simulation model built in a single platform, allows a seamless data transfer from various elements such as fields, facilities, pipelines, and terminal and is one stop solution for accessing impediments across architecture The above approach elaborates on result matter approach that steer and advocate on the situational analysis for new field sequencing by ascertaining CAPEX /OPEX optimization that could steer decision of lining up of four new fields within span of two years at appropriate intensity of the network with optimal monetization.
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Kubek, Daniel. "The impact of short term traffic forecasting on the effectiveness of vehicles routes planning in urban areas." In CIT2016. Congreso de Ingeniería del Transporte. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.3512.

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An impossibility to foresee in advance the accurate traffic parameters in face of dynamism phenomena in complex transportation system is a one of the major source of uncertainty. The paper presents an approach to robust optimization of logistics vehicle routes in urban areas on the basis of estimated short-term traffic time forecasts in a selected area of the urban road network. The forecast values of optimization parameters have been determined using the spectral analysis model, taking into account the forecast uncertainty degree. The robust counterparts approach of uncertain bi-criteria shortest path problem formulation is used to determining the robust routes for logistics vehicles in the urban network. The uncertainty set is created on the basis of forecast travel times in chosen sections, estimated by means of spectral analysis. The advantages and the characteristics are exemplified in the actual Krakow road network. The obtained data have been compared with classic approach wherein it is assumed that the optimization parameters are certain and accurate. The results obtained in the simulation example indicate that use of forecasting techniques with robust optimization models has a positive impact on the quality of final solutions.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3512
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