1,169 research outputs found

    Chapter 6: The Compact Dynamic Bus Station

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    AbstractThis document is one of the parts of the electronic version of the PhD thesis by S.F.M. van Vlijmen [8]. The goal of the PhD project was to get a better understanding of the problems with the integration of formal specification technique in the day to day software practice. The approach followed was to execute a number of projects in cooperation with industry on realistic cases.In this document is reported on the design of an innovative system for the control of bus stations. The innovative aspect is that buses do not have a fixed platform at the station, the platform is dynamically assigned and communicated to the driver upon entering the station. It was tried to specify data and processes in an algebraic style. During the project it turned out that formal specification in this style did not work to our advantage

    Approximate Algorithms for the Combined arrival-Departure Aircraft Sequencing and Reactive Scheduling Problems on Multiple Runways

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    The problem addressed in this dissertation is the Aircraft Sequencing Problem (ASP) in which a schedule must be developed to determine the assignment of each aircraft to a runway, the appropriate sequence of aircraft on each runway, and their departing or landing times. The dissertation examines the ASP over multiple runways, under mixed mode operations with the objective of minimizing the total weighted tardiness of aircraft landings and departures simultaneously. To prevent the dangers associated with wake-vortex effects, separation times enforced by Aviation Administrations (e.g., FAA) are considered, adding another level of complexity given that such times are sequence-dependent. Due to the problem being NP-hard, it is computationally difficult to solve large scale instances in a reasonable amount of time. Therefore, three greedy algorithms, namely the Adapted Apparent Tardiness Cost with Separation and Ready Times (AATCSR), the Earliest Ready Time (ERT) and the Fast Priority Index (FPI) are proposed. Moreover, metaheuristics including Simulated Annealing (SA) and the Metaheuristic for Randomized Priority Search (Meta-RaPS) are introduced to improve solutions initially constructed by the proposed greedy algorithms. The performance (solution quality and computational time) of the various algorithms is compared to the optimal solutions and to each other. The dissertation also addresses the Aircraft Reactive Scheduling Problem (ARSP) as air traffic systems frequently encounter various disruptions due to unexpected events such as inclement weather, aircraft failures or personnel shortages rendering the initial plan suboptimal or even obsolete in some cases. This research considers disruptions including the arrival of new aircraft, flight cancellations and aircraft delays. ARSP is formulated as a multi-objective optimization problem in which both the schedule\u27s quality and stability are of interest. The objectives consist of the total weighted start times (solution quality), total weighted start time deviation, and total weighted runway deviation (instability measures). Repair and complete regeneration approximate algorithms are developed for each type of disruptive events. The algorithms are tested against difficult benchmark problems and the solutions are compared to optimal solutions in terms of solution quality, schedule stability and computational time

    ๋‹ค์ค‘๊ณตํ•ญ์—์„œ ์ง€์ƒ ์ง€์—ฐ ํ”„๋กœ๊ทธ๋žจ ๋ฐœ์ƒ์‹œ ์ง€์—ฐ์ „ํŒŒ๋ฅผ ๊ณ ๋ คํ•œ ํ•ญ๊ณต์‚ฌ์˜ ์šดํ•ญ ์ผ์ • ๋ณ€๊ฒฝ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2022. 8. ๋ฌธ์ผ๊ฒฝ.๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ํ•ญ๊ณต ๊ตํ†ต์„ ์ œ์–ดํ•˜๋Š” ์ค‘์š”ํ•œ ์ˆ˜๋‹จ ์ค‘ ํ•˜๋‚˜์ธ ์ง€์ƒ ์ง€์—ฐ ํ”„๋กœ๊ทธ๋žจ(GDP)์ด ๋ฐœ์ƒํ•  ๊ฒฝ์šฐ ๊ณตํ•ญ์˜ ๋ณ€๊ฒฝ๋œ ์ˆ˜์šฉ๋ ฅ์— ๋Œ€์‘ํ•˜๋„๋ก ํ•ญ๊ณต์‚ฌ์˜ ๊ด€์ ์—์„œ ํ•ญ๊ณตํŽธ์„ ์žฌ์กฐ์ •ํ•˜๋Š”๋ฐ ๋„์›€์„ ์ฃผ๋Š” ๊ฒƒ์ด๋‹ค. ๋‹จ์ผ ๊ณตํ•ญ์ด ์•„๋‹Œ ๋‹ค์ค‘ ๊ณตํ•ญ์œผ๋กœ ํ™•์žฅํ•˜์—ฌ ๋™์ผํ•œ ๊ณตํ•ญ๋ฟ ์•„๋‹ˆ๋ผ ๋‹ค๋ฅธ ๊ณตํ•ญ์œผ๋กœ๋ถ€ํ„ฐ์˜ ์ง€์—ฐ ์ „ํŒŒ๋ฅผ ๊ณ ๋ คํ–ˆ์œผ๋ฉฐ, ํ•ญ๊ณต๊ธฐ ๋ฐ ์Šน๋ฌด์›์˜ ๊ณ„ํš๋œ ์ผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํ˜„์‹ค์ ์ธ ๋น„์šฉ์„ ํฌํ•จํ–ˆ๋‹ค. GDP๊ฐ€ ๋ฐœํ–‰๋˜๋ฉด ํ•ญ๊ณต์‚ฌ๋“ค์€ ๋ณ€๊ฒฝ๋œ ์‹œ๊ฐ„๋Œ€์— ๋งž์ถฐ ํ•ญ๊ณตํŽธ์„ ์žฌ์กฐ์ •ํ•  ์ˆ˜ ์žˆ๋Š” ์งง์€ ์‹œ๊ฐ„์ด ์ฃผ์–ด์ง„๋‹ค. ๊ฐ ๊ณตํ•ญ์—๋Š” ์ˆ˜์šฉ๋ ฅ์ด ์žˆ์œผ๋ฉฐ, ํŠนํžˆ ๋“ค์–ด์˜ค๋Š” ํ•ญ๊ณต๊ธฐ๋ฅผ ์ˆ˜์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์šฉ๋Ÿ‰์ธ ๊ณตํ•ญ ์ˆ˜์šฉ๋ฅ (AAR)์ด ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ ๋น„ํ–‰ ์Šค์ผ€์ค„์„ ์žฌ์กฐ์ •ํ•˜๊ธฐ ์œ„ํ•ด ํ˜ผํ•ฉ ์ •์ˆ˜ ์„ ํ˜• ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๋ชจ๋ธ์„ ์„ธ์› ๋‹ค. ๋˜ํ•œ, ๋ฏธ๋ž˜์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ๋‹ค๋ฃจ๊ธฐ ์œ„ํ•ด, MILP์˜ ๋‘ ๊ฐ€์ง€ ๋ฒ„์ „์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. AAR์ด ์–ด๋Š ์‹œ์ ์— ๋‹ค์‹œ ๋ฐ”๋€Œ๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๋งŒ๋“  ํ›„, ๊ฐ ์‹œ๋‚˜๋ฆฌ์˜ค ๋ณ„๋กœ ์ด ๊ด€๋ จ ๋น„์šฉ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ์†”๋ฃจ์…˜์„ ๋„์ถœํ•˜๋Š” ์ตœ์  ๋ชจ๋ธ๊ณผ ๋ชจ๋“  ์‹œ๋‚˜๋ฆฌ์˜ค ์†”๋ฃจ์…˜์˜ ์ด ๊ด€๋ จ ๋น„์šฉ์˜ ๊ธฐ๋Œ“๊ฐ’์„ ์ตœ์†Œํ™”ํ•˜๋Š” ์†”๋ฃจ์…˜์„ ๋„์ถœํ•˜๋Š” ์ถ”๊ณ„ ๋ชจ๋ธ์„ ์ œ์‹œํ•˜๊ณ  ์„œ๋กœ ๋น„๊ตํ•˜์˜€๋‹คThe purpose of this thesis is to reschedule flights from the airline companyโ€™s perspective to correspond to the airportโ€™s changed capacity in the event of a ground delay program (GDP), one of the important means of controlling air traffic. We considered delay propagation not only within the same airport but within other airports by extending the setup to include several airports rather than a single airport. We also included realistic costs from planned schedules of the aircraft and crew. When a GDP is issued, airlines are given a short time to reschedule flights in time for the changed slot. Each airport has its own capacity, especially the airport acceptance rate (AAR), which is a capacity that can accommodate incoming aircraft. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. To handle the uncertainty of future scheduling, two versions of the MILP model may be applied. With scenarios in which the AAR changes again, an optimal model that obtains a minimizing total relevant cost in each scenario solution and a stochastic model solution that obtains a minimizing expectation of the total relevant cost of all scenarios are presented and compared.Chapter 1 Introduction 1 Chapter 2 Literature review 3 Chapter 3 Mathematical model 5 3.0 Model description 5 3.1 Multi-airport Scenario-based Optimal Rescheduling Problem 10 3.2 Multi-airport Scenario-based Stochastic Rescheduling Problem 13 Chapter 4 Computational experiments 14 4.0 Settings 14 4.1 Experiment 1 16 4.2 Experiment 2 18 4.3 Experiment 3 19 4.4 Experiment 4 20 Chapter 5 Conclusions 25 Appendix 27 Appendix A. 27 Appendix B. 28 Bibliography 31 ๊ตญ๋ฌธ์ดˆ๋ก 35์„

    A mixed integer linear programming model with heuristic improvements for single-track railway rescheduling problem

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    A rescheduling algorithm for trains on a single-track railway was developed in case of disturbances that would cause conflicts between trains. This algorithm is based on mixed integer linear programming (MILP) with speed-up routines. The model considers station capacities explicitly (i.e., the number of available tracks for meeting and overtaking operations). Because the model is too hard for the solvers (CPLEX in this study) to tackle, three speed-up routines were devised when rescheduling trains. These routines are a greedy heuristic to reduce the solution space, using the lazy constraint attribute of the solver and a multiobjective approach to find a good initial feasible solution that conforms to actual railway operation. The algorithm was tested on a hypothetical rail line for different sizes of timetable instances with disturbed trains in a maximum two-hour time horizon. It managed to solve the hardest instances within a three-minute time limit thus minimizing the total weighted delay of rescheduled trains. The optimality gap metric is used to show the effectiveness and efficiencies of the speed-up heuristics developed

    Disruption Management in Passenger Railways

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    Disruption Management in Passenger Railways

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    A railway timetable rescheduling approach for handling large scale disruptions

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    On a daily basis, relatively large disruptions require infrastructure managers and railway operators to reschedule their railway timetables together with their rolling stock and crew schedules. This research focuses on timetable rescheduling for passenger trains at a macroscopic level in a railway network. An integer programming model is formulated for solving the timetable rescheduling problem, which minimizes the number of cancelled and delayed trains while adhering to infrastructure and rolling stock capacity constraints. The possibility of rerouting trains in order to reduce the number of cancelled and delayed trains is also considered. In addition, all stages of the disruption management process (from the start of the disruption to the time the normal situation is restored) are taken into account. Computational tests of the described model on a heavily used part of the Dutch railway network show that we are able to find optimal solutions in short computation times. This makes the approach applicable for use in practice
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