http://dl.gi.de/handle/20.500.12116/10599 2026-07-22T22:10:52Z 2026-07-22T22:10:52Z Bazi, Gabriel Khoury, John Srour, F. Jordan http://dl.gi.de/handle/20.500.12116/10722 2018-03-26T09:09:41Z 2017-01-01T00:00:00Z

Integrating Data Collection Optimization into Pavement Management Systems Bazi, Gabriel; Khoury, John; Srour, F. Jordan This paper describes a method for using location data to optimize the routing of pavement data collection vehicles. In much of the developed world, pavement testing is performed on a regular basis; the pavement testing data, in turn, serves as input to Pavement Management Systems. Currently, in the United States of America, state departments of transportation plan this data collection work by providing the list of roads that must be tested and then leave the routing of the vehicles to the equipment operators who typically execute the work in an ad hoc manner. This study presents the processes required to code the list of roads for testing, select appropriate hotels in the region of testing, and apply a Traveling Salesman Problem with Hotel Stops model to derive a route. Applying the processes to a case study shows significant cost savings associated with this method of roadway testing, as opposed to the current ad hoc methods.

2017-01-01T00:00:00Z Ulmer, Marlin W. Heilig, Leonard Voß, Stefan http://dl.gi.de/handle/20.500.12116/10720 2018-03-26T09:09:41Z 2017-01-01T00:00:00Z

On the Value and Challenge of Real-Time Information in Dynamic Dispatching of Service Vehicles Ulmer, Marlin W.; Heilig, Leonard; Voß, Stefan Ubiquitous computing technologies and information systems pave the way for real-time planning and management. In the process of dynamic vehicle dispatching, the adherent challenge is to develop decision support systems using real-time information in an appropriate quality and at the right moment in order to improve their value creation. As real-time information enables replanning at any point in time, the question arises when replanning should be triggered. Frequent replanning may lead to efficient routing decisions due to vehicles’ diversions from current routes while less frequent replanning may enable effective assignments due to gained information. In this paper, the authors analyze and quantify the impact of the three main triggers from the literature, exogenous customer requests, endogenous vehicle statuses, and replanning in fixed intervals, for a dynamic vehicle routing problem with stochastic service requests. To this end, the authors generalize the Markov-model of an established dynamic routing problem and embed the different replanning triggers in an existing anticipatory assignment and routing policy. They particularly analyze under which conditions each trigger is advantageous. The results indicate that fixed interval triggers are inferior and dispatchers should focus either on the exogenous customer process or the endogenous vehicle process. It is further shown that the exogenous trigger is advantageous for widely spread customers with long travel durations and few dynamic requests while the endogenous trigger performs best for many dynamic requests and when customers are accumulated in clusters.

2017-01-01T00:00:00Z Kliewer, Natalia Ehmke, Jan Fabian Mattfeld, Dirk Christian http://dl.gi.de/handle/20.500.12116/10715 2018-03-26T09:09:41Z 2017-01-01T00:00:00Z

Computational Mobility, Transportation, and Logistics Kliewer, Natalia; Ehmke, Jan Fabian; Mattfeld, Dirk Christian

2017-01-01T00:00:00Z Nofer, Michael Gomber, Peter Hinz, Oliver Schiereck, Dirk http://dl.gi.de/handle/20.500.12116/10716 2018-03-26T09:09:41Z 2017-01-01T00:00:00Z

Blockchain Nofer, Michael; Gomber, Peter; Hinz, Oliver; Schiereck, Dirk

2017-01-01T00:00:00Z