Many cities and towns offer nowadays to citizens a bike sharing system (BSS). When a company star... more Many cities and towns offer nowadays to citizens a bike sharing system (BSS). When a company starts the service, several decisions have to be taken on the location and size of the rental stations, and the number of vehicles to use to re-balance the bikes in the stations, in addition to the cost and policies for the payment of the service. Also, when the service is in place, it is often necessary to modify it, in many cases to expand it. In this paper, starting from the experience gained in a real-case application, we present a simulation framework to support the tactical decisions in the design or revision of a BSS. We will also present the application of the framework to the case of Bicimia in Brescia, Italy.
The class of inventory routing problems (IRP) includes a variety of different optimization prob-l... more The class of inventory routing problems (IRP) includes a variety of different optimization prob-lems that, though often very different from each other, all consider a routing and an inventory component of an optimization problem. Time may be discrete or continuous, demand may be deterministic or stochastic, inventory holding costs may be accounted for in the objective
Many cities and towns offer nowadays to citizens a bike sharing system (BSS). When a company star... more Many cities and towns offer nowadays to citizens a bike sharing system (BSS). When a company starts the service, several decisions have to be taken on the location and size of the rental stations, and the number of vehicles to use to re-balance the bikes in the stations, in addition to the cost and policies for the payment of the service. Also, when the service is in place, it is often necessary to modify it, in many cases to expand it. In this paper, starting from the experience gained in a real-case application, we present a simulation framework to support the tactical decisions in the design or revision of a BSS. We will also present the application of the framework to the case of Bicimia in Brescia, Italy.
The class of inventory routing problems (IRP) includes a variety of different optimization prob-l... more The class of inventory routing problems (IRP) includes a variety of different optimization prob-lems that, though often very different from each other, all consider a routing and an inventory component of an optimization problem. Time may be discrete or continuous, demand may be deterministic or stochastic, inventory holding costs may be accounted for in the objective
The Enhanced Index Tracking Problem (EITP) calls for the determination of an optimal portfolio of... more The Enhanced Index Tracking Problem (EITP) calls for the determination of an optimal portfolio of assets with the bi-objective of maximizing the excess return of the portfolio above a benchmark and, simultaneously, minimizing the tracking error. The EITP is capturing a growing attention among academics, both for its practical relevance and for the scientific challenges that its study, as a multi-objective problem, poses. Several optimization models have been proposed in the literature, where the tracking error is measured in terms of standard deviation or in linear form using, for instance, the mean absolute deviation. More recently, reward-risk optimization measures, like the Omega ratio, have been adopted for the EITP. On the other side, shortfall or quantile risk measures have nowadays gained an established popularity in a variety of financial applications. In this paper, we propose a class of bi-criteria optimization models for the EITP, where risk is measured using the Weighted multiple Conditional Value-at-Risk (WCVaR). The WCVaR is defined as a weighted combination of multiple CVaR measures, and thus allows a more detailed risk aversion modeling compared to the use of a single CVaR measure. The application of the WCVaR to the EITP is analyzed, both theoretically and empirically. Through extensive computational experiments, the performance of the optimal portfolios selected by means of the proposed optimization models is compared, both in-sample and, more importantly, out-of-sample, to the one of the portfolios obtained using another recent optimization model taken from the literature.
The Conditional Value-at-Risk (CVaR) has become a very popular concept to measure the risk of an ... more The Conditional Value-at-Risk (CVaR) has become a very popular concept to measure the risk of an investment. In fact, though originally proposed for adoption in a financial context, the concept has potential for a broad range of applicability. In this paper, we consider problems that are formulated as mixed integer linear programming (MILP) models and show that a discrete version of the CVaR, that we call Discrete CVaR (DCVaR), can be adopted. The DCVaR mediates between a conservative Min-imax/Maximin criterion and an aggressive minimum cost/maximum profit criterion, in all cases where uncertainty or variability matters. We show that the Discrete CVaR satisfies properties that make it an attractive measure. In particular, the models resulting from the adoption of the Discrete CVaR remain MILP models. To illustrate the relevance of the proposed model, we apply the DCVaR measure to several instances of the multidimensional knapsack problem and the p-median problem.
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Papers by M.Grazia Speranza