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Journal articles on the topic 'Transportation System Uncertainty'
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1
Krygina, I. E. "ORGANIZATION OF MULTIMODAL TRANSPORTATION UNDER UNCERTAINTY AND RISK." Business Strategies, no. 11 (November 23, 2019): 08–11. http://dx.doi.org/10.17747/2311-7184-2019-11-08-11.
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The article provides a terminological interpretation of the concept of multimodal transportation and the signs that determine it. We consider the features of the transport and logistics system of our country, dictating specific requirements for logistics schemes for delivering goods. Also considered is the concept of risk, its types and factors as applied to multimodal transport. In conclusion, a unified procedure for risk management in logistics systems is formulated.
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Tao, Xianding, and Paul Schonfeld. "Lagrangian Relaxation Heuristic for Selecting Interdependent Transportation Projects under Cost Uncertainty." Transportation Research Record: Journal of the Transportation Research Board 1931, no. 1 (January 2005): 74–80. http://dx.doi.org/10.1177/0361198105193100110.
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The capital budgeting process for a transportation system is usually complicated by the interdependencies of projects and the uncertainty of project costs. The existence of project interdependence in transportation systems makes it difficult to evaluate the project effects with analytical methods. Furthermore, when the construction costs of candidate projects are uncertain, the budget constraints that bind project selection become chance constraints, and this may render most existing approaches inapplicable. This paper formulates the project selection problem as a nonlinear integer optimization problem whose objective function is implicit but can be evaluated with network simulation. The Lagrangian method is applied to relax the complex project constraints that are nonlinear under cost uncertainty. An efficient genetic algorithm is developed to solve the Lagrangian subproblems. This paper applies an equilibrium traffic assignment model to evaluate the project impacts and the objective values of the Lagrangian subproblems. Experiments are designed to test the performance of the developed approach on a fairly generic highway system. The experiment results show that the developed approach can effectively solve the problem of selecting interdependent projects under cost uncertainty.
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Maksakova, Darya. "Gas transportation system development in Mongolia under gas prices uncertainty." E3S Web of Conferences 289 (2021): 04006. http://dx.doi.org/10.1051/e3sconf/202128904006.
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The paper analyses the stability of a solution to a problem of gas transportation system development in terms of gas import prices. The object of the study is a future gas transportation system in Mongolia. The employed tools are based on an original optimization problem, which is aimed to support decision-making process when choosing capacity, location, and time for investments in gas infrastructure. Different scenarios of gas import prices are considered for Mongolia. A stable solution is defined as the solution that is included in the optimal plans for every scenario. A multi-criteria approach is proposed to expanding the area of stable solutions. In conclusion, the priority areas of gas transportation system development in Mongolia are highlighted.
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Sevcikova, Hana, and Brice Nichols. "Land use uncertainty in transportation forecast." Journal of Transport and Land Use 14, no. 1 (July 26, 2021): 805–20. http://dx.doi.org/10.5198/jtlu.2021.1853.
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Using an integrated land use and travel model system implemented for the Puget Sound region in Washington state, a Bayesian Melding technique is applied to represent variations in land use outcomes, and is propagated into travel choices across a multi-year agent-based simulation. A scenario is considered where zoned capacity is increased around light rail stations. Samples are drawn from the posterior distribution of households to generate travel model inputs. They allow for propagation of land use uncertainty into travel choices, which are themselves assessed for uncertainty by comparing against observed data. Resulting travel measures of zonal vehicle miles traveled (VMT) per capita and light rail station boardings indicate the importance of comparing distributions rather than point forecasts. Results suggest decreased VMT per capita in zones near light rail stations and increased boardings at certain stations with existing development, and less significant impacts around stations with lower initial development capacity. In many cases, individual point level comparisons of scenarios would lead to very different conclusions. Altogether, this finding adds to a line of work demonstrating the policy value of incorporating uncertainty in integrated models and provides a method for assessing these variations in a systematic way.
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Zhao, Weixin, Hongyu Jiang, Kai Tang, Wenqi Pei, Yadong Wu, and Abdul Qayoom. "Knotted-line: A Visual explorer for uncertainty in transportation system." Journal of Computer Languages 53 (August 2019): 1–8. http://dx.doi.org/10.1016/j.cola.2019.01.001.
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Shrivastava, Himanshu, Andreas T. Ernst, and Mohan Krishnamoorthy. "Distribution and Inventory Planning in a Supply Chain Under Transportation Route Disruptions and Uncertain Demands." International Journal of Information Systems and Supply Chain Management 12, no. 3 (July 2019): 47–71. http://dx.doi.org/10.4018/ijisscm.2019070103.
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This article considers transportation disruptions and its detrimental impact on the quality of the enroute shipment. The authors consider a supply chain system of a short life cycle product that has a capacitated supplier, a retailer and multiple routes of transportation under different disruption risks, uncertain cost of transportation, and uncertain demands. The authors investigate a hybrid problem in which the firm needs to develop a suitable distribution strategy under disruption risks along with an optimal checking policy when faced with the supply of varying quantities of damaged items. The authors formulate a non-linear mathematical model in which the overall objective is to maximise the expected profit and to help the firm in decision making under uncertain environments. Lastly, a statistical study is carried out to perform uncertainty analysis.
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Raskin, Lev, Oksana Sira, and Viacheslav Karpenko. "TRANSPORTATION MANAGEMENT IN A DISTRIBUTED LOGISTIC CONSUMPTION SYSTEM UNDER UNCERTAINTY CONDITIONS." EUREKA: Physics and Engineering 4 (July 31, 2019): 82–90. http://dx.doi.org/10.21303/2461-4262.2019.00936.
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The problem of supply management in the supplier-to-consumer logistics transport system has been formed and solved. The novelty of the formulation of the problem consists in the integrated accounting of costs in the logistic system, which takes into account at the same time the cost of transporting products from suppliers to consumers, as well as the costs for each of the consumers to store the unsold product and losses due to possible shortages. The resulting optimization problem is no longer a standard linear programming problem. In addition, the work assumes that the solution of the problem should be sought taking into account the fact that the initial data of the problem are not deterministic. The analysis of traditional methods of describing the uncertainty of the source data. It is concluded that, given the rapidly changing conditions for the implementation of the delivery process in a distributed supplier-to-consumer system, it is advisable to move from a theoretical probability representation of the source data to their description in terms of fuzzy mathematics. At the same time, in particular, the fuzzy values of the demand for the delivered product for each consumer are determined by their membership functions. Distribution of supplies in the system is described by solving a mathematical programming problem with a nonlinear objective function and a set of linear constraints of the transport type. In forming the criterion, a technology is used to transform the membership functions of fuzzy parameters of the problem to its theoretical probabilistic counterparts – density distribution of demand values. The task is reduced to finding for each consumer the value of the ordered product, minimizing the average total cost of storing the unrealized product and losses from the deficit. The initial problem is reduced to solving a set of integral equations solved, in general, numerically. It is shown that in particular, important for practice, particular cases, this solution is achieved analytically. The paper states the insufficient adequacy of the traditionally used mathematical models for describing fuzzy parameters of the problem, in particular, the demand. Statistical processing of real data on demand shows that the parameters of the membership functions of the corresponding fuzzy numbers are themselves fuzzy numbers. Acceptable mathematical models of the corresponding fuzzy numbers are formulated in terms of bifuzzy mathematics. The relations describing the membership functions of the bifuzzy numbers are given. A formula is obtained for calculating the total losses to storage and from the deficit, taking into account the bifuzzy of demand. In this case, the initial task is reduced to finding the distribution of supplies, at which the maximum value of the total losses does not exceed the permissible value.
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Hong, Sungchul, and Alan P. Vonderohe. "Uncertainty Issues in Integrating Geographic Information Systems and the Global Positioning System for Transportation." Transportation Research Record: Journal of the Transportation Research Board 2215, no. 1 (January 2011): 50–58. http://dx.doi.org/10.3141/2215-05.
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Liu, Dong Jie, and Zhen Lin Wei. "Safety Assessment of Railway Dangerous Goods Transportation Based on Set Pair Analysis." Advanced Materials Research 869-870 (December 2013): 288–92. http://dx.doi.org/10.4028/www.scientific.net/amr.869-870.288.
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In order to overcome the drawbacks that occur when traditional safety assessment methods do not consider the certain and uncertain characteristics of information and in order to enhance the calculation efficiency, a new Identical Discrepancy Contrary (IDC) system is safety comprehensive assessment model based on Set Pair Analysis is established. Incorporating the Analytic Hierarchy Process (AHP) to obtain the index weight, this model analyses the uncertainties in the degree of connection acquired by assessment, and then makes the identical discrepancy contrary system research on the certainty and uncertainty of information in the engineering system.
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Ngossaha, J. Moskolaï, R. Houé Ngouna, B. Archimède, and J. M. Nlong. "Sustainability assessment of a transportation system under uncertainty: an integrated multicriteria approach." IFAC-PapersOnLine 50, no. 1 (July 2017): 7481–86. http://dx.doi.org/10.1016/j.ifacol.2017.08.1064.
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Saeed Osman, Mojahid. "Uncertainty modeling for bus selection and allocation in a private transportation system." Transportation Planning and Technology 42, no. 7 (August 3, 2019): 714–28. http://dx.doi.org/10.1080/03081060.2019.1650435.
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Fang, Li De, Xiu Ming Xiang, Xiao Ting Li, Li Li Pang, Xiao Jie Wang, and Ran Liu. "Study on Uncertainty of Gas Monitoring Dynamic Calibration System." Applied Mechanics and Materials 105-107 (September 2011): 1970–74. http://dx.doi.org/10.4028/www.scientific.net/amm.105-107.1970.
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With the rapid development of industry and transportation, air pollution is worsening, therefore, monitoring of air pollution components is more and more heeded. In this study, based on the measurement model and the composition of the air quality monitoring system on line, the mathematical model of the measurement system value transmission was analyzed, and the uncertainty components were calculated respectively, a conclusion that the main factor of the overall system uncertainty is the uncertainty of system itself in the existing air quality monitoring system was drawn, so measurement uncertainty of the calibration system was focused on the research. The effects of the Zero-gas, calibration gases and gas mass flow controller on the uncertainty of the calibration system was experimented and analyzed, and measurement uncertainty of the dynamic calibration system was evaluated.
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Nowakowski, Tomasz, and Sylwia Werbińska-Wojciechowska. "Uncertainty Problem in Decision Support System for Means of Transport Maintenance Processes Performance Development / Problem Niepewności W Systemie Wsparcia Decyzyjnego Procesów Utrzymania W Stanie Zdatności Środków Transportu." Journal of KONBiN 23, no. 1 (September 1, 2012): 173–92. http://dx.doi.org/10.2478/jok-2013-0047.
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Abstract In this paper, the authors’ research work is focused on uncertainty analysis implementation in the developed DSS for transportation means’ maintenance processes performance. Thus, in the Introduction section, the transportation means’ maintenance processes issues and the uncertainty problem are described. Later, there is briefly literature overview in the research area discussed. In the next Section, the expert system for means of transport maintenance processes performance is also investigated. Following this, the uncertainty analysis is developed and the examples of expert system implementation are given. The work ends up with summary and directions for further research.
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Du, Muqing, Xiaowei Jiang, Lin Cheng, and Changjiang Zheng. "Robust Evaluation for Transportation Network Capacity under Demand Uncertainty." Journal of Advanced Transportation 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9814909.
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As more and more cities in worldwide are facing the problems of traffic jam, governments have been concerned about how to design transportation networks with adequate capacity to accommodate travel demands. To evaluate the capacity of a transportation system, the prescribed origin and destination (O-D) matrix for existing travel demand has been noticed to have a significant effect on the results of network capacity models. However, the exact data of the existing O-D demand are usually hard to be obtained in practice. Considering the fluctuation of the real travel demand in transportation networks, the existing travel demand is represented as uncertain parameters which are defined within a bounded set. Thus, a robust reserve network capacity (RRNC) model using min–max optimization is formulated based on the demand uncertainty. An effective heuristic approach utilizing cutting plane method and sensitivity analysis is proposed for the solution of the RRNC problem. Computational experiments and simulations are implemented to demonstrate the validity and performance of the proposed robust model. According to simulation experiments, it is showed that the link flow pattern from the robust solutions to network capacity problems can reveal the probability of high congestion for each link.
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Dai, Qian, and Jiaqi Yang. "A Distributionally Robust Chance-Constrained Approach for Modeling Demand Uncertainty in Green Port-Hinterland Transportation Network Optimization." Symmetry 12, no. 9 (September 10, 2020): 1492. http://dx.doi.org/10.3390/sym12091492.
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This paper discusses a bi-objective programming of the port-hinterland freight transportation system based on intermodal transportation with the consideration of uncertain transportation demand for green concern. Economic and environmental aspects are integrated in order to obtain green flow distribution solutions for the proposed port-hinterland network. A distributionally robust chance constraint optimization model is then established for the uncertainty of transportation demand, in which the chance constraint is described such that transportation demand is satisfied under the worst-case distribution based on the partial information of the mean and variance. The trade-offs among different objectives and the uncertainty theory applied in the modeling both involve the notion of symmetry. Taking the actual port-hinterland transportation network of the Yangtze River Economic Belt as an example, the results reveal that the railway-road intermodal transport is promoted and the change in total network CO2 emissions is contrary to that in total network costs. Additionally, both network costs and network emissions increase significantly with the growth of the lower bound of probability for chance constraint. The higher the probability level grows, the greater the trade-offs between two objectives are influenced, which indicates that the operation capacity of inland intermodal terminals should be increased to meet the high probability level. These findings can help provide decision supports for the green development strategy of the port-hinterland container transportation network, which meanwhile faces a dynamic planning problem caused by stochastic demands in real life.
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Singh, Prerna, Baabak Ashuri, and Adjo Amekudzi-Kennedy. "Application of Dynamic Adaptive Planning and Risk-Adjusted Decision Trees to Capture the Value of Flexibility in Resilience and Transportation Planning." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (July 13, 2020): 298–310. http://dx.doi.org/10.1177/0361198120929012.
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Transportation infrastructure around the world is under pressure to perform with ever-changing climate scenarios, unpredictable disasters, and stress on resources stemming from rapid urbanization and population growth. Current approaches to developing resilience applied to the transportation system focus primarily on engineering resilience and do not explicitly deal with deep uncertainties arising from climate change. This paper reviews adaptation, a critical aspect of a resilient system in an uncertain and changing environment, as applied in the transportation resilience literature. It compares and contrasts the status of adaptive resiliency in transportation with that in other fields to highlight gaps and research opportunities. The paper then presents Dynamic Adaptive Planning (DAP) as a method for dealing more effectively with deep uncertainty in decision making and offers an approach that combines economic analysis with DAP to enhance decision making under external uncertainties, such as natural disasters, with financial constraints. It presents a case study of the San Francisco–Oakland Bridge to demonstrate the economic benefits of DAP. This paper provides transportation practitioners with guidance on the application of DAP and insight into the economic benefits of such an approach to decision making in various settings including emergency response planning, long-range planning, maintenance and renewal planning, and operations planning. The paper also identifies areas for possible future research combining financial theory with DAP as important in developing more robust decision-making frameworks for handling deep uncertainty.
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Sun and Li. "Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncertainty." Mathematics 7, no. 8 (August 12, 2019): 739. http://dx.doi.org/10.3390/math7080739.
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In this study, we systematically investigate a road-rail intermodal routing problem the optimization of which is oriented on the customer demands on transportation economy, timeliness and reliability. The road-rail intermodal transportation system is modelled as a hub-and-spoke network that contains time-flexible container truck services and scheduled container train services. The transportation timeliness is optimized by using fuzzy soft time windows associated with the service level of the transportation. Reliability is enhanced by considering multiple sources of time uncertainty, including road travel time and loading/unloading time. Such uncertainty is modelled by using fuzzy set theory. Triangular fuzzy numbers are adopted to represent the uncertain time. Under the above consideration, we first establish a fuzzy mixed integer nonlinear programming model with a weighted objective that includes minimizing the costs and maximizing the service level for accomplishing transportation orders. Then we use the fuzzy expected value model and fuzzy chance-constrained programming separately to realize the defuzzification of the fuzzy objective and use fuzzy chance-constrained programming to deal with the fuzzy constraint. After defuzzification and linearization, an equivalent mixed integer linear programming (MILP) model is generated to enable the problem to be solved by mathematical programming software. Finally, a numerical case modified from our previous study is presented to demonstrate the feasibility of the proposed fuzzy programming approaches. Sensitivity analysis and fuzzy simulation are comprehensively utilized to discuss the effects of the fuzzy soft time windows and time uncertainty on the routing optimization and help decision makers to better design a crisp transportation plan that can effectively make tradeoffs among economy, timeliness and reliability.
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Lin, Xi, Yafeng Yin, and Fang He. "Credit-Based Mobility Management Considering Travelers’ Budgeting Behaviors Under Uncertainty." Transportation Science 55, no. 2 (March 2021): 297–314. http://dx.doi.org/10.1287/trsc.2020.1014.
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This study analyzes the performance of a credit-based mobility management scheme considering travelers’ budgeting behaviors for credit consumption under uncertainty. In the scheme, government agencies periodically distribute a certain number of credits to travelers; travelers must pay a credit charge for driving to complete their trips. Otherwise, they can take public transit free of credit charge. Consequently, within a credit-releasing cycle, travelers must budget their credit consumption to fulfill their mobility needs. Such budgeting behaviors can be viewed as a multistage decision-making process under uncertainty. Considering a transportation system with a credit scheme, we propose parsimonious models to investigate how the uncertainty associated with individual mobility needs and the subsequent travelers’ credit-budgeting behavior influence the multistage equilibrium of the transportation system, as well as the performance of the credit scheme on managing the transportation system. Both analytical and numerical results suggest that travelers tend to restrict their credit consumption in the early stage of a credit-releasing cycle to hedge against the risks associated with using up all credits, which compromises the performances of credit-based schemes. Moreover, a negative attitude toward risk aggravates the discrepancy between the credit consumption of the early and late stages. Last, we propose a contingency credit scheme to mitigate the negative impact incurred by travelers’ budgeting behaviors.
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Lee, Joosung J., Ian A. Waitz, Brian Y. Kim, Gregg G. Fleming, Lourdes Maurice, and Curtis A. Holsclaw. "System for assessing Aviation’s Global Emissions (SAGE), Part 2: Uncertainty assessment." Transportation Research Part D: Transport and Environment 12, no. 6 (August 2007): 381–95. http://dx.doi.org/10.1016/j.trd.2007.03.006.
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Marchal, Fabrice, and André de Palma. "Measurement of Uncertainty Costs with Dynamic Traffic Simulations." Transportation Research Record: Journal of the Transportation Research Board 2085, no. 1 (January 2008): 67–75. http://dx.doi.org/10.3141/2085-08.
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Nonrecurrent congestion in transportation networks occurs as a consequence of stochastic factors affecting demand and supply. Intelligent transportation systems such as advanced traveler information systems and advanced traffic management systems are designed to reduce the impacts of nonrecurrent congestion by providing information to a fraction of users or by controlling the variability of traffic flows. For these reasons, the design of these systems requires a reliable forecast of nonrecurrent congestion. A new method is proposed to measure the impacts of nonrecurrent congestion on travel costs by taking risk aversion into account. The traffic model is based on the dynamic traffic simulation model METROPOLIS. Incidents are generated randomly by reducing the capacity of the network. Users can instantaneously adapt to the unexpected travel conditions or can also change their behavior through a day-to-day adjustment process. Comparisons with incident-free simulations provide a benchmark for potential travel time savings that can be brought about by a state-of-the-art information system. The impact of variable travel conditions is measured by describing the willingness to pay to avoid risky or unreliable journeys. Indeed, for risk-averse drivers, any uncertainty corresponds to a utility loss. This utility loss is computed for several levels of network disruption. The main result of the study is that the utility loss due to uncertainty is of the same order of magnitude as the total travel costs.
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Teodorovic, Dusan, Jovan Popovic, and Panta Lucic. "Transport modeling: An artificial immune system approach." Yugoslav Journal of Operations Research 16, no. 1 (2006): 3–30. http://dx.doi.org/10.2298/yjor0601003t.
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This paper describes an artificial immune system approach (AIS) to modeling time-dependent (dynamic, real time) transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions) that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies) for different antigens (different traffic "scenarios"). This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.
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Lukinskiy, Valery, Vladislav Lukinskiy, and Yuri Merkuryev. "MODELLING OF TRANSPORT OPERATIONS IN SUPPLY CHAINS IN OBEDIENCE TO “JUST-IN-TIME” CONCEPTION." Transport 33, no. 5 (December 18, 2018): 1162–72. http://dx.doi.org/10.3846/transport.2018.7112.
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Transportation is a key logistics function, which determines the dynamic nature of material flows in logistics systems. At the same time, transportation is a source of uncertainty of logistics operations performance in the supply chain. Obviously, the development of a new approach for evaluation of the duration of delivery “Just-In-Time” (JIT) will improve the efficiency of supply chains in accordance with one of the major criteria, namely customer satisfaction. One of the basic approaches to make effective management decisions in transportation and other logistic operations is the JIT concept. In the majority of examined sources the JIT concept is described on the verbal level without any usage of calculation dependences. The paper is devoted to the formation of analytical and simulation models, which allow obtaining the probabilistic evaluation of the implementation of unimodal and multimodal international transportation JIT. The first model where the order of the operations implementation does not affect final result is formed on the basis of the probability theory: distribution laws composition, theorems of numerical characteristics of random variables, formula of complete probability. The second model accounts the impact of operations implementation order in transportation and their interconnection and is based on the simulation (the method of statistic experiments) and shown as a corresponding algorithm, which allows to consider different limitations (technical, organizational and so on). Considered analytical dependences give the possibility to obtain the necessary estimations of the transport operations implementation according to JIT: mean transportation time, delivery implementation probability by the set moment or the delivery time with the set probability. To carry out some comparative calculations and clarify the algorithm, two international routes have been chosen: the first one is a unimodal road transportation, the second one is a multimodal transportation (road and marine transport). All the data, which is necessary for calculation has been collected on the basis of official information (in particular, the data of tachograph, special questionnaires filled in by the drivers, the survey results of the managers). For unimodal transportations analytical dependences and modelling results give close results. For the combined multimodal transportations taking into account various limitations the preference must be given to the simulation. The modelled indexes take into consideration their intercommunication and definitely estimate the supply chains reliability, and this allows decreasing the uncertainty of the logistic system.
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Lv, Pin, and Cheng Zheng Ma. "The Choice of Different Logistics Policies Considering Transportation and Inventory Costs in a Two-Echelon Distribution System." Applied Mechanics and Materials 505-506 (January 2014): 902–6. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.902.
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This paper presented a formal analysis for the alternative of logistics centralization and decentralization policy in a two-echelon distribution system. First, functions of transportation and inventory costs under different policies in the system were developed. Second, the differences between transportation and inventory costs were compared, and the relationship between transportation and inventory cost difference was analyzed through a balance ratio. At last, some indicators were derived to measure the level of demand uncertainty, the amount of retailers and the size of customer orders. It shows that the optimal logistics policy can be determined as a tradeoff between transportation and inventory costs. And the indicators can provide a basis for selecting the optimal logistics policy.
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Roy, Jagannath, Saibal Majumder, Samarjit Kar, and Krishnendu Adhikary. "A Multiobjective Multi-Product Solid Transportation Model with Rough Fuzzy Coefficients." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 05 (October 2019): 719–53. http://dx.doi.org/10.1142/s0218488519500326.
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Transportation management is one of the key success factors to keep an organization competitive, sustain its growth pace, and raise profits not only at a local but also a global scale. Therefore, planning and designing a transport system are prerequisite and vital topics for achieving these goals. In this paper, a multiobjective multi-product solid transportation problem (MOMPSTP) under uncertainty is formulated and solved by two different methods of multiobjective optimization problems (MOPs). The system parameters namely unit transportation cost, availability of products at source points, demands of products at destinations and the capacity of transportation mode all are taken as rough fuzzy variables (RFVs). A chance constraint programming model for MOP with RFVs is developed in order to obtain satisfactory solutions when decision makers (DMs) aim to optimize multiple objectives (cost, time, profit, etc.) simultaneously. For given credibility (Cr) and trust (Tr) levels of RFVs, Cr-Tr constraint programming technique is used to reduce the uncertain transportation problem into equivalent deterministic form. Two classical solution techniques-weighted sum method (WSM) and ideal point method (IPM) are utilized to solve the problem. Finally, a numerical example is provided to illustrate the usefulness of our proposed model and then a sensitivity analysis is performed to verify different solutions due to different level of satisfaction.
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Tak, Hye-Young, Wonho Suh, and Young-Joo Lee. "System-Level Seismic Risk Assessment of Bridge Transportation Networks Employing Probabilistic Seismic Hazard Analysis." Mathematical Problems in Engineering 2019 (August 25, 2019): 1–17. http://dx.doi.org/10.1155/2019/6503616.
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Earthquakes can have significant impacts on transportation networks because of the physical damage they can cause to bridges. Hence, it is essential to assess the seismic risk of a bridge transportation network accurately. However, this is a challenging task because it requires estimating the performance of a bridge transportation network at the system level. Moreover, it is necessary to deal with various possible earthquake scenarios and the associated damage states of component bridges considering the uncertainty of earthquake locations and magnitudes. To overcome these challenges, this study proposes a new method of system-level seismic risk assessment for bridge transportation networks employing probabilistic seismic hazard analysis (PSHA). The proposed method consists of three steps: (1) seismic fragility estimation of the bridges based on PSHA; (2) system-level performance estimation using a matrix-based framework; and (3) seismic risk assessment based on the total probability theorem. In the proposed method, PSHA enables the seismic fragility estimation of the component bridges considering the uncertainty of earthquake locations and magnitudes, and it is systemically used to carry out a posthazard bridge network flow capacity analysis by employing the matrix-based framework. The proposed method provides statistical moments of the network performance and component importance measures, which can be used by decision makers to reduce the seismic risk of a target area. To test the proposed method, it is applied to a numerical example of an actual transportation network in South Korea. In the seismic risk assessment of the example, PSHA is successfully integrated with the matrix-based framework to perform system reliability analysis in a computationally efficient manner.
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Bertolini, Luca. "Evolutionary Urban Transportation Planning: An Exploration." Environment and Planning A: Economy and Space 39, no. 8 (August 2007): 1998–2019. http://dx.doi.org/10.1068/a38350.
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For urban transportation planners these are challenging times. Mounting practical concerns are mirrored by more fundamental critiques. The latter comes together in the observation that conventional approaches do not adequately account for the irreducible uncertainty of future developments. The author's central aim is to explore whether and how an evolutionary approach can help overcome this limit. Two core hypotheses are formulated. The first is that the urban transportation system behaves in an evolutionary fashion. The second hypothesis is that, because of this, urban transportation planning needs to focus on enhancing the resilience and adaptability of the system. Changes in transport and land-use development patterns and policies, and in the broader context of the postwar period in the Amsterdam region, are analysed in order to illustrate the two core hypotheses. More general implications are also drawn.
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Koutsoukis, Nikitas‐Spiros, Belen Dominguez‐Ballesteros, Cormac A. Lucas, and Gautam Mitra. "A prototype decision support system for strategic planning under uncertainty." International Journal of Physical Distribution & Logistics Management 30, no. 7/8 (September 2000): 640–61. http://dx.doi.org/10.1108/09600030010346387.
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Cheng, Xiaoqing, Yuejian Chen, Zongyi Xing, Yifan Li, and Yong Qin. "A Novel Online Detection System for Wheelset Size in Railway Transportation." Journal of Sensors 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/9507213.
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The online detection of wheelset size has important implications for ensuring the safety of railway operation and decreasing the maintenance costs. Based on laser displacement sensors (LDS), a novel online detection system of the wheel size is proposed using only six two-dimensional LDS and two one-dimensional LDS. The calculation principles of tread profile and wheel diameter are given, as well as the calibration method. Errors induced by wheel-rail vibration, misalignment, sensor noise, S-shape running, and wheelset differential are also analyzed. After system implementation, field experiments were performed using both standard wheel and several real trains. It turns out that the detection uncertainty of flange width and height is 0.1 mm and wheel diameter 0.3 mm, which can meet the requirements of maintenance.
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Botea, Adi. "Hedging the Risk of Delays in Multimodal Journey Planning." AI Magazine 37, no. 4 (January 17, 2017): 105–6. http://dx.doi.org/10.1609/aimag.v37i4.2676.
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Traditional multimodal journey planners are deterministic. However, uncertainty in a transportation network can often lead to missed connections, causing delays and hurting the reliability level of the service. We overview what we believe to be the first multimodal journey advising system that is capable to reason under uncertainty, providing more reliable journey plans.
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Iannelli, Andrea, Mark Lowenberg, and Andrés Marcos. "An extension of the structured singular value to nonlinear systems with application to robust flutter analysis." CEAS Aeronautical Journal 11, no. 4 (September 9, 2020): 1057–69. http://dx.doi.org/10.1007/s13272-020-00469-4.
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Abstract The paper discusses an extension of $$\mu$$ μ (or structured singular value), a well-established technique from robust control for the study of linear systems subject to structured uncertainty, to nonlinear polynomial problems. Robustness is a multifaceted concept in the nonlinear context, and in this work the point of view of bifurcation theory is assumed. The latter is concerned with the study of qualitative changes of the steady-state solutions of a nonlinear system, so-called bifurcations. The practical goal motivating the work is to assess the effect of modeling uncertainties on flutter, a dynamic instability prompted by an adverse coupling between aerodynamic, elastic, and inertial forces, when considering the system as nonlinear. Specifically, the onset of flutter in nonlinear systems is generally associated with limit cycle oscillations emanating from a Hopf bifurcation point. Leveraging $$\mu$$ μ and its complementary modeling paradigm, namely linear fractional transformation, this work proposes an approach to compute margins to the occurrence of Hopf bifurcations for uncertain nonlinear systems. An application to the typical section case study with linear unsteady aerodynamic and hardening nonlinearities in the structural parameters will be presented to demonstrate the applicability of the approach.
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Li, Zhi-Chun, Zhao-Kun Li, and William H. K. Lam. "An integrated design of sustainable land use and transportation system with uncertainty in future population." Transportmetrica A: Transport Science 10, no. 2 (October 2, 2012): 160–85. http://dx.doi.org/10.1080/18128602.2012.725268.
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Raskin, Lev, Oksana Sira, Yurii Parfeniuk, and Kseniia Bazilevych. "DEVELOPMENT OF METHODS FOR SUPPLY MANAGEMENT IN TRANSPORTATION NETWORKS UNDER CONDITIONS OF UNCERTAINTY OF TRANSPORTATION COST VALUES." EUREKA: Physics and Engineering, no. 2 (March 29, 2021): 108–23. http://dx.doi.org/10.21303/2461-4262.2021.001691.
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The problem of transport management in a distributed logistics system "suppliers – consumers" is considered. Under the assumption of a random nature of transportation costs, an exact algorithm for solving this problem by a probabilistic criterion has been developed. This algorithm is implemented by an iterative procedure for sequential improvement of the transportation plan. The rate of convergence of a computational procedure to an exact solution depends significantly on the dimension of the problem and is unacceptably low in real problems. In this regard, an alternative method is proposed, based on reducing the original problem to solving a nontrivial problem of fractional-nonlinear programming. A method for solving this problem has been developed and substantiated. The corresponding computational algorithm reduces the fractional-nonlinear model to the quadratic one. The resulting problem is solved by known methods. Further, the original problem is supplemented by considering a situation that is important for practice, when in the conditions of a small sample of initial data there is no possibility of obtaining adequate analytical descriptions for the distribution densities of the random costs of transportation. In this case, the available volume of statistical material is sufficient only to estimate the first two moments of unknown distribution densities. For this marginal case, a minimax method for finding the transportation plan is proposed. The first step is to solve the problem of determining the worst distribution density with the given values of the first two moments. In the second step, the transportation plan is found, which is the best in this most unfavorable situation, when the distribution densities of the random cost of transportation are the worst. To find such densities, let’s use the modern mathematical apparatus of continuous linear programming
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Noreiga, Quentin, and Mark McDonald. "Parsimonious Modeling and Uncertainty Quantification for Transportation Systems Planning Applied to California High-Speed Rail." Transportation Research Record: Journal of the Transportation Research Board 2266, no. 1 (January 2012): 49–59. http://dx.doi.org/10.3141/2266-06.
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This paper presents a parsimonious travel demand model (PTDM) derived from a proprietary parent travel demand model developed by Cambridge Systematics (CS) for the California high-speed rail system. The purpose of the PTDM is to reduce computational expense for model simulations, optimization and sensitivity analyses, and other repetitive analyses. The PTDM is used to quantify the significance of parameter uncertainties with the use of mean value first-order second moment methods for uncertainty quantification and sensitivity analysis. The PTDM changes the model resolution of the parent travel demand model from a traffic analysis zone to a county-level analysis. The three-step model contains trip frequency, destination choice, and main mode choice models and is calibrated to match the results of the CS model. The main mode choice model predicts primary mode choice results for car, commercial air, conventional rail, and high-speed rail. The PTDM uses data and models similar to parent models to show how uncertainty in travel demand model predictions can be quantified. This paper does not attempt to assess the reliability of parent model forecasts, and the results should not be used to evaluate uncertainty in the California High-Speed Rail Authority's rider ship and revenue forecasts. However, the uncertainty quantification methodology presented here, when applied to the CS model, can be used to quantify the impact of parameter uncertainty on the forecast results.
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Raskin, Lev, Yurii Parfeniuk, Larysa Sukhomlyn, Mykhailo Kravtsov, and Leonid Surkov. "A METHOD FOR SOLVING THE CANONICAL PROBLEM OF TRANSPORT LOGISTICS IN CONDITIONS OF UNCERTAINTY." Innovative Technologies and Scientific Solutions for Industries, no. 2 (16) (July 6, 2021): 80–88. http://dx.doi.org/10.30837/itssi.2021.16.080.
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Subject.The canonical task of transport logistics in the distributed system "suppliers - consumers" is considered. Goal. Development of an accurate algorithm for solving this problem according to the probabilistic criterion in the assumption of the random nature of transportation costs has been done. Tasks. 1. Development of an exact method for solving the problem of finding a plan that minimizes the total cost of transportation in conditions when their costs are given by their distribution densities. 2. Development of a method for solving the problem when the distribution density of the cost of transportation is not known. Methods. A computational scheme for solving the problem is proposed, which is implemented by an iterative procedure for sequential improvement of the transportation plan. The convergence of this procedure is proved. In order to accelerate the convergence of the computational procedure to the exact solution, an alternative method is proposed based on the solution of a nontrivial problem of fractional nonlinear programming. The method reduces the original complex problem to solving a sequence of simpler problems. The original problem is supplemented by considering a situation that is important for practice when, in the conditions of a small sample of initial data, there is no possibility of obtaining adequate analytical descriptions for the distribution densities of the random costs of transportation. To solve the problem in this case, a minimax method is proposed for finding the best transportation plan in the most unfavorable situation, when the distribution densities of the random cost of transportation are the worst. To find such densities, the modern mathematical apparatus of continuous linear programming was used. Results. A mathematical model and a method for solving the problem of transport logistics in conditions of uncertainty of the initial data are proposed. The desired plan is achieved using the solution of the fractional nonlinear programming problem. Conclusions: The problem of forming a transportation plan is considered, provided that their costs are random values. Also, a method for solving the problem of optimization of transportation for a situation where the density of distribution of random cost cannot be correctly determined is considered.
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Lee, Renee, and Anne S. Kiremidjian. "Uncertainty and Correlation for Loss Assessment of Spatially Distributed Systems." Earthquake Spectra 23, no. 4 (November 2007): 753–70. http://dx.doi.org/10.1193/1.2791001.
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Seismic risk assessment for a spatially distributed system, such as a lifeline network, involves characterization of ground shaking and structural damage for multiple structures in a region. The expected value of monetary loss, a common measure of the risk, has been previously formulated but with little attention to the uncertainty around this monetary loss. Furthermore, prior research on risk assessment for lifeline systems, in particular transportation networks, assumes no spatial ground motion correlation and no structure-to-structure damage correlation between sites in the network. In this paper, a framework for treating these correlations in the network risk analysis is presented. A demonstration of this methodology is carried out for two transportation networks located in the San Francisco Bay region. Coefficients of variation for network physical loss using a non–distance dependent ground motion correlation model in the framework range between 0.6 and 1.5 for the sample networks presented here. Coefficients of variation for network physical loss using a distance-dependent ground motion correlation model in the framework range between 1.0 and 1.4 for the same networks. It is demonstrated through these applications that assuming no correlation in ground motion and in damage may potentially underestimate uncertainty in the overall loss estimation.
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Xie, Ying, Xingzhi Wang, and Fangrui Mai. "Calculation of theoretical transmission loss in trunk gas pipeline." Advances in Mechanical Engineering 11, no. 12 (December 2019): 168781401989544. http://dx.doi.org/10.1177/1687814019895440.
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Owing to the limited accuracy and measurement uncertainty of the instruments installed in gas pipeline systems, it is impossible to completely avoid transmission loss during transportation of natural gas. This study established an uncertainty model for the measurement system determining the complex flow rates of trunk gas pipelines, analyzed the uncertainty calculation methods of different metering systems, and developed a calculation method for determining the theoretical transmission loss. Application showed that the theoretical transmission loss serves not only as an early warning regarding a transmission loss, but also as a guide to the pipeline enterprise for determining the transmission loss assessment index. If the actual transmission loss rate is smaller than the theoretical transmission loss rate during the calculation cycle, it means that the pipeline metering system is working normally. Otherwise, it is necessary to immediately investigate the reason behind the transmission loss and implement corresponding measures.
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Zhang, Hong Liang. "Urban Vehicle Emissions Management under Uncertainty - A Traffic Planning Model with Interval-Parameter Programming." Advanced Materials Research 864-867 (December 2013): 1586–91. http://dx.doi.org/10.4028/www.scientific.net/amr.864-867.1586.
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In this study, an interval-parameter programming method has been used for urban vehicle emissions management under uncertainty. The model improves upon the existing optimization methods with advantages in uncertainty reflection, system costs and limitation of emission. Moreover, the model is applied to a case study of urban vehicle emissions management in a virtual city. The results indicate that the interval linear traffic planning model can effectively reduce the vehicles emission and provide strategies for authorities to deal with problems of transportation system.
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Liu, Yang, Jing Shi, and Meiying Jian. "Understanding Visitors’ Responses to Intelligent Transportation System in a Tourist City with a Mixed Ranked Logit Model." Journal of Advanced Transportation 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8652053.
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One important function of Intelligent Transportation System (ITS) applied in tourist cities is to improve visitors’ mobility by releasing real-time transportation information and then shifting tourists from individual vehicles to intelligent public transit. The objective of this research is to quantify visitors’ psychological and behavioral responses to tourism-related ITS. Designed with a Mixed Ranked Logit Model (MRLM) with random coefficients that was capable of evaluating potential effects from information uncertainty and other relevant factors on tourists’ transport choices, an on-site and a subsequent web-based stated preference survey were conducted in a representative tourist city (Chengde, China). Simulated maximum-likelihood procedure was used to estimate random coefficients. Results indicate that tourists generally perceive longer travel time and longer wait time if real-time information is not available. ITS information is able to reduce tourists’ perceived uncertainty and stimulating transport modal shifts. This novel MRLM contributes a new derivation model to logit model family and for the first time proposes an applicable methodology to assess useful features of ITS for tourists.
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Miller-Hooks, Elise, and Baiyu Yang. "Impact of Travel Time Models on Quality of Real-Time Routing Instructions." Transportation Research Record: Journal of the Transportation Research Board 1857, no. 1 (January 2003): 21–29. http://dx.doi.org/10.3141/1857-03.
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Mobile communication systems coupled with intelligent transportation systems technologies can permit information service providers to supply real-time routing instructions to suitably equipped vehicles as real-time travel times are received. Simply considering current conditions in updating routing decisions, however, may lead to suboptimal path choices, because future travel conditions likely will differ from that currently observed. Even with perfect and continuously updated information about current conditions, future travel times can be known a priori with uncertainty at best. Further, in congested transportation systems, conditions vary over time as recurrent congestion may change with a foreseeable pattern during peak driving hours. It is postulated that better, more robust routing instructions can be provided by explicitly accounting for this inherent stochastic and dynamic nature of future travel conditions in generating the routing instructions. It is further hypothesized that nearly equally good routing instructions can be provided by collecting real-time information from only a small neighborhood within the transportation system as from the entire system. Extensive numerical experiments were conducted to assess the validity of these two hypotheses.
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Sudtachat, Kanchala. "Transportation and Production Lot-size for Sugarcane under Uncertainty of Machine Capacity." MATEC Web of Conferences 167 (2018): 02007. http://dx.doi.org/10.1051/matecconf/201816702007.
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The integrated transportation and production lot size problems is important effect to total cost of operation system for sugar factories. In this research, we formulate a mathematic model that combines these two problems as two stage stochastic programming model. In the first stage, we determine the lot size of transportation problem and allocate a fixed number of vehicles to transport sugarcane to the mill factory. Moreover, we consider an uncertainty of machine (mill) capacities. After machine (mill) capacities realized, in the second stage we determine the production lot size and make decision to hold units of sugarcane in front of mills based on discrete random variables of machine (mill) capacities. We investigate the model using a small size problem. The results show that the optimal solutions try to choose closest fields and lower holding cost per unit (at fields) to transport sugarcane to mill factory. We show the results of comparison of our model and the worst case model (full capacity). The results show that our model provides better efficiency than the results of the worst case model.
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Jouzdani, Javid, and Mohammad Fathian. "Hybrid Electromagnetism-Like Algorithm for Dynamic Supply Chain Network Design under Traffic Congestion and Uncertainty." Mathematical Problems in Engineering 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/2498616.
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With the constantly increasing pressure of the competitive environment, supply chain (SC) decision makers are forced to consider several aspects of business climate. More specifically, they should take into account the endogenous features (e.g., available means of transportation, and the variety of products) and exogenous criteria (e.g., the environmental uncertainty, and transportation system conditions). In this paper, a mixed integer nonlinear programming (MINLP) model for dynamic design of a supply chain network is proposed. In this model, multiple products and multiple transportation modes, the time value of money, traffic congestion, and both supply-side and demand-side uncertainties are considered. Due to the complexity of such models, conventional solution methods are not applicable; therefore, two hybrid Electromagnetism-Like Algorithms (EMA) are designed and discussed for tackling the problem. The numerical results show the applicability of the proposed model and the capabilities of the solution approaches to the MINLP problem.
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Yu, Ke, Yanni Yang, and Lang Fan. "Robust Speed Limits Scheme Design for Bimodal Transportation Systems." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/750694.
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In the context of travel demand uncertainty, this paper investigates how to determine the robust road speed limits for improving mobility and lowering vehicular emissions in bimodal transportation systems that involve private cars and subway. More specifically, the total demand vector is supposed to vary within a given set. Our target is to find the optimal road speed limits against the worst feasible demand scenario so as to minimize the sum social cost of system travel time and vehicular emissions. In order to estimate traffic emissions more reliably, motor vehicle emission simulator (MOVES) is utilized to simulate the emission factor function with respect to average speed. On these bases, we formulate the robust speed limits design problem as a “min-max” nonlinear model with complementarity constraints and solve it iteratively by a cutting-plane scheme that contains two sub-MPCCs. A numerical example is illustrated at the end.
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Czyzak, P., and J. Zak. "A model of an urban transportation system formulated as a multiobjective mathematical programming problem under uncertainty." Journal of Advanced Transportation 29, no. 1 (December 1995): 43–62. http://dx.doi.org/10.1002/atr.5670290107.
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Cottam, Bobby J. "Transportation Planning for Connected Autonomous Vehicles: How It All Fits Together." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 51 (March 29, 2018): 12–19. http://dx.doi.org/10.1177/0361198118756632.
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As connected and autonomous vehicle (CAV) technology continues to evolve and rapidly develop new capabilities, it is becoming increasingly important for transportation planners to consider the effects that these vehicles will have on the transportation network. It is evident that this trend has already started; over 60% of long-range transportation plans in the largest urban areas now include some discussion of CAVs, up from just 6% in 2015. There are also numerous CAV pilot programs currently underway that entail testing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction in both isolated and real-world environments. In this review of the current assessments for CAV impacts, two primary trends are identified. First, there is a great deal of uncertainty that is not being explicitly considered and properly accounted for in the transportation-network planning process. Second, the predictions that are being made are not considering potential policy or planning actions that could shape or affect the impacts of CAVs. This paper provides a picture of how ongoing CAV research interacts with current transportation planning practices by examining how the methods, the ranges of predictions, and the different sources of uncertainty in each method impact the planning process and potential system outcomes. Finally, it will identify best practices from decision analysis to help plan the best possible future transportation networks.
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Novizayanti, Dita, and Eko Agus Prasetio. "Orchestrating Innovation Network: Case of Sustainable Transportation Technology." International Journal of Sustainable Transportation Technology 4, no. 1 (April 30, 2021): 35–41. http://dx.doi.org/10.31427/ijstt.2021.4.1.5.
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Currently, the transportation sectors are heading into the advancement of sustainable transportation technology to tackle the issue of unsustainable transportation system which leads to various drawbacks in the future. One of the innovation in sustainable transportation technology is the development of electric vehicle (EV). However, there are noticeable difference between sustainable transportation system and conventional transportation system that requires a massive and synchronous network of related stakeholders to keep innovating and catch up with the progression. On the other side, transportation sector is one of the sector with the massive development that has the consequences of high risk and uncertainty, which leads to the tendency of innovation avoidance. Therefore, the innovation network of EV needs to be nurtured to ensure its sustainability. From the previous study on various sectors, the innovation strategy focuses on two matters: innovation target and actor integration. The latter purpose is required to be conducted in an inclusive manner by engaging people from inside or outside the organization. In the case of innovation in sustainable transportation, the vertical approach plays a significant role in terms of fostering technological advancement. The main solution to reach this objective is through the well-established research ecosystem, which significantly supported by R&D investment by the government. The government takes the role of the innovation strategy implementer to establish a strong research network. Besides as the R&D funder and the hub between researcher and industry, the government also take a role to keep the dynamics of an ecosystem through interaction stimulation, while also engage the innovation results to be relevant with the social, technological, environmental, and economic problems. Moreover, the research center also takes an important role in the ecosystem, such as to engage reiteratively with the industry, become the open-innovation enabler, have the dedicated research agenda, become the multidisciplinary innovation enabler, collaborate with multi-stakeholders, has core research infrastructure, and become the innovation cultural hub. While the country’s industrialization demand keep rising, the research center could consistently become the intermediaries in conducting the research and build the relationship with other non-industry actor. This way, the high risk and uncertainty of innovation could be reduced.
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Li, Zongzhi, and Kumares C. Sinha. "Application of Shackle’s Model and System Optimization for Highway Investment Decision Making under Uncertainty." Journal of Transportation Engineering 135, no. 3 (March 2009): 129–39. http://dx.doi.org/10.1061/(asce)0733-947x(2009)135:3(129).
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Wang, Sishen, Hao Wang, Pengyu Xie, and Xiaodan Chen. "Life-Cycle Assessment of Carbon Footprint of Bike-Share and Bus Systems in Campus Transit." Sustainability 13, no. 1 (December 25, 2020): 158. http://dx.doi.org/10.3390/su13010158.
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Low-carbon transport system is desired for sustainable cities. The study aims to compare carbon footprint of two transportation modes in campus transit, bus and bike-share systems, using life-cycle assessment (LCA). A case study was conducted for the four-campus (College Ave, Cook/Douglass, Busch, Livingston) transit system at Rutgers University (New Brunswick, NJ). The life-cycle of two systems were disaggregated into four stages, namely, raw material acquisition and manufacture, transportation, operation and maintenance, and end-of-life. Three uncertain factors—fossil fuel type, number of bikes provided, and bus ridership—were set as variables for sensitivity analysis. Normalization method was used in two impact categories to analyze and compare environmental impacts. The results show that the majority of CO2 emission and energy consumption comes from the raw material stage (extraction and upstream production) of the bike-share system and the operation stage of the campus bus system. The CO2 emission and energy consumption of the current campus bus system are 46 and 13 times of that of the proposed bike-share system, respectively. Three uncertain factors can influence the results: (1) biodiesel can significantly reduce CO2 emission and energy consumption of the current campus bus system; (2) the increased number of bikes increases CO2 emission of the bike-share system; (3) the increase of bus ridership may result in similar impact between two systems. Finally, an alternative hybrid transit system is proposed that uses campus buses to connect four campuses and creates a bike-share system to satisfy travel demands within each campus. The hybrid system reaches the most environmentally friendly state when 70% passenger-miles provided by campus bus and 30% by bike-share system. Further research is needed to consider the uncertainty of biking behavior and travel choice in LCA. Applicable recommendations include increasing ridership of campus buses and building a bike-share in campus to support the current campus bus system. Other strategies such as increasing parking fees and improving biking environment can also be implemented to reduce automobile usage and encourage biking behavior.
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Lu, Hongshi, Li Aijun, Wang Changqing, and Zabolotnov Michaelovitch Yuriy. "Impact analysis and adaptive sliding mode control for cislunar payload transportation using spinning tether system." Aircraft Engineering and Aerospace Technology 90, no. 8 (November 5, 2018): 1168–79. http://dx.doi.org/10.1108/aeat-02-2017-0067.
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Purpose This paper aims to present the impact analysis of payload rendezvous with tethered satellite system and the design of an adaptive sliding mode controller which can deal with mass parameter uncertainty of targeted payload, so that the proposed cislunar transportation scheme with spinning tether system could be extended to a wider and more practical range. Design/methodology/approach In this work, dynamical model is first derived based on Langrangian equations to describe the motion of a spinning tether system in an arbitrary Keplerian orbit, which takes the mass of spacecraft, tether and payload into account. Orbital design and optimal open-loop control for the payload tossed by the spinning tether system are then presented. The real payload rendezvous impact around docking point is also analyzed. Based on reference acceleration trajectory given by optimal theories, a sliding mode controller with saturation functions is designed in the close-loop control of payload tossing stage under initial disturbance caused by actual rendezvous error. To alleviate the influence of inaccurate/unknown payload mass parameters, the adaptive law is designed and integrated into sliding mode controller. Finally, the performance of the proposed controller is evaluated using simulations. Simulation results validate that proposed controller is found effective in driving the spinning tether system to carry payload into desired cislunar transfer orbit and in dealing with payload mass parameter uncertainty in a relatively large range. Findings The results show that unideal rendezvous manoeuvres have significant impact on in-plane motion of spinning tether system, and the proposed adaptive sliding mode controller with saturation functions not only guarantees the stability but also provides good performance and robustness against the parameter and unstructured uncertainties. Originality/value This work addresses the analysis of actual impact on spinning tether system motion when payload is docking with system within tolerated docking window, rather than at the particular ideal docking point, and the robust tracking control of deep-space payload tossing missions with the spinning tether system using the adaptive sliding mode controller dealing with parameter uncertainties. This combination has not been proposed before for tracking control of multivariable spinning tether systems.
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Yuan, Yunmei, Hongyu Li, and Lili Ji. "Application of Deep Reinforcement Learning Algorithm in Uncertain Logistics Transportation Scheduling." Computational Intelligence and Neuroscience 2021 (September 25, 2021): 1–9. http://dx.doi.org/10.1155/2021/5672227.
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Nowadays, finding the optimal route for vehicles through online vehicle path planning is one of the main problems that the logistics industry needs to solve. Due to the uncertainty of the transportation system, especially the last-mile delivery problem of small packages in uncertain logistics transportation, the calculation of logistics vehicle routing planning becomes more complex than before. Most of the existing solutions are less applied to new technologies such as machine learning, and most of them use a heuristic algorithm. This kind of solution not only needs to set a lot of constraints but also requires much calculation time in the logistics network with high demand density. To design the uncertain logistics transportation path with minimum time, this paper proposes a new optimization strategy based on deep reinforcement learning that converts the uncertain online logistics routing problems into vehicle path planning problems and designs an embedded pointer network for obtaining the optimal solution. Considering the long time to solve the neural network, it is unrealistic to train parameters through supervised data. This article uses an unsupervised method to train the parameters. Because the process of parameter training is offline, this strategy can avoid the high delay. Through the simulation part, it is not difficult to see that the strategy proposed in this paper will effectively solve the uncertain logistics scheduling problem under the limited computing time, and it is significantly better than other strategies. Compared with traditional mathematical procedures, the algorithm proposed in this paper can reduce the driving distance by 60.71%. In addition, this paper also studies the impact of some key parameters on the effect of the program.
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Giang, Wayne C. W., Lavinia Hui, Birsen Donmez, Mahvareh Ahghari, and Russell D. MacDonald. "Dispatch Decision Making in an Air Medical Transport System." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 259–63. http://dx.doi.org/10.1177/1541931213601059.
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Air medical transport relies on effective dispatching of air and land vehicles to provide the fastest and best care possible for patient transfers. These difficult dispatch decisions are characterized by high time pressure, uncertainty, and the dynamic and complex environment of medical transportation. This paper describes a preliminary study of the decision making processes that occur during dispatch decisions at Ornge, the air medical transportation system in Ontario, Canada. We drew upon the Critical Decision Method and the structured data analysis approach to understand the major decision points faced by Ornge’s dispatchers, and the cues and sources of information attended to in those situations. We found that the decision points deal with three main goals: maintain situation awareness, match resource to transfer, and plan logistics of transfer. Furthermore, we found that time estimation might play an important role in helping dispatchers coordinate within the dispatch team and with their external partners. These findings may help improve the design of computer aided dispatch software to better support the goals of the dispatchers.