On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services

The operation of a demand responsive transport service usually involves the management of dynamic requests. The underlying algorithms are mainly adaptations of procedures carefully designed to solve static versions of the problem, in which all the requests are known in advance. However there is no guarantee that the effectiveness of an algorithm stays unchanged when it is manipulated to work in a dynamic environment. On the other hand, the way the input is revealed to the algorithm has a decisive role on the schedule quality. We analyze three characteristics of the information flow (percentage of real-time requests, interval between call-in and requested pickup time and length of the computational cycle time), assessing their influence on the effectiveness of the scheduling proces

Design and operational control of an AGV system

In this paper we first deal with the design and operational control of Automated Guided Vehicle (AGV) systems, starting from the literature on these topics. Three main issues emerge: track layout, the number of AGVs required and operational transportation control. An hierarchical queueing network approach to determine the number of AGVs is decribed. Also basic concepts are presented for the transportation control of both a job-shop and a flow-shop. Next we report on the results of a case study, in which track layout and transportation control are the main issues. Finally we suggest some topics for further research

Vehicle Routing with Traffic Congestion and Drivers' Driving and Working Rules

For the intensively studied vehicle routing problem (VRP), two real-life restrictions have received only minor attention in the VRP-literature: traffic congestion and driving hours regulations. Traffic congestion causes late arrivals at customers and long travel times resulting in large transport costs. To account for traffic congestion, time-dependent travel times should be considered when constructing vehicle routes. Next, driving hours regulations, which restrict the available driving and working times for truck drivers, must be respected. Since violations are severely fined, also driving hours regulations should be considered when constructing vehicle routes, even more in combination with congestion problems. The objective of this paper is to develop a solution method for the VRP with time windows (VRPTW), time-dependent travel times, and driving hours regulations. The major difficulty of this VRPTW extension is to optimize each vehicle’s departure times to minimize the duty time of each driver. Having compact duty times leads to cost savings. However, obtaining compact duty times is much harder when time-dependent travel times and driving hours regulations are considered. We propose a restricted dynamic programming (DP) heuristic for constructing the vehicles routes, and an efficient heuristic for optimizing the vehicle’s departure times for each (partial) vehicle route, such that the complete solution algorithm runs in polynomial time. Computational experiments emonstrate the trade-off between travel distance minimization and duty time minimization, and illustrate the cost savings of extending the depot opening hours such that traveling before the morning peak and after the evening peak becomes possible

Planning and Scheduling Transportation Vehicle Fleet in a Congested Traffic Environment

Transportation is a main component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routing is a crucial management problem. Despite numerous publications dealing with efficient scheduling methods for vehicle routing, very few addressed the inherent stochastic nature of travel times in this problem. In this paper, a vehicle routing problem with time windows and stochastic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture the stochastic behavior of travel times. A case study is used both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment which is often the case on the european road networkstransportation; vehicle fleet; planning; scheduling; congested traffic

Un modelo para resolver el problema dinámico de despacho de vehículos con incertidumbre de clientes y con tiempos de viaje en arcos

Indexación: Web of Science; ScieloIn a real world case scenario, customer demands are requested at any time of the day requiring services that are not known in advance such as delivery or repairing equipment. This is called Dynamic Vehicle Routing (DVR) with customer uncertainty environment. The link travel time for the roadway network varies with time as traffic fluctuates adding an additional component to the dynamic environment. This paper presents a model for solving the DVR problem while combining these two dynamic aspects (customer uncertainty and link travel time). The proposed model employs Greedy, Insertion, and Ant Colony Optimization algorithms. The Greedy algorithm is utilized for constructing new routes with existing customers, and the remaining two algorithms are employed for rerouting as new customer demands appear. A real world application is presented to simulate vehicle routing in a dynamic environment for the city of Taipei, Taiwan. The simulation shows that the model can successfully plan vehicle routes to satisfy all customer demands and help managers in the decision making process.En un escenario real, los pedidos de los clientes son solicitados a cualquier hora del día requiriendo servicios que no han sido planificados con antelación tales como los despachos o la reparación de equipos. Esto es llamado ruteo dinámico de vehículos (RDV) considerando un ambiente con incertidumbre de clientes. El tiempo de viaje en una red vial varía con el tiempo a medida que el tráfico vehicular fluctúa agregando una componente adicional al ambiente dinámico. Este artículo propone un modelo para resolver el problema RDV combinando estos dos aspectos dinámicos. El modelo propuesto utiliza los algoritmos Greedy, Inserción y optimización basada en colonias de hormigas. El algoritmo Greedy es utilizado para construir nuevas rutas con los clientes existentes y los otros dos algoritmos son usados para rutear vehículos a medida que surjan nuevos clientes con sus respectivos pedidos. Además, se presenta una aplicación real para simular el ruteo vehicular en un ambiente dinámico para la ciudad de Taipei, Taiwán. Esta simulación muestra que el modelo es capaz de planificar exitosamente las rutas vehiculares satisfaciendo los pedidos de los clientes y de ayudar los gerentes en el proceso de toma de decisiones.http://ref.scielo.org/3ryfh