Real-Time Scheduling Approaches for Vehicle-Based Internal Transport Systems

In this paper, we study the problem of scheduling and dispatching vehicles in vehicle-based internal transport systems within warehouses and production facilities. We develop and use two rolling horizon policies to solve real-time vehicle scheduling problems. To solve static instances of scheduling problems, we propose two new heuristics: combined and column-generation heuristics. We solve a real-time scheduling problem by applying a heuristic to dynamically solve a series of static instances under a rolling horizon policy. A rolling horizon can be seen either as a fixed-time interval in which advance information about loads’ arrivals is available, or as a fixed number of loads which are known to become available in the near future. We also propose a new look-ahead dynamic assignment algorithm, a different dynamic vehicle-scheduling approach. We evaluate these dynamic scheduling strategies by comparing their performance with that of two of the best online vehicle dispatching rules mentioned in the literature. Experimental results show that the new look-ahead dynamic assignment algorithm and dynamic scheduling approaches consistently outperform vehicle dispatching rules

Performance Evaluation of Real-time Scheduling Approaches in Vehicle-based Internal Transport Systems

This paper studies the performance of static and real-time scheduling approaches in vehicle-based internal transport (VBIT) systems, which can be found in manufacturing and warehouse facilities. We propose three heuristic approaches for static VBIT problems (insertion, combined and column generation), extend them to a dynamic, real-time setting and compare their performance over a rolling time horizon. This time horizon can be seen either as a fixed-time interval in which advance information about loads’ arrivals is available, or as a fixed number of loads which are known to become available in the near future. We also propose two dynamic assignment approaches: with and without look-ahead, respectively. Performance (primarily average load waiting time) of the above five dynamic scheduling approaches is compared with two nearest-vehicle-first rules (with and without look-ahead), which are the best vehicle dispatching rules known from literature and which are commonly used in practice. Experimental results show that, if sufficient prior information is available, our dynamic scheduling approaches consistently outperform vehicle dispatching rules. Results also reveal that guide-path layout, load arrival rate and variance, and amount of load pre-arrival information have strong impacts on the performance of vehicle control approaches. Column generation or the combined heuristics are recommended in small or medium-scale VBIT systems, whereas for large scale VBIT systems, both the combined heuristic and the dynamic assignment approach with look ahead perform best

Comparison of heuristic approaches for the multiple depot vehicle scheduling problem

Given a set of timetabled tasks, the multi-depot vehicle scheduling problemis a well-known problem that consists of determining least-cost schedulesfor vehicles assigned to several depots such that each task is accomplishedexactly once by a vehicle. In this paper, we propose to compare theperformance of five different heuristic approaches for this problem,namely, a heuristic \\mip solver, a Lagrangian heuristic, a columngeneration heuristic, a large neighborhood search heuristic using columngeneration for neighborhood evaluation, and a tabu search heuristic. Thefirst three methods are adaptations of existing methods, while the last twoare novel approaches for this problem. Computational results on randomlygenerated instances show that the column generation heuristic performs thebest when enough computational time is available and stability is required,while the large neighborhood search method is the best alternative whenlooking for a compromise between computational time and solution quality.tabu search;column generation;vehicle scheduling;heuristics;Lagrangian heuristic;large neighborhood search;multiple depot

Hacia la integración de la resincronización en el problema de programación por tipo de vehículo

In this paper, we propose an integer linear programming (ILP) aiming at optimizing timetabling generation and the Vehicle Type Scheduling Problem (VTSP), based on a time-space network (TSN). The model was defined as Vehicle Type Scheduling Problem with Sequential Changes of timetable (VTSP- SCT). Additionally, we developed a new methodology to insert time window to the proposed problem based on small changes on the TSN structure, with easy computational implementation and optimal solution at low computation run-times. By including small changes to the timetable and/or including time windows for timetabling trips, we introduced flexibility levels in the departure times of trips, resulting in operational advantages for the service provider. Since we use a very short time window interval, the current timetable is only slightly modified, minimally changing the passenger routines. The developed approaches were tested using random instances based on a Brazilian city. The VTSP-SCT with and without time windows have resulted in relevant savings in the daily operations of the public transportation service, reducing the required number of scheduled vehicles to carry out the historic demand.En este documento, proponemos una programación lineal entera (ILP) con el objetivo de optimizar la generación de horarios y el problema de programación de tipo de vehículo (VTSP), basado en una red de tiempo y espacio (TSN). El modelo se definió como un problema de programación de tipo de vehículo con cambios secuenciales de horario (VTSP-SCT). Además, desarrollamos una nueva metodología para insertar una ventana de tiempo al problema propuesto en base a pequeños cambios en la estructura TSN, con una implementación computacional sencilla y una solución óptima en tiempos de ejecución de computación bajos. Al incluir pequeños cambios en el cronograma y/o incluir ventanas de tiempo para programar viajes, introdujimos niveles de flexibilidad en los horarios de salida de los viajes, lo que resulta en ventajas operacionales para el proveedor del servicio. Dado que usamos un intervalo de ventana de tiempo muy corto, el horario actual solo se modifica ligeramente, cambiando mínimamente las rutinas de los pasajeros. Los enfoques desarrollados fueron probados usando instancias aleatorias basadas en una ciudad brasileña. El VTSP-SCT con y sin ventanas de tiempo ha resultado en ahorros relevantes en las operaciones diarias del servicio de transporte público, reduciendo el número requerido de vehículos programados para llevar a cabo la demanda histórica

Comparison of heuristic approaches for the multiple depot vehicle scheduling problem

Given a set of timetabled tasks, the multi-depot vehicle scheduling problem is a well-known problem that consists of determining least-cost schedules for vehicles assigned to several depots such that each task is accomplished exactly once by a vehicle. In this paper, we propose to compare the performance of five different heuristic approaches for this problem, namely, a heuristic \\mip solver, a Lagrangian heuristic, a column generation heuristic, a large neighborhood search heuristic using column generation for neighborhood evaluation, and a tabu search heuristic. The first three methods are adaptations of existing methods, while the last two are novel approaches for this problem. Computational results on randomly generated instances show that the column generation heuristic performs the best when enough computational time is available and stability is required, while the large neighborhood search method is the best alternative when looking for a compromise between computational time and solution quality

The Vehicle Rescheduling Problem with Retiming

When a vehicle breaks down during operation in a public transportation system, the remaining vehicles can be rescheduled to minimize the impact of the breakdown. In this paper, we discuss the vehicle rescheduling problem with retiming (VRSPRT). The idea of retiming is that scheduling flexibility is increased, such that previously inevitable cancellations can be avoided. To incorporate delays, we expand the underlying recovery network with retiming possibilities. This leads to a problem formulation that can be solved using Lagrangian relaxation. As the network gets too large, we propose an iterative neighborhood exploration heuristic to solve the VRSPRT. This heuristic allows retiming for a subset of trips, and adds promising trips to this subset as the al

Resolução integrada de problemas do planejamento do transporte público: foco na tabela de horários e no escalonamento de veículos com frota heterogênea

In this paper, we propose a new Integer Linear Programming model, based on a time-space network that integrates the timetable generation problem and the vehicle scheduling problem with heterogeneous fleet. A difference of this approach consists in considering the demand for the timetable redefinition and the vehicle scheduling, factor rarely applied in optimization models of the transportation system. We applied real and large random instances. The results indicate that the model may contribute to optimizing the public transport planning leading to significant savings in terms of scheduled vehicles. Moreover, as the timetable changes are fairly short, it is slightly modified, minimally modifying the passengers routine, which enables the application of these approaches to real context.Neste artigo, propõe-se um novo modelo de Programação Linear Inteira, baseado em uma rede tempo-espaço, que integra os problemas de geração da tabela de horários e o escalonamento de veículos com frota heterogênea. Um diferencial dessa abordagem consiste na consideração da demanda para a redefinição da tabela de horários e para o escalonamento dos veículos, fator raramente aplicado nos modelos de otimização do sistema de transporte. Foram utilizadas instâncias reais e aleatórias de grande porte. Os resultados indicam que o modelo pode contribuir para a otimização do planejamento do transporte público, tendo em vista que possibilita economias significativas no número de veículos escalonados. Além disso, como os intervalos de alteração da tabela de horários são bastante curtos, obtêm-se alterações sutis, modificando minimamente a rotina dos passageiros, o que possibilita a aplicação dessa abordagem ao contexto real

Uma proposta para o problema de escalonamento de veículos sob o paradigma de queda de demanda

Este trabalho apresenta dois modelos diferentes para o problema de escalonamento de ônibus em sistemas de trânsito, considerando a perda de passageiros. Est pesquisa é motivad a a pela perda considerável na demanda por transporte de ônibus no Brasil nas últimas décadas. O principal objetivo dos modelos é permitir que os operadores de trânsito ajustem a frota dada a perda de passageiros. Ao agrupar as viagens dentro de pequenos intervalos de tempo no horário, o pico de demanda das viagens agrupadas é ajustado à frota, defi nindo uma atribuição de menor custo para uma demanda decrescente de passageiros. Dois diferentes agrupamentos foram criados, abordagens sequenciais e combinatórias, formuladas como modelos inteiros de programação linear. Devido à complexidade da última abo rdagem, um algoritmo baseado em geração de colunas foi projetado. Nós os comparamos entre si e com a solução do problema tradicional de programação de veículos, usando dados reais de uma empresa de transporte no sul do Brasil e grandes instâncias geradas a leatoriamente, considerando diferentes perfis de demanda e intervalos de viagem de agrupamento aceitáveis. Os resultados dos modelos permitiram economia no escalonamento de veículos, mantendo bons níveis de serviço aos passageiros. Os modelos provaram apoi ar a tomada de decisão no planejamento do transporte público, considerando diferentes cenários operacionais.This research work presents two different models for the bus scheduling problem in transit systems, considering loss in ridership. This research is motivated by the considerable loss in the demand for bus transport in Brazil in the last decades. The main objective of the models is to allow transit operators to adjust the fleet given loss of passengers. By grouping trips within small time intervals in the timetabling, the peak demand of the grouped trips is adjusted to the fleet, defining a lower cost assignment for a fallen demand of passengers. Two different groupings were devised, sequential and combinatorial approaches, formulated as integer linear programming models. Due to the complexity of the latter approach, a column generation based algorithm has been designed. We compared them with each other and with the solution of the traditional vehicle scheduling problem, using real world data from a transportation company in southern Brazil and large random generated instances, considering different demand profiles and acceptable grouping trip intervals. The results of models allowed savings in the vehicle scheduling, while keeping good service levels to passengers. The models proved to support decision making in the planning of public transport, considering diferente operational scenarios