Railway traffic controllers face the problem that trains are often not operated as planned in timetables due to perturbations such as unexpected, degraded operations, and technical failures. This paper deals with the ...
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Railway traffic controllers face the problem that trains are often not operated as planned in timetables due to perturbations such as unexpected, degraded operations, and technical failures. This paper deals with the problem of train rerouting and rescheduling faced by the controllers of different regional railway control centers that must coordinate their decisions to minimize the impact of perturbations over the whole network. We split this real-time railway traffic management problem into two decision levels. At the lower level, the local area controllers, the dispatchers, manage train schedules and routes in their control areas from a microscopic perspective. At the higher level, a coordinator ensures the compatibility of dispatchers' decisions over two or more areas from a macroscopic perspective. To solve our problem, we propose an iterative optimization algorithm. The preliminary experimental results show that the proposed algorithm can reduce the delay propagation.
The optimal control problem including random variables is difficult to solve. Existing methods typically rely on defining explicit decision functions to make the problem tractable. However, in practical numerical test...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
The optimal control problem including random variables is difficult to solve. Existing methods typically rely on defining explicit decision functions to make the problem tractable. However, in practical numerical testing, we observed that some strongly coupled constraints, such as energy storage level limits, will impose strict restrictions on these simplified decision functions, potentially leading to significantly suboptimal solutions. Motivated by these challenges, this paper proposes a multi-stage robust implicit decision rule for the scheduling problem of energy storage systems. The main idea is to find an explicitly feasible decision function space to guarantee the multi-stage operating feasibility. When random variables are observed, decisions are adaptively optimized within the feasible decision space by solving a straightforward mathematical programming. Explicit decision functions are not required, ultimately enhancing the feasibility and optimality of the stochastic optimization for energy storage systems. Numerical tests are implemented on a real-world microgrid, verifying the effectiveness of the proposed method.
Model-free reinforcement learning (RL) provides a potential alternative to earlier formulations of adaptive transit signal priority (TSP) algorithms based on mathematical programming that require complex and nonlinear...
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ISBN:
(数字)9798331534202
ISBN:
(纸本)9798331534219
Model-free reinforcement learning (RL) provides a potential alternative to earlier formulations of adaptive transit signal priority (TSP) algorithms based on mathematical programming that require complex and nonlinear objective functions. This study extends RL-based traffic control to include TSP. Using a micro-scopic simulation environment and connected vehicle data, the study develops and tests a TSP event-based RL agent that assumes control from another developed RL-based general traffic signal controller. The TSP agent assumes control when transit buses enter the dedicated short-range communication (DSRC) zone of the intersection. This agent is shown to reduce the bus travel time by about 21 %, with marginal impact to general traffic at a saturation rate of 0.95. The TSP agent also shows slightly better bus travel time compared to actuated signal control with TSP. The architecture of the agent and simulation is selected considering the need to improve simulation run time efficiency.
Motivated by the commitments from the Talmud in Judaism, we consider the family planning rules which require a couple to get children till certain numbers of boys and girls are reached. For example, the rabbinical sch...
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The paper presents experience in using non-smooth optimization algorithms to solve problems of long-term planning of the functioning and development of transport systems. The class of problems under consideration is f...
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ISBN:
(数字)9798350353099
ISBN:
(纸本)9798350353105
The paper presents experience in using non-smooth optimization algorithms to solve problems of long-term planning of the functioning and development of transport systems. The class of problems under consideration is formulated as mathematical programming problems. mathematical models are characterized by high dimensionality and block structure of constraints. The solution method is based on the use of effective non-smooth optimization algorithms in combination with decomposition methods. As a rule, solving a problem consists of two stages. At the first stage, a dual problem is solved regarding general constraints that contain variables from various blocks of constraints. At the second stage, the solution to the initial problem is determined. Numerical aspects of this approach to solving applied logistics problems are discussed. A brief description of recently developed subgradient algorithms is given.
Reversible computing is a promising field that explores the possibility of performing computations in such a way that the initial state of the computation can be uniquely reconstructed from its final state. In this wo...
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The expanding e-commerce market and fierce competition forced managers to improve their delivery operations at each step in the parcel delivery process. The final part of this delivery process, known as last mile deli...
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Traditional methods for designing concrete mixtures provide good results;however, they do not guarantee the optimum composition. Consequently, applying operational research techniques is motivated by an increasing nee...
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Traditional methods for designing concrete mixtures provide good results;however, they do not guarantee the optimum composition. Consequently, applying operational research techniques is motivated by an increasing need for designers to proportion the concrete's raw materials that satisfy the concrete performance requirements such as mechanical properties, chemical proper-ties, workability, sustainability, and cost. For this reason, many authors have been looking for mathematical programming and machine learning solutions to predict concrete mix properties and optimise concrete mixtures. Therefore, a comprehensive review of operational research techniques concerning the design and proportioning of concrete mixtures and a classification framework are presented herein.
In this study, an interval two-stage fuzzy fractional programming (TFFP) method is developed to support collaborative governance of economy and water resources. Methods of interval programming, fuzzy programming, two-...
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This paper addresses the optimization of flight crew assignments by introducing a mathematical optimization model aimed at maximizing the allocation of flight crew to flights while minimizing overall occurrences and t...
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ISBN:
(数字)9798350360240
ISBN:
(纸本)9798350384161
This paper addresses the optimization of flight crew assignments by introducing a mathematical optimization model aimed at maximizing the allocation of flight crew to flights while minimizing overall occurrences and the use of substitute qualifications. The proposed approach involves route-based clustering, where flight routes are aggregated around hub airports to serve as the fundamental unit for matching flight crew. This transforms the problem of flight crew assignment into a route-based matching scenario. A mathematical programming model is established to minimize the number of unfulfilled crew assignments, flight occurrences, and substitute qualification usage, while also prioritizing these objectives. To enhance computational efficiency, a simulated annealing algorithm and a novel heuristic strategy are employed for solving. Results demonstrate the superiority of the new heuristic strategy over simulated annealing. Specifically, for dataset A, 202 flights were successfully scheduled with no crew occurrences or substitute qualification usage. For dataset B, 13235 flights were successfully arranged with zero crew occurrences or substitute qualification usage, and the program execution time was 0.32s.
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