Managing disruptions in the multi-depot vehicle scheduling problem

Ucar, Ezgi; Birbil, Ş. İlker; Muter, İbrahim

doi:10.1016/j.trb.2017.09.002

Cited by 17 publications

(5 citation statements)

References 25 publications

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“…Mahadikar et al [ 30 ] constructed a multi-stage allocation model with the main objective of minimizing the empty movement mileage of trains by considering the capacity of the depot and the demand for the number of vehicles on the train service route. Ucar et al [ 31 ] considered two types of disruptions in the scheduling process of the multi-depot vehicle and proposed a unique recovery method to deal with these potential disruptions, and designed a simultaneous column-and-row generation algorithm to find a valid lower bound of the model.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated optimization of urban rail transit line planning, timetabling and rolling stock scheduling

Tang

Zhang³

et al. 2023

PLoS ONE

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Urban rail transit train operation plan is a comprehensive production plan encompassing line planning, timetabling, and rolling stock scheduling. In order to solve the problem of infeasibility of the line plan and timetable because the number of rolling stocks could be only precisely considered in the rolling stock scheduling. An integrated optimization solution is proposed which considers the line plan, timetable, and rolling stock schedule. Candidate service routes are generated according to the layout of the turn-back stations. Considering the constraints of operation and passenger flow demand, an integer nonlinear programming model is established to minimize the cost of operation and passenger waiting time. The model complexity is analyzed and based on its decomposability a deterministic search algorithm is designed. Taking Chongqing Metro Line 3 in China as an example to verify the effectiveness of the proposed model and algorithm. Compared with the train operation plan based on manual experience and compiled by stages, the integrated optimization model can better improve the quality of train operation plan.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated optimization of urban rail transit line planning, timetabling and rolling stock scheduling

Tang

Zhang³

et al. 2023

PLoS ONE

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“…We refer to Muter et al (2013) for a in-depth paper on column-and-row generation with column-dependent-rows that oriented our contribution. Some applications of this method consider the multicommodity capacitated fixed-charge network design problem (Gendron and Larose 2014), a railway crew capacity planning problem (Suyabatmaz and Sahin 2015), an integrated airline recovery problem (Maher 2016), disruptions arising in the multidepot vehicle scheduling (Uccar et al 2017), and the one-dimensional two-stage cutting stock problem (Muter and Sezer 2018).…”

Section: Column Generationmentioning

confidence: 99%

Decomposition-based heuristic for the zoning and crop planning problem with adjacency constraints

Albornoz

Zamora

2020

TOP

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This paper tackles management zone delineation and crop planning problems in an integrated precision agriculture framework. The zoning problem defines relatively homogeneous management zones regarding their soil properties, and for which specific rates of agricultural inputs are necessary. From a sustainable point of view, the crop planning problem considers cropping of species from different botanic families in adjacent zones at the same time. With this in mind, we propose a novel linear binary integer program for an integrated zoning and crop planning problem with adjacency constraints. In this model, we maximize the incomes of the crop plan subject to zoning constraints and adjacency constraints on crop families. The proposed model has a column-based formulation, and as such, we develop a decompositionbased heuristic which make use of the column generation method with columndependent rows. The decomposition strategy involves a master problem that deals with ensuring homogeneity of the selected management zones within the field partition and ensuring that the crop plan meets adjacency policies. On the other hand, the pricing problem generates rectangular management zones whose incorporation improves the objective value of the master problem. The algorithm is implemented in JuMP, a modeling language for mathematical optimization embedded in Julia. Results from a set of instances show the relevance of the decomposition-based heuristic.

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“…The authors conclude that, under certain conditions, a single depot vehicle scheduling model performs better. Two types of disruptions arising in the MDVSP were considered: the delays and the extra trips [22]; these disruptions may or may not occur during operations, and they are indirectly incorporated into the planned schedule by anticipating their likely occurrence times. The objective functions more frequently used in a MDVSP are to minimize the total number of vehicles used, the total deadhead time (cost) of the operation, the operation time of the vehicles and a combination of some previous objectives.…”

Section: Some Papers About Vsp Include Implementation Of Models and Amentioning

confidence: 99%

“…In constraints (16), it is ensured that each task in CT that starts in a PI depot, ends in a PF depot. In constraints (17) and 18 (22) to (24) guarantee that a task that requires a special bus, is performed by a bus with these characteristics. (34) and (35) are the first two terms of the objective function, according to the operators' objectives:…”

Section: Mathematical Programming Formulationmentioning

confidence: 99%

A model for solving vehicle scheduling problems a case study

Baldoquín

Rengifo-Campo²

2018

Rev. Fac. Ing. Univ. Antioquia

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In this work we formulate a model to solve a type of vehicle scheduling problem, derived from the operation of the mass transit system (MIO) in Cali city, Colombia. Four companies operate the system with 3 types of buses and four depots. Two kinds of tasks should be assigned to operators' buses. A task is a sequence of consecutive travels of a route between two stations: initial and final. Each task should start and should finish in a depot, not necessarily the same. There are two main objectives defined by the operators. One objective is to minimize the total deadhead kilometers between depots and stations where tasks should start or end. The other objective is to minimize the maximum deviation of kilometers (commercial and deadhead) assigned to the operators regarding the ideal quantity of kilometers that they should have, according to the number of their buses in the fleet. We have implemented the proposed model in AMPL using Gurobi solver and we validate it using nine representative instances, generated with real data obtained from the system operation. The results obtained, in all cases, improve the solutions proposed by the company. Model variations are proposed to deal with new desirable constraints for the company. RESUMEN: En este trabajo se formula un modelo para resolver un tipo de problema de programación de vehículos, derivado de la operación del sistema de transporte masivo (MIO) en la ciudad de Cali, Colombia. Cuatro compañías operan el sistema con 3 tipos de buses y cuatro patios. Dos tipos de tareas deben asignarse a los buses de los operadores. Una tarea es una secuencia de viajes consecutivos de una ruta entre dos estaciones: inicial y final. Cada tarea debe comenzar y debe terminar en un patio, no necesariamente el mismo. Hay dos objetivos principales definidos por los operadores. Un objetivo es minimizar el total de kilómetros en vacío entre patios y estaciones donde las tareas deben comenzar o terminar. El otro objetivo es minimizar la desviación máxima de kilómetros (comercial y en vacío) asignada a los operadores con respecto a la cantidad ideal de kilómetros que deberían tener, según el número de sus buses en la flota. El modelo propuesto se implementa con el software Gurobi, utilizando nueve instancias representativas generadas con datos reales obtenidos de la operación del sistema. Todos los resultados obtenidos mejoran las soluciones propuestas por la empresa. Se proponen variaciones del modelo para considerar nuevas restricciones deseables para la empresa.

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Managing disruptions in the multi-depot vehicle scheduling problem

Cited by 17 publications

References 25 publications

Integrated optimization of urban rail transit line planning, timetabling and rolling stock scheduling

Integrated optimization of urban rail transit line planning, timetabling and rolling stock scheduling

Decomposition-based heuristic for the zoning and crop planning problem with adjacency constraints

A model for solving vehicle scheduling problems a case study

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