Problem Of Rescheduling Research Articles

Integrated speed modeling and traffic management to precisely model the effect and dynamics of temporary speed restrictions to high-speed railway traffic

This paper investigates the integrated optimization problem of train rescheduling and train control for high-speed railway lines, where perturbations occur and cause Temporary Speed Restriction (TSR) to trains.We consider microscopic details (i.e., block sections) for ensuring the feasibility of the solution from a train signaling point of view, and an even higher level of detail, to accurately represent the train speed profiles. Running time and headway time are variable, at the same time depending from, and affecting, traffic. We optimize train speed profiles by considering vehicle and infrastructure constraints (e.g., traction, slopes). The model naturally considers the transition along the normal, disturbed and the recovery operation periods.A mixed-integer nonlinear programming (MINLP) model is first developed to simultaneously optimize train orders, routes, and departure and arrival times, as well as train speed profiles, aiming at reducing total train deviation time. The MINLP model is difficult to solve; thus we further reformulate it into a mixed-integer linear programming (MILP) model by means of piecewise linear approximation. A two-step approach is designed to speed up the solving procedure of the MILP model: first estimate the upper/lower bounds of train speeds and then solve the MILP model based on the estimated bounds of train speeds.Three instances (i.e., a small-scale line, a medium-scale line, and a large-scale network) are used to highlight the performance of the approach, verify the benefit of the integration, and its dependence on the parameters used. According to the experimental results, our integrated optimization method leads to an average improvement of 3%-36% in solution quality, compared with the integrated approach without train rerouting measure. Moreover, the integrated optimization method outperforms the sequential approach, achieving 6%-9% improvement in solution quality.

An improved bald eagle search optimization algorithm for optimal home energy management systems

In this study, an improved bald eagle search optimization algorithm (IBES) is utilized to develop home energy management systems for smart homes. This research is crucial for energy field researchers who are interested in optimizing energy consumption. The primary objective is to optimally manage load demand, reduce the average peak ratio, lower electricity bills, and enhance user comfort. To accomplish this goal, the load conversion strategy is used to coordinate household appliances and manage the home power system effectively. This approach aims to minimize peak–average ratios and electricity costs while ensuring consumer convenience. To minimize electricity bills, the study schedules the consumer’s daily activities based on actual time and next day’s energy demand. Furthermore, a fitness criterion is used to balance the load between off-peak and on-peak hours. The scheduler is designed to achieve an optimal device on/off state that minimizes device waiting time by coordinating household appliances in real time. To address the background problem of real-time rescheduling, dynamic programming is employed. The study evaluates the modified algorithm’s performance using three pricing strategies: critical peak pricing, real-time pricing, and time of use. The modified IBES technique is utilized to achieve the specified objectives of minimizing the electricity bill, reducing the peak–average ratio, and enhancing user convenience.

Self-reconfiguration and rescheduling of aero-engine assembly shop with rework disruption in knowledgeable manufacturing environment

An aero-engine has to be assembled twice or more to satisfy stringent technical standards. Substandard engines are required to re-enter for additional reassembly, which may trigger rework disruption. Work groups with flexible skills are responsible for the operations and can be reassigned when rescheduling triggers. An integrated problem of rescheduling of engines and reconfiguration of work groups with rework disruption is formulated here. A heuristic is proposed to solve the problem. In self-reconfiguration optimization, work groups are reassigned in accordance with the workload balance criterion without violating the assembly skills constraints. In rescheduling optimization, locally optimal sequencing propositions of operations are derived and applied to generate a feasible schedule. Three neighborhood structures are defined, and a variable neighborhood search is implemented to optimize the assignment and the schedule of operations on parallel work groups. The effectiveness of the proposed algorithm is tested by several numerical experiments. The results show that the proposed algorithm promises apparently lower total cost and more desirable assignment of work groups by scheduling.

Rescheduling Urban Rail Transit Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains

This paper develops a multi-objective mixed-integer linear programming model for the problem of robust rescheduling for capacitated urban rail transit (URT) trains to serve passengers from delayed high-speed railway (HSR) trains. The capacity of each extra train is not assumed to be unlimited in this paper. Robust passenger assignment constraints are developed to ensure that delayed passengers can board the URT trains under different random delay scenarios of HSR operations. Robust dispatching constraints of URT trains are designed for a stable disrupting number of URT trains across different scenarios. The multi-objective model is used to maximize the number of expected transported passengers and minimize the number of extra trains and operation-ending time of all extra trains. An iterative solution approach based on a revised version of the epsilon-constraint method combined with the weighted-sum method is designed for the computation of the multi-objective model. Computational experiments are performed on the Beijing URT lines and the Beijing-Shanghai HSR line. We evaluate the impact of the robustness constraints of passenger assignment and the number of extra trains to ensure that the number of trains are maintained and the passengers can successfully take the trains during different delayed scenarios.

An Algorithm for Rescheduling of Trains under Planned Track Closures

This work considered a joint problem of train rescheduling and closure planning. The derivation of a new train run schedule and the determination of a closure plan not only must guarantee the satisfaction of all the given constraints but also must optimize the number of accepted closures, the number of approved train runs, and the total time shift between the resultant and the original schedule. Presented is a novel nonlinear mixed integer optimization problem which is valid for a broad class of railway networks. A multi-level hierarchical heuristic algorithm is introduced due to the NP-hardness of the considered optimization problem. The algorithm is able, on an iterative basis, to jointly select closures and train runs, along with the derivation of a train schedule. Results obtained by the algorithm, launched for the conducted experiments, confirm its ability to provide acceptable and feasible solutions in a reasonable amount of time.

Aircraft rerouting and rescheduling in multi-airport terminal area under disturbed conditions

The disturbances, caused by resource competition, weather changes, and demand uncertainties, frequently occur and result in obvious fluctuations (e.g., the increase or decrease of demand and capacity) in demand and capacity. This work addresses the problem of aircraft rerouting and rescheduling in presence of disturbances, characterized by the increase or decrease of demand and capacity, at a multi-airport terminal maneuvering area (TMA). A multi-objective optimization model is proposed to minimize the total flight delays and total extra flying time, considering the aircraft wake turbulence separation, runway operation rules, air traffic control regulations, scheduling time windows, and airport/airspace capacity, etc. Based on the non-dominated sorting genetic algorithm (NSGA-II), we designed an efficient evolutionary algorithm to solve the proposed model and search the Pareto solutions. Five scenarios are designed to analyze and compare the operation performance under normal and disturbed conditions, using the classic First-Come-First-Served (FCFS) and optimized scheduling (OPTS) methods. A case study is conducted for Shanghai Pudong and Hongqiao Airports, and the computational results show that, compared with the FCFS method, the proposed OPTS method significantly performs a overall better performance in minimizing total flight delay and total extra flying time, while slightly inferior in optimizing the delay and extra flying time of one single flight.

Performance comparison using firefly and PSO algorithms on congestion management of deregulated power market involving renewable energy sources

The power industry across the globe is subjected to a sweeping change in its business as well as in an operating model where the monopoly utilities are being liberalized and opened up for competition with private players. As an outcome of this, the transmission corridors evacuating the power of inexpensive generators would be burdened if all such transactions are admitted. One of the most proficient techniques for congestion management is rescheduling the generators. This research paper suggests a framework to regulate the power flows of the transmission lines within the stipulated limit in a deregulated electricity market environment through rescheduling with and without renewable energy sources (RES). The problem of rescheduling is framed with the intention of lessening the congestion cost. Unlike the traditional method, the best location for the placement of RES is established utilizing a novel weighted locational marginal price (LMP)-based method. The firefly algorithm (FA) and particle swarm optimization (PSO) algorithm are employed in order to get optimized results. The realistic cases are considered, and the results obtained with and without RES using FA and PSO are compared to prove the research study. The efficacy of the method is explored with IEEE 30-bus system.

Hyperheuristic genetic algorithm for steelmaking continuous casting rescheduling based on strong disturbance of task

The problem of steelmaking and continuous casting rescheduling is the key problem in the steel production scheduling. Aiming at the problem of steelmaking and continuous casting rescheduling under strong disturbance of task in this paper, a fast and efficient hyperheuristic genetic algorithm is proposed to minimise the difference degree before and after the scheduling adjustment as the objective function. In the framework of the hyperheuristic algorithm, the high layer strategy is designed as a self-learning heuristic rule selection strategy, the low layer design is a series of genetic operators related to steelmaking and continuous casting scheduling, optimal solution of update iteration by searching for each other through high and low two layers of strategy in the effective threshold. Simulation experiments show that the effectiveness of the algorithm can meet the needs of real-time and stable production to the maximum extent.

Heuristic Based Real‐Time Train Rescheduling System

This article deals with the problem of train rescheduling on a railway network. Starting from a defined network topology and initial timetable, the article considers a dynamic train rescheduling in response to disturbances that have occurred. The train rescheduling problem was mapped into a special case of the job shop scheduling problem and solved by applying a constraint programming approach. To improve the time performance of available constraint programming tool and to satisfy a selected objective function, a combination of three classes of heuristics are proposed: bound heuristics, separation heuristics, and search heuristics. Experimental evaluation of the implemented software in Belgrade railway dispatching area indicates that the proposed approach is capable of providing the support to a real‐life operational railway control. In our solution, the dispatcher has the possibility of choosing the most suitable optimization criterion from the set of seven available ones. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 67(1), 32–48 2016

Train Operation Rescheduling Algorithm Based on Passenger Satisfaction

Although computer systems can assist rescheduling experts and have recently been utilized to restore disrupted train operations, they are not so helpful in decreasing dispatcher workloads. This is because they lack the intelligence that would enable them to create rescheduling plans automatically. In this paper, we propose an algorithm for automatic train rescheduling. Firstly, we proposed using the causes of passenger dissatisfaction as rescheduling plan criteria and to regard the problem of train rescheduling as a constraint optimization problem in which the degree of passenger dissatisfaction should be minimized. Then we introduced an algorithm for train rescheduling designed as a combination of Program Evaluation and Review Technique (PERT) and metaheuristics. We have shown some experimental results derived from applying the algorithm to data from existing train schedules.

DO defence expenditures increase debt rescheduling in Turkey? probit model approach

This paper empirically investigates the importance of financial and political variables in determining debt rescheduling probabilities in Turkey for 1955–2000. The problem of sovereign debt default and rescheduling has been the subject of substantial academic research during the last two decades. There has been criticism of models of developing countries’ indebtedness and rescheduling that rely solely on some economic or financial predictors related to country debt, the foreign exchange sector or the domestic economic situation. Using probit analysis, this paper indicates that financial variables are important determinants of rescheduling probabilities. However, political variables are not significant in our models.

PRIAM: polite rescheduler for intelligent automated manufacturing

This paper considers the problem of rescheduling in a decentralized manufacturing system. Flexible manufacturing systems must be able to respond to unexpected disruptions; including schedule disruptions. However, when a cell controller in a decentralized system responds to a disruption, it may disrupt some other cells, because the actions taken at one cell may have some consequence at another cells. In the approach we propose, a controller at a disrupted cell tries to respond in a way which is likely to be least disruptive to other cells, through negotiation with controllers at other cells. This approach, which we call replanning, has the advantage of retaining much of the original distributed plan, while avoiding wide propagation of the disruption through the rest of the system. We apply this concept to the domain of distributed factory rescheduling, and describe PRIAM (polite rescheduler for intelligent automated manufacturing), a polite rescheduling architecture which is currently under development. Simulation results show that the use of negotiation in polite rescheduling prevents the wide propagation of disruption from an initial local disruption.

Open Order Rescheduling in Job Shops with Demand Uncertainty: A Simulation Study

ABSTRACTThis paper addresses the problem of open order rescheduling in a job shop. Results are reported of experiments conducted in a simulation model of an 8‐machine job shop. Four different order updating policies are examined. The due‐date process is modeled to include several realistic features about the pattern of due‐date variability. These features are parameterized and tested at several levels. Tardiness results indicate that open order rescheduling is beneficial only when allowances are loosely set. The results indicate that inventory performance is improved by order rescheduling, particularly in cases when due dates are revised to earlier times than originally forecast.