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A Train Sequencing and Stop Scheduling Model inDouble Track Railway Lines by hHybrid GRASP-VNS Meta-Heuristic

Hassannayebi, Erfan | 2011

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 42688 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Kianfar, Fereydoon
  7. Abstract:
  8. The train scheduling problem is one of the most important scheduling problems in the transportation systems. The goal of train scheduling problem is generating a feasible timetable which consists of train departure times and determining the best station to stop. Optimizing the railway capacity is one of the most important goals in train scheduling phase. The sequence of dispatching trains and stopping schedule are the main factors that can affect railway capacity on double track lines. In this thesis, a double-track train sequencing problem is studied in order to maximize the railway capacity, subject to a set of operational requirements. This research proposes a flexible flow shop scheduling formulation which considers block section and station as single and parallel machines. In this thesis, an integer programming formulation for train sequencing problem is extended by incorporating the railway infrastructure characteristics and praying service schedule in IRAN railway network. This study also proposed some valid inequality constraints and preprocessing step to effectively reduce the solution space. This problem has proven to be strongly NP-hard. For finding the upper bounds, some hybrid greedy randomized adaptive search procedure (GRASP) and variable neighborhood search (VNS) algorithms are designed to solve the Problem. Comprehensive numerical experiments are conducted to illustrate the computational performance of the proposed meta-heuristic algorithms. In order to compare the hybrid meta-heuristic algorithms, random test problems, ranging in size from small, medium, to large are generated and solved by the meta-heuristic algorithms. The results show that the hybrid meta-heuristic algorithms are effective for real-sized instances
  9. Keywords:
  10. Flexible Jobshop Scheduling (FJSP) ; Greedy Randomized Adaptive Search Procedure (GRASP) ; Variable Neighborhood Search ; Train Sequencing ; Lower Bound

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