2011
DOI: 10.1142/s1793830911001334
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A Search-Based Approach to Railway Rolling Stock Allocation Problems

Abstract: Experts working for railway operators still have to devote much time and effort to creating plans for rolling stock allocation. In this paper, we formulate the railway rolling stock allocation problem as a set partitioning multi-commodity flow (SPMCF) problem and propose a search-based heuristic approach for SPMCF. We show that our approach can obtain an approximate solution near the optimum in shorter time than CPLEX for real-life problems. Since our approach deals with a wide variety of constraint expression… Show more

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Cited by 4 publications
(2 citation statements)
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References 5 publications
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“…Budai et al [10] proposed a method to reschedule the train-set circulation plan based on a current timetable, so as to meet the demands of the new timetable. Otsuki et al [11] described the railway rolling stock allocation problem based on a set partitioning multi-commodity flow (SPMCF) problem. Thorlacius et al [12] constructed the rolling stock plan by using a heuristic algorithm.…”
Section: Train-set Rescheduling Problemmentioning
confidence: 99%
“…Budai et al [10] proposed a method to reschedule the train-set circulation plan based on a current timetable, so as to meet the demands of the new timetable. Otsuki et al [11] described the railway rolling stock allocation problem based on a set partitioning multi-commodity flow (SPMCF) problem. Thorlacius et al [12] constructed the rolling stock plan by using a heuristic algorithm.…”
Section: Train-set Rescheduling Problemmentioning
confidence: 99%
“…EMU maintenance must be scheduled with respect to the assigned train tasks; however, EMU‐maintenance‐based mileage limits cannot be directly expressed in a linear form. To manage these mileage limits, various heuristic and intelligent algorithms have been proposed [8–11]. Problems in the second category, in which maintenance is not considered, are predominantly solved using multi‐commodity models [12–16].…”
Section: Introductionmentioning
confidence: 99%