Application of mathematical models for the optimization and improvement of related logistics processesreduction of carbon footprint and improvement of efficient driving

Abstract

This doctoral dissertation has been presented in the form of thesis by publicationThe current PhD thesis presented by Mr. Adrián Valverde Mateo, Chief of Research duties at Primarfio Logistics Group in Spain defended under the label of industrial and international doctorate has several aims. Grupo Primafrio is a leader company in the movement of refrigerated goods. This company is a reference in Europe in road transport and logistics sector. Obviously, the optimization of their processes in terms of economic saving, reduction of carbon footprint and efficient driving can be the main challenges in recent days. Therefore the current PhD thesis has had two different sets of objectives. The first big objective deals with the optimization approach at the decision making needed at the logistics process. In this sense, there was an open problem regarding the indecisiveness in choice data that we treat by using Maxwell distribution to handle selector’s indecisiveness in choice data, in fact we propose a new latent Bayesian choice model. On the other hand, in the same frame of problems, we deal with the assistance at the decision making at the binary case, in this sense we propose a generalization of choice models for the binary scenario. These two contributions constitute the content of Papers 1 and 2. Second block of problems deals with the open question of measuring how efficient driving reduce the footprint carbon in terms of saving fuel. Therefore, we state a reactive model where we can analyze the impact of driver at the process. We are able to solve this model by creating a swarming Meyer wavelet. The conclusions were that for the same level of traffic a better efficiency at driving reduce costs. When traffic level is high this save is even more important, therefore make not sense to reduce cost invest in the driving formation on efficient driving. This item is presented at Paper 3. Finally, in Paper 4, we have developed a global model of traffic where we combine for first time the effect of train in the logistic process of moving goods. As colaborate at this aim with Professor Huatao Chen from Division of Dynamics and Control, School of Mathematics and Statistics, Shandong University of Technology, Zibo 255000, China, Head of the Laboratory of Traffic Control we have used the data of Shandong Province in China which has a deep railway infrastructure. This model is exportable to other part of the work with a similar operational complexity. In this sense, it is shown that highway operation as well as rail transit promotes the development of traffic, while traffic accidents inhibit traffic development. More over, the maximum error between the output data and the statistics bureau, based on which some forecasts for the development of traffic in the future are given, is obtained, some suggestions and optimization schemes for traffic development are given. Finally, a neural network model of the development of Shandong traffic is also derived.