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Society is becoming more conscious on the need to preserve the environment. Sustainable development schemes have grown rapidly as a tool for managing, predicting and improving the growth path in different regions and economy sectors. We introduce a novel and simple mathematical model of ordinary differential equations (ODEs) in order to obtain a dynamical description for each one of the sustainability components (economy, social development and environment conservation), together with their dependence with demographic dynamics. The main part in the modeling task is inspired by the works by Cobb, Douglas, Brander and Taylor. This is completed through some new insights by the authors. A model application is presented for three specific geographical rural regions in Caldas (Colombia).

Car sharing holds a promise of reducing traffic congestion and pollution in cities as well as of boosting the use of public transport when used as a last-mile solution in a multimodal transportation scenario. Despite this huge potential, several problems related to the deployment and operations of car sharing systems have yet to be fully addressed. In this work, we focus on station-based car sharing and we define an optimization problem for the deployment of its stations. The goal of this problem is to find the minimum cost deployment (in terms of number of stations and their capacity) that can guarantee a pre-defined level of service to the customers (in terms of probability of finding an available car/parking space). This problem combines insights from queueing theory (used to model the stochastic demand for cars/parking spaces at the stations) with a variant of the classical set covering problem. For its evaluation, we use a trace of more than 100,000 pickup and drop-off events at a free-floating car sharing service in The Netherlands, which are used to model the input demand of the car sharing system. Our results show that the proposed solution is able to strike the right balance between cost minimisation and quality of service, outperforming three alternative schemes used as benchmarks.

Intrinsic characteristics of food products and processes along with growing sustainability concerns lead to the need for decision support tools that can integrate economic considerations with quality preservation and environmental protection in food supply chains. In this study, we develop a multi-objective linear programming (MOLP) model for a generic beef logistics network problem. The objectives of the model are (i) minimizing total logistics cost and (ii) minimizing total amount of greenhouse gas emissions from transportation operations. The model is solved with the ee-constraint method. This study breaks away from the literature on logistics network models by simultaneously considering transportation emissions (affected by road structure, vehicle and fuel types, weight loads of vehicles, traveled distances), return hauls and product perishability in a MOLP model. We present computational results and analysis based on an application of the model on a real-life international beef logistics chain operating in Nova Andradina, Mato Grosso do Sul, Brazil and exporting beef to the European Union. Trade-off relationships between multiple objectives are observed by the derived Pareto frontier that presents the cost of being sustainable from the point of reducing transportation emissions. The results from the pie chart analysis indicate the importance of distances between actors in terms of environmental impact. Moreover, sensitivity analysis on practically important parameters shows that export ports' capacities put pressure on the logistics system; decreasing fuel efficiency due to the bad infrastructure has negative effects on cost and emissions; and green tax incentives result in economic and environmental improvement.