The demand responsive transport systems (DRTS) aim to satisfy two main objectives: the service flexibility and the costs minimization. They are a good solution for the trade-off between flexibility and efficiency. They require the planning of travel paths (routing) and customers pick-up and drop-off times (scheduling) according to received requests. DRTS may operate according to a static or dynamic mode. The aim of this work is to test on a real case a heuristic for a flexible transport system with different service parameters: fleet size, vehicle capacity, time windows and incoming requests.

One of the most important problems in the coordination of the entire supply chain comes from the fact that the whole system, working on the basis of a future prediction, is strongly affected by unexpected changes in external demand and even small changes can lead to huge distortions in the management of supply to higher levels. This phenomenon is called "Bullwhip Effect". The study carried out has the purpose to analyze the occurrence of Bullwhip Effect varying the parameters of demand, but also to quantify it through a discrete event simulation model.

The concept of innovation in transport systems requires the satisfaction of two main objectives: the service flexibility and the costs minimization. The demand responsive transport systems (DRTS) seem to be the solution for the trade-off between flexibility and efficiency. They require the planning of travel paths (routing) and customers pick-up and drop-off times (scheduling) according to received requests, respecting the limited capacity of the fleet and time constraints (hard time windows) for each network's node, and the service time of the system. Even considering invariable conditions of the network a DRTS may operate according to a static or to a dynamic mode. In the static setting, all customers' requests are known beforehand and the DRTS returns routing and scheduling solutions by solving a Dial-a-Ride Problem (DaRP) instance which derives from the Pick-up and Delivery Problem with Time Windows (PDPTW). In reality, the static setting may be representative of a phase of reservation occurred the day before the execution of the service. In the dynamic mode, customers' requests arrive when the service is already running and, consequently, the solution may change whilst the vehicle is already travelling. In this mode it is necessary that the schedule is updated when each new request arrives and that this is done in a short time to ensure that the potential customer will not leave the system before a possible answer. In this work, we use an algorithm able to solve a dynamic multi-vehicle DaRP by managing incoming transport demand as fast as possible. The heuristics is a greedy method that tries to assign the requests to one of the fleet's vehicles finding each time the local optimum. The feature of this work is that, in addition to finding a plan schedule, it can be used for sizing the number of vehicles required to satisfy a percentage of demand that may be established before. Vehicles will be employed only when strictly necessary, in this way the costs will be minimized. The work is enriched by a series of tests with different values of the fleet's vehicles and their capacity, of time windows and of incoming requests' number. Finally, a set of performance indicators evaluate the solution planned by the heuristics.

A demand responsive transport system (DRTS) is a flexible system in which the stops are fixed and tours are variable. Client takes a reservation about a trip which could be in the same day or in the next. DRTS can be analyzed by two kind of dynamisms. The first is about the way in which requests arrives to the system. In this case DRTS could be static if the algorithm runs after received all the requests, or can be dynamic on line if the algorithm runs while the requests are arriving to the system. The second kind of dynamism is about when the requests are served: on reservation or on service.

The realization of innovative transport services, require increasingly greater flexibility and inexpensiveness of the service. In many cases the solution is to realize a demand responsive transportation system, in which there are two main goals: minimize costs and maximize flexibility. In this work, we address a Demand Responsive Transport System capable of managing incoming transport demand using a solution based on an insertion heuristics to solve an On-Line dynamic DaRP instance. The solutions provided by the heuristics are simulated dynamically in a discrete events environment in which it is possible to reproduce the movement of the vehicles, the passengers' arrival to the stops, the delays due to the traffic congestion and possible anomalies in the behavior of the passengers. Finally, at the end of the simulation, a set of performance indicators summarize the solution planned by the heuristics.

The concept of innovation in transport systems requires the satisfaction of two main objectives: flexibility and costs minimization. The demand responsive transport systems (DRTS) seem to be the solution for the trade-off between flexibility and efficiency. They require the planning of travel paths (routing) and customers pick-up and drop-off times (scheduling) according to received requests and respecting the limited capacity of the fleet and time constraints (hard time windows) for each networks node. Even considering invariable conditions of the network a DRTS may operate according to a static or to a dynamic mode. In the dynamic mode, customers requests arrive when the service is already running and, consequently, the solution may change over time. In this work, we use an algorithm able to solve a dynamic multi-vehicle DaRP by managing incoming transport demand as fast as possible. The heuristics is a greedy method that tries to assign the requests to one of the fleets vehicles finding each time the local optimum. The usage of vehicles only when strictly necessary, provides to costs minimization. The work is enriched by a series of tests with different values of the fleets vehicles and their capacity, of time windows and of incoming requests number. The solutions provided by the heuristics are simulated in a discrete events environment in which its possible to reproduce the movement of the buses, the passengers' arrival to the stops, and in the next step the delays due to the traffic congestion and possible anomalies in the behaviour of the passengers. Finally, at the end of the simulation, a set of performance indicators evaluate the solution planned by the heuristics.

The realization of innovative transport services requires greater flexibility and inexpensive service. In many cases the solution is to realize demand responsive transportation system. A Demand Responsive Transport System (DRTS) requires the planning of travel paths (routing) and customer pick-up and drop-off times (scheduling) according to received requests. In particular, the problem has to deal with multiple vehicles, limited capacity of the fleet vehicles and temporal constraints (time windows). A DRTS may operate according to static or dynamic mode. In the static setting, all the customer requests are known beforehand and the DRTS solves a Dial-a-Ride Problem (DaRP) instance, to produce the tour of each bus, respecting the pick up and delivery time windows while minimising the solution cost. In the dynamic mode, the customer requests arrive over time to a control station and, consequently, the solution may also change over time. In this work, we address a Demand Responsive Transport System capable of managing incoming transport demand using a two-stage algorithm by solving a DaRP instance. The solutions provided by the heuristics are simulated in a discrete events environment in which it is possible to reproduce the movement of the buses, the passengers' arrival to the stops, the delays due to the traffic congestion and possible anomalies in the behaviour of the passengers. Finally, a set of performance indicators evaluate the solution planned by the heuristics. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Organizing Committee.

Vehicle routing and scheduling are two main issues in the hazardous material (hazmat) transportation problem. In this paper, we study the problem of managing a set of hazmat transportation requests in terms of hazmat shipment route selection and actual departure time definition. For each hazmat shipment, a set of minimum and equitable risk alternative routes from origin to destination points and a preferred departure time are given. The aim is to assign a route to each hazmat shipment and schedule these shipments on the assigned routes in order to minimize the total shipment delay, while equitably spreading the risk spatially and preventing the risk induced by vehicles traveling too close to each other. We model this hazmat shipment scheduling problem as a job-shop scheduling problem with alternative routes. No-wait constraints arise in the scheduling model as well, since, supposing that no safe area is available, when a hazmat vehicle starts traveling from the given origin it cannot stop until it arrives at the given destination. A tabu search algorithm is proposed for the problem, which is experimentally evaluated on a set of realistic test problems over a regional area, evaluating the provided solutions also with respect to the total route risk and length.

This paper deals with the generation of minimal risk paths for the road transportation of hazardous materials between an origin–destination pair of a given regional area. The main considered issue is the selection of paths that minimize the total risk of hazmat shipments while spreading the risk induced on the population in an equitable way. The problem is mathematically formulated, and two heuristic algorithms are proposed for its solution. Substantially, these procedures are modified versions of Yen's algorithm for the k-shortest path problem, which take into due consideration the risk propagation resulting from close paths and spread the risk equitably among zones of the geographical region in which the transportation network is embedded. Furthermore, a lower bound based on a Lagrangean relaxation of the given mathematical formulation is also provided. Finally, a series of computational tests, referring to a regional area is reported.

In this work, we address a Demand Responsive Transport System capable of managing incoming transport demand using a solution architecture based on a two- stage algorithm to solve a Dial-a-Ride Problem instance. In the first stage, a constructive heuristic algorithm quickly provides a feasible solution to accept the incoming demand. The algorithm in the second stage is a specialized Hybrid Genetic Algorithm that attempts to improve the solution evaluated at the first stage by using the time between two consecutive transportation events.

This paper focuses on a new method to compute fitness function (ff) values in genetic algorithms for bus network optimization. In the proposed methodology, a genetic algorithm is used to generate iteratively new populations (sets of bus networks). Each member of the population is evaluated by computing a number of performance indicators obtained by the analysis of the assignment of the O/D demand associated to the considered networks. Thus, ff values are computed by means of a multicriteria analysis executed on the performance indicators so found. The goal is to design a heuristic algorithm that allows to achieve the best bus network satisfying both the transport demand and supply.

An integrated software tool environment is presented, and a methodology is proposed for the operational support of the local authority, for analysis of the impact of transport measures in terms of network energy consumption and pollutant emissions. It is based on work done by the European Union within the save program (speci®c actions for vigorous energy eciency)ÐSlam project (supporting local authorities methodology). As background, the Slam project is described, with the principal aspects and needs of environmental and trac network management. The central section de®nes a methodology able to support technicians in recognizing the trac asset and decision makers in evaluating interventions on urban transport infrastructures or technological systems. The role of the di€erent models and their interactions with the transport telematics services currently active on the Florence (Italy) network is discussed. Finally, the procedure for calculating the trac impacts on energy consumption is described with the help of a test case, the evaluation of a dedicated bus corridor in Florence. # 1999 Published by Elsevier Science Ltd. All rights reserved.

With road traffic in Europe forecast to increase, strategies are needed to keep transportation sector growth within the bounds imposed by a sustainable development. Research is contributing through a large number of projects dealing with transport-environment interactions. This paper reviews international research activities in this field, focusing on technological innovations, air and noise pollution prediction models, and existing tools for socioeconomic evaluation of traffic impacts on the environment. In particular, research projects of the Second Special Project on Transport (PFT2) of the Italian National Research Council (CNR) are outlined.