Marta Flamini

A job-shop problem with one additional resource type

We consider a job-shop scheduling problem with n jobs and the constraint that at most p<n jobs can be processed simultaneously. This model arises in several manufacturing processes, where each operation has to be assisted by one human operator and there are p (versatile) operators. The problem is binary NP-hard even with n=3 and p=2. When the number of jobs is fixed, we give a pseudopolynomial dynamic programming algorithm and a fully polynomial time approximation scheme (FPTAS). We also propose an enumeration scheme based on a generalized disjunctive graph, and a dynamic programming-based heuristic algorithm. The results of an extensive computational study for the case with n=3 and p=2 are presented.

Real time management of a metro rail terminus

This paper addresses a scheduling problem arising in the real time management of a metro rail terminus. It mainly consists in routing incoming trains through the station and scheduling their departures with the objective of optimizing punctuality and regularity of train service. The purpose of this work is to develop an automated train traffic control system, able to directly implement most traffic control actions, without the authorization of the local area manager. The scheduling problem is modeled as a bicriteria job shop scheduling problem with additional constraints. The two objective functions, in lexicographical order, are the minimization of tardiness/earliness and the headway optimization. The problem is solved in two steps. At first a heuristic builds a feasible solution by considering the first objective function. Then the regularity is optimized without deteriorating the first objective function. Computational results show that the system is able to manage the terminus very efficiently.

Scheduling three chains on two parallel machines

We consider the problem of scheduling n tasks subject to chain-precedence constraints on two identical machines with the objective of minimizing the makespan. The problem is known to be strongly NP-hard. Here, we prove that it is binary NP-hard even with three chains. Furthermore, we characterize the complexity of this case by presenting a pseudopolynomial time algorithm and a fully polynomial time approximation scheme.

Interval Estimation of the Herfindahl-Hirschman Index under Incomplete Market Information

An interval estimate is provided for the Herfindahl-Hirschman Index (HHI) when the knowledge about the market is incomplete, and we know just the largest market shares. Two rigorous bounds are provided for the HHI, without any further assumptions. Though the interval gets wider as the sum of the known market shares gets smaller, the estimate proves to be quite tight even when the fraction of the market that we do not know in detail is as high as 30%. This robustness is shown through three examples, considering respectively a set of real data and two sets of synthetic data, with the company sizes (a proxy for market shares) following respectively a Zipf law and a Pareto distribution.

A Game-Theoretic Formulation of Security Investment Decisions under Ex-ante Regulation

Data breaches represents a major source of worries (and economic losses) for customers and service providers. We introduce a data breach model that recognizes that breaches can take place on the customer’s premises as well as on the service provider’s side, but the customer bears the economic loss. In order to induce the service provider into investing in security, a regulatory policy that apportions the money loss between the customer and the service provider is introduced. A game-theoretic formulation is given for the strategic interaction to the customer and the service provider, where the former sets the amount of personal information it releases and the latter decides how much to invest in security. The game’s outcome shows that shifting the burden of the money loss due to data breaches towards the service provider spurs its investment in security (though up to moderate levels) and leads the customer to be more confident, but the apportionment must not be too unbalanced for a Nash equilibrium to exist. On the other hand, changes in the probability of data breach of both sides do not affect significantly the service provider’s behaviour, but cause heavy consequences on the customer’s confidence.

Damage Sharing May Not Be Enough: An Analysis of an Ex-ante Regulation Policy for Data Breaches

Data breaches, occurring either on the customer’s PCs or on the service provider’s equipment, expose customers to significant economic losses. An ex-ante regulation policy that apportions a fraction of the losses to the service provider (a damage-sharing policy) may reduce the burden for the customer and lead the service provider to invest more in security. We analyse this regulation policy through a game-theoretic approach, where the customer acts on the amount of personal information it reveals, and the service provider acts on the amount of security investments. We show that the game exhibits a single Nash equilibrium in a realistic scenario. In order to optimize the social welfare, the regulator has to choose the fraction of damage apportioned to the service provider. We show that the policy is relatively ineffective unless the fraction of damage charged to the service provider is quite large, beyond 60%. On the other hand, if the policy is applied with a large damage-sharing factor, the overall social welfare falls heavily.

Aircraft Ground Routing and Scheduling Optimization

In this paper we deal with the ground optimization problem, that is the problem of routing and scheduling airplanes surface maneuvering operations. We consider the specific case study of Malpensa Terminal Maneuvering Area (Italy). Our objective function is the minimization of total tardiness. At first a routing problem is solved to assign a path to each aircraft in the terminal, then the scheduling problem of minimizing the average tardiness is addressed. We model the scheduling problem as a job-shop scheduling problem. We develop heuristic procedures based on the alternative graph formulation of the problem to construct and improve feasible solutions. Experimental results based on real data and analysis are reported.

Information Security Investments: When Being Idle Equals Negligence

The Learned Hands rule, comparing security investments against the expected loss from data breaches, can be used as a simple tool to determine the negligence of the company holding the data. On the other hand, companies may determine their investments in security by maximizing their own net profit. We consider the well known Gordon-Loeb models as well as the more recent Huang-Behara models for the relationship between investments and the probability of money loss due to malicious attacks to determine the outcome of the application of three forms of Hands rule: status quo (loss under no investments), ex-post (loss after investment), transitional (loss reduction due to investment). The company is always held negligent if it does not invest in both the status quo and the transitional form. In the ex-post form, it is instead held negligent just if the potential loss is below a threshold, for which we provide the exact expression.

Limiting pollution in ground control optimization

In this paper we deal with the problem of scheduling aircrafts maneuvering on ground in the specific case study of Malpensa Airport. Given a fixed route and a landing/take off time instant for each landing/departing aircraft, we consider three objective functions in the following lexicographical order: (i) the minimization of the number of tardy aircrafts; (ii) the maximization of the safety; (iii) the minimization of pollution and noise. Problem constraints are related to the safety rules. We model the problem as a job-shop scheduling problem. We develop heuristic procedures based on an alternative graph formulation of the problem to construct and improve feasible solutions. Experimental results based on real data and analysis are reported.

The value of real-time traffic information in urban freight distribution

ABSTRACT Routes optimization in urban freight distribution is usually an off-line process based on the knowledge of historical conditions on the network. Real-time data provided by Intelligent Transportation Systems (ITSs) enable online re-optimization on the basis of actual traffic conditions.  This paper evaluates the added value generated by re-optimizing the off-line solution with real-time information. The study is carried out for a practical application to the freight distribution of perishable goods in the city of Rome (Italy). The off-line problem is formulated as a vehicle routing problem with soft time windows while in the online problem it is also allowed to skip some customers or to re-sequence the deliveries. Both versions are solved with different algorithms and with different data sets. Results can be used to evaluate the potential return on investment on the acquisition of different kinds of traffic data. At the same time, results can be of interest for information providers, to fix the price of off-line and online information and/or to estimate the associated potential market share.