Optimization and Control (math.OC)

Abstract: Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger costs were found to be the most significant component in the objective function, with an 80\% decrease resulting in a 25\% cost reduction. VOT costs decrease substantially as energy costs increase. The number of fast chargers increases as VOT doubles. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible.

Abstract: We develop a dynamic model of cascading failures in a financial network whereby cross-holdings are viewed as feedback, external assets investments as inputs and failure penalties as static nonlinearities. We provide sufficient milder and stronger conditions for the system to be a positive one, and study equilibrium points and stability. Stability implies absence of cascades and convergence of market values to constant values. We provide a constructive method for control design to obtain stabilizing market investments in the form of feedback-feedforward control inputs.

Abstract: In this paper, we present a methodology based on a multiobjective optimization suggesting which facility to implement, in which location, and at which time. In this context, we define a new elicitation procedure to handle Decision Makers (DMs) preferences with an intrinsic and more general interest that goes beyond the specific decision problem. In particular, the user's preferences are elicited by conjugating the deck of cards method with the ordinal regression approach allowing the DM to provide preference information in terms of ranking and pairwise comparing with regard to the intensity of preference of some solutions of the optimization problem. Then, the score of the reference solutions obtained through the deck of the cards method is used as a basis for an ordinal regression procedure that, to take into account interaction between criteria, represents DM's multicriteria preferences by means of a value function expressed in terms of a Choquet Integral. The obtained value function is then used to define a multiobjective optimization problem. The new feasible solutions obtained by the resolution of the optimization problem are proposed to the DM to verify his appreciation and collect further new preference information to iterate the interaction procedure ending when the DM is satisfied of the proposed solution. We apply our methodology to a real world problem to handle the planning procedure of a sustainable Ecovillage in the province of Turin (Italy). We consider a set of facilities to be distributed in a given space in a proper temporal sequence that we conveniently formulated in terms of the space time model introduced by Barbati et al. (2020). We interact with the President of the cooperative owning the Ecovillage to detail what facilities of the Ecovillage should be selected among the proposed ones, where they should be located, and when they should be planned.