Sensors and intelligent applications enabling smart vehicular traffic create an opportunity for improving the welfare of people, from the viewpoints of efficiency, sustainability, and social inclusivity. Like the opportunities, challenges of such an endeavour are multifaceted, including the scalable collection and processing of the hefty data volumes generated by sensors, and the coordinated operation between selfish agents. The purpose of this work is to survey recent literature with an emphasis on applications and a multidisciplinary eye, with the aim of stimulating discussion and reflection in the scientific communities. The principal application areas of smart traffic and smart mobility are discussed, synthesizing different perspectives. Many intriguing areas for future research exist besides those relative to connectivity, data fusion, and privacy. Some research challenges pertinent to sustainability, insurance, simulation and the handling of ambiguous information are highlighted.

This document presents a methodology for the optimal selection of the vehicle fleet necessary to distribute medical products from the company’s storage center to many intermediate transfer nodes, and from these to a group of public hospitals. The problem addressed is a real case presented by a company that provides logistics services in the Mexican Republic. An algorithm that incorporates three modified mathematical models was designed for its solution. A version of the Dijkstra algorithm was modified for the solution to develop the clustering of nodes (clinics) within the system. A modified version of the model of the traveling salesman generated the routes of the transports and the sequence of distribution of the products. Finally, a model of mixed integer linear programming provided the quantities and characteristics of the transports to buy.

Today, there are a wide variety of ways to produce goods in a manufacturing company. Among the most common are mass or line production and process production, both of which are antagonists. In an online production system, materials move from station to station, receiving added value on a well-defined layout. In a production line by process, the materials randomly visit a set of machines strategically located in order to receive a treatment, almost always through metalwork machines, according to the final product of which they will be part. In this case, there is not a predefined layout, as the incoming materials are sectioned and each piece forms a continuous flow through different workstations to receive some process. This activity depends on the function of the product and its final destination as a component of a finished product. In this proposal, Markov chain theory is used to model a manufacturing system by process in order to obtain the expected values of the average production per machine, the total expected production in all the facilities, the leisure per machine and the total productive efficiency of the system, among other indicators. In this research, we assume the existence of historical information about the use of the equipment, its failures, the causes of failure and their repair times; in any factory, this information is available in the area of manufacturing engineering and plant engineering. From this information, statistical frequency indicators are constructed to estimate transition probabilities, from which the results presented here are derived. The proposal is complemented with a numerical example of a real case obtained from a refrigerator factory established in Mexico in order to illustrate the results derived from this research. The results obtained show their feasibility when successfully implemented in the company.

This document presents an economic optimization model which identifies the location, the nominal plant capacity and the operation scheduling for set of biorefineries of second-generation ethanol using the biomass obtained as waste in the sugarcane industry. The model also determines the gasoline volumes that will be mixed with ethanol in order to produce a mixed fuel. Given a planning horizon of the operation of the system, the model obtains its optimal parameters at fixed time intervals (annual) so the global optimum is obtained by minimizing the mathematical expectation of the stochastic process generated when the product demand is assumed random with known density. Partial optimization of the process is achieved using a mixed integer linear programming model. Real information obtained from the Secretariat of Energy for the management of biorefineries in the state of Veracruz of the Mexican Republic is included and numerical results are reported.

In this research, we develop an extension of the stochastic routing model with a fixed capacity for the distribution of perishable products with a time window. We use theoretical probability distributions to model the life of transported products and travel times in the network. Our main objective is to maximize the probability of delivering products within the established deadline with a certain level of customer service. Our project is justified from the perspective of reducing the pollution caused by greenhouse gases generated in the process. To optimize the proposed model, we use a Generic Random Search Algorithm. Finally, we apply the idea to a real problem of designing strategies for the optimal management of perishable food distribution routes that involve a time window, the objective being to maximize the probability of meeting the time limit assigned to the route problem by reducing, in this way, the pollution generated by refrigerated transport.