Abstract
In this paper we briefly present our feelings about urban logistic and its role in urban mobility. In some way, we can say that this is a position paper based on an extensive review of all known related published material. We support the development of new approaches for the management of passenger and freight transport together as a single logistics system; based on the access to more and more sophisticated flows of data and better communication means, we envisage the dissemination of sufficient information for the correct decision of every citizens between several mobility options in real time (especially with the support of mobile technology); and we sustain that new tools are needed to help the design of innovative business models and policies, and the change of habits and behaviors. We visualize urban logistics as a multi-stakeholder, multi-criteria and multimodal mobility dynamic system.

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In this work we address the Hop-Constrained Minimum cost Flow Spanning Tree (HMFST) problem with nonlinear costs. The HMFST problem is an extension of the Hop-Constrained Minimum Spanning Tree problem since it considers flow requirements other than unit flows. We propose a hybrid heuristic, based on ant colony optimization and on local search, to solve this class of problems given its combinatorial nature and also that the total costs are nonlinearly flow dependent with a fixed-charge component. We solve a set of benchmark problems available online and compare the results obtained with the ones reported in the literature for a Multi-Population hybrid biased random key Genetic Algorithm (MPGA). Our algorithm proved to be able to find an optimum solution in more than 75 % of the runs, for each problem instance solved, and was also able to improve on many results reported for the MPGA. Furthermore, for every single problem instance we were able to find a feasible solution, which was not the case for the MPGA. Regarding running times, our algorithm improves upon the computational time used by CPLEX and was always lower than that of the MPGA.

Abstract
Nowadays, organizations are often faced with the development of complex and innovative projects. This type of projects often involves performing tasks which are subject to failure. Thus, in many such projects several possible alternative actions are considered and performed simultaneously. Each alternative is characterized by cost, duration, and probability of technical success. The cost of each alternative is paid at the beginning of the alternative and the project payoff is obtained whenever an alternative has been completed successfully. For this problem one wishes to find the optimal schedule, i.e., the starting time of each alternative, such that the expected net present value is maximized. This problem has been recently proposed in Ranjbar (Int Trans Oper Res 20(2):251-266, 2013), where a branch-and-bound approach is reported. Since the problem is NP-Hard, here we propose to solve the problem using genetic algorithms.