Integrated Line Planning and Timetable Scheduling for Railways Considering the Dynamics and Uncertainty of Passenger Demand

This paper develops the pricing model for hotel industries competing in the same market segment. We consider the condition that the hotels may face the deterministic demand or uncertain demand and price sensitive customers. The seasonal factors are also considered. We use game theory to evaluate the systems for both deterministic and stochastic demand scenarios. Based on case studies we can conclude that in general pricing model considering stochastic demands enables to represent the actual system better than the deterministic demand model.

Train timetabling in rail transit network under uncertain and dynamic demand using Advanced and Adaptive NSGA-II

The general optimal market area model with uncertain and nonstationary demand

The passenger demand of urban rail transit is dynamic and uneven in time and space, and traditional train plan of single train formation cannot adapt to dynamic passenger demand. In order to solve the redundancy of train capacity caused by uneven passenger demand in bi-directions, we proposed a mixed-integer linear programing model (MILP) to optimize the train formation plan and rolling stock scheduling integrally based on known passenger demand and timetable for an urban rail transit line. The turnaround operation, coupling/decoupling operation, the entering/exiting depot operation of train services, the number of available trains and the capacity of depot are involved. The model is solved by the CPLEX solver. As illustration, the model is applied to Beijing Batong line to verify its effectiveness and performance. The results show that through this integrated approach the number of operation formations can reduce 44% and the number of rolling stocks can reduce 20%. It demonstrated that the proposed model can effectively reduce the operation cost while satisfy the uneven demand.

The research on virtual coupling technology in metros is a hot topic recently. Intelligent automatic train units can flexibly form virtually coupled train formations (VCTFs) when needed, so that the train capacity would be able to adapt to passenger demands, which are changing dynamically with time and space. However, matching the capacity of trains with passenger demands is a difficult decision-making optimization problem. In this work, we propose a collaborative control problem to decide when and how to create a VCTF to meet the dynamic passenger demands in a metro system. A mathematical model is formulated which can simultaneously optimize the number of related train units in a VCTF, the train dwelling time at a station and the running time in a section. Performance indicators such as train load factor and number of stranded passengers are adopted to measure the satisfaction of the operators and passengers. Then, a multi-objective genetic algorithm is adopted to obtain the Pareto optimum solutions. Finally, simulation experiments are conducted, and the relationship between line efficiency and passenger demand in different discrete time periods is analyzed to verify the correctness and feasibility of the model. Compared with the traditional timetabling method, adopting the virtual coupling technology can better adapt the train capacity to meet the time changing passenger demands, which is welcomed by both metro operators and passengers in reality.

Preventive maintenance is a planned and scheduled maintenance method that is carried out before a machine failure occurs. The maintenance schedule can be determined based on experience, historical data, or recommendations. Selecting the maintenance schedule greatly affects the production system. The Clin machine in cement manufacturing has an important role in the cement production process. During treatment, the client machine cannot produce clinker, so it is necessary to plan a production system to meet the demand. This paper aims to design an optimization model for determining the preventive maintenance schedule for cement manufacturing by considering the production process and inventory control. Mathematical models with binary options are used to model that system. The model supports showing the optimal preventive maintenance schedule for the ciln machines with a binary decision each period. This mathematical model describes the interaction of production planning, inventory control, and scheduling of total maintenance on a kiln machine. The goal of this system is to determine the optimal preventive maintenance schedule with minimum costs. In addition, the system's output is the optimal production and inventory decision rule for each period. Based on the analysis and simulation of the model with the deterministic and dynamic demand, the optimal preventive maintenance schedule is in the 9th and 21st periods. The kiln machines are maintained every July with minimal costs. The model scenario shows the interaction of the variables and the sensitivity of the production capacity and demand to the decision rule of the variable.

Robust deadline-aware network function parallelization framework under demand uncertainty

Emergency resource is important for people evacuation and property rescue when accident occurs. The relief efforts could be promoted by a reasonable emergency resource allocation schedule in advance. As the marine environment is complicated and changeful, the place, type, severity of maritime accident is uncertain and stochastic, bringing about dynamic demand of emergency resource. Considering dynamic demand, how to make a reasonable emergency resource allocation schedule is challenging. The key problem is to determine the optimal stock of emergency resource for supplier centers to improve relief efforts. This paper studies the dynamic demand, and which is defined as a set. Then a maritime emergency resource allocation model with uncertain data is presented. Afterwards, a robust approach is developed and used to make sure that the resource allocation schedule performs well with dynamic demand. Finally, a case study shows that the proposed methodology is feasible in maritime emergency resource allocation. The findings could help emergency manager to schedule the emergency resource allocation more flexibly in terms of dynamic demand.

Both passenger demand and service supply are among the most important factors that determine the performance of urban rail transit system. It is not easy to find out optimal solution for the match between the passenger demand and service supply with traditional methods, due to the complexity of the combinatorial intelligent supply - demand matching problem. In order to get the comprehensively optimal matching degree, this paper transforms the multi-criteria problem into the distributed artificial intelligence optimization by using multi-agent dynamic interaction technique. On the demand side, the dynamic passenger traffic demand with agents is modelled from perspective of boundedly rational travel decision. On the supply side, the dynamic service supply of train traffic with agent is modelled. The headway time is designated as the main decision variable, for the key link between the passenger demand and service supply is the headway time in different time-of-day intervals. To make the passenger demand more closely matched with service supply in urban rail transit network system at the reasonable travel cost and operational cost, the calculation formula for matching degree is proposed, along with the distributed system architecture for agent-based matching mechanism, and the negotiation-based iterative mechanisms for balancing. The proposed methods are validated on the simulation platform NetLogo. The simulation results emphasize the importance of representing the supply side and the demand side jointly/interactively. These findings are meaningful for policies on both development of efficient capacity usage strategies of urban rail transit network and provision of high level of service for passengers.

Full-time scale resilience enhancement strategies for an integrated electricity-gas-transportation coupled system

The increasing complexity and interconnectivity of transportation networks call for a better understanding of the stochasticity and uncertainty of the spatio-temporal network variables such as demand, flow, and speed. On the one hand, classical network models overlook the statistical features of network dynamics, a critical feature of recurrent traffic. The statistical features of spatio-temporal networks are also critical inputs for off-line and on-line transportation management for large-scale networks. On the other hand, various data resources, from traditional traffic sensors (loops, cameras, etc.) as well as emerging sensors (Bluetooth, GPS probe, parking spot occupancy, etc.), are available and have been archived for decades in many mega-cities. With the network models and sensing technologies being developed for decades, there is a lack of study on the understanding of inter-relations of spatio-temporal vehicles/passengers in the network, their causes from demand characteristics, and how big data can help estimate, predict and ultimately intervene any component of network flow aiming for system optimum. This dissertation presents a comprehensive study on the statistical traffic assignment models and probabilistic demand estimation models in the constant-time networks as well as the time-varying networks. The following three major questions are answered throughout the dissertation: 1) How to characterize the spatio-temporal relationship of flow dynamics (traffic flow rate, speed, choices of time and routes) given uncertain traffic demand and system performance? 2) How to infer the flow dynamics using both archived and real-time data from multiple resources? 3) How to use the new methodology to improve the reliability and sustainability of large-scale networks through improving both recurrent and non-recurrent traffic conditions? As the preliminary research, we first present a complete study on the generalized statistical traffic assignment model (GESTA) and probabilistic demand estimation model in the constant-time networks. The GESTA builds the relationship among probability distributions of link/path flow and their travel cost where the variance stems from three sources, demand, route choice and unknown errors. A novel theoretical framework for estimating demand distribution using multi-source traffic data is proposed. Both models can be solved efficiently in a large-scale network to provide insights for decision making and demonstrate computational efficiency. To understand the flow dynamics in time-varying networks, we propose a novel concept of dynamic assignment ratio (DAR) matrix. The DAR matrix enables the realistic representation and efficient computation of dynamic traffic flow. With DAR matrix, we propose a theoretical formulation for estimating dynamic demand using computational graph. The computational graph can be evaluated on multi-core CPUs or Graphics Processing Units (GPU) efficiently, and hence the proposed method can be efficiently applied to the large-scale networks. Using the concept of DAR matrix, we present a solution framework for multi-class dynamic OD demand estimation (MCDODE) in large-scale networks. The proposed framework is built on a computational graph with tensor representations of spatio-temporal flow and all intermediate features involved in the MCDODE formulation. A forward-backward algorithm is proposed to efficiently solve the MCDODE formulation on computational graphs. Additional, we rigorously formulate the spatio-temporal structure of probabilistic traffic demand and propose a statistical inference framework to infer the structure of dynamic demand using multi-source data. Lastly, we present two real-world applications that are built on top of the transportation network models. The first application analyzes the impact of the Greenfield Bridge closure using the dynamic network analysis for Pittsburgh Metropolitan Area. The second application builds a real-time traffic management application based on the dynamic traffic assignment. The two applications demonstrate the potentials of our proposed models in improving the mobility, safety and reliability of the transportation systems.

In network function virtualization, a service function chain (SFC) specifies a sequence of virtual network functions that user traffic has to traverse to realize a network service. The problem of SFC orchestration has been extensively studied in the literature. However, most existing works assume deterministic demands and resort to costly runtime resource reprovisioning to deal with dynamic demands. In this work, we formulate the deadline-aware co-located and geo-distributed SFC orchestration with demand uncertainty as robust optimization problems and develop exact and approximate algorithms to solve them. A key feature of our formulation is the consideration of end-to-end delay in service chains by carefully modeling load-independent propagation delay as well as load-dependent queueing and processing delays. To avoid frequent resource reprovisioning, our algorithms utilize uncertain demand knowledge to compute proactive SFC orchestrations that can withstand fluctuations in dynamic service demands. Extensive simulations are conducted to evaluate the performance of our algorithms in terms of ability to cope with demand fluctuations, scalability, and relative performance against other recent algorithms.

Robust optimization models for integrated train stop planning and timetabling with passenger demand uncertainty

Rail systems are preferred to find sustainable solutions to traffic problems in cities with high population density. To increase the use of public transport without reducing service quality, it is essential to construct a demand-based timetable. In this study, a mathematical model that meets the passenger demand in cases of over and under-saturation, considers structural-operational constraints, aims minimum energy consumption and machinist costs is presented. Passenger demands are forecasted using four popular techniques. In both over and under-saturated situations, it is possible to meet passenger demand by using adaptive headways and determining train specifications as coupled or decoupled. Moreover, a comfort coefficient is incorporated in the model to adjust the service quality of the trip. To demonstrate the performance of the proposed MILP model, Kayseri Ulasim A.S. timetabling problem is solved. The effect of different passenger demand levels, over and under-saturated cases, on headways and train types is analysed.

Robust metro train scheduling integrated with skip-stop pattern and passenger flow control strategy under uncertain passenger demands