Nicoletta Ricciardi

Models for Evaluating and Planning City Logistics Systems

City logistics aims to reduce the nuisances associated to freight transportation in urban areas while supporting their economic and social development. The fundamental idea is to view individual stakeholders and decisions as components of an integrated logistics system. This implies the coordination of shippers, carriers, and movements as well as the consolidation of loads of several customers and carriers into the same environment-friendly vehicles. City logistics explicitly aims to optimize such advanced urban transportation systems. We focus on a challenging city logistics planning issue, the integrated short-term scheduling of operations and management of resources, for the general case involving a two-tiered distribution structure. We investigate the main issues related to the problem, introduce a new problem class, propose both a general model and formulations for the main system components, and identify promising solution avenues.

Optimal facility location with random throughput costs

In this paper we consider the optimal location and size of facilities where the throughput costs for each facility are random. Given a set of origins and a set of destinations, we want to determine the optimal location and size of a set of intermediate facilities in order to minimize the expected total generalized transportation cost. The generalized transportation cost of a freight unit from an origin to a destination passing through a facility is the sum of two terms: the transportation cost from the origin to the destination through the facility and the throughput cost of the facility. While the first term is deterministic, the second one is stochastic with a Gumbel probability distribution. Looking for the expected value of the optimal solution, a mixed deterministic nonlinear problem for the optimal location of the facilities is derived. Two heuristics, which give very good approximations to the optimum, are proposed.

The stochastic p-median problem with unknown cost probability distribution

We want to find the location of p facilities which minimize the expected total cost, when the cost for using a facility is a stochastic variable with unknown probability distribution. Using the method of the asymptotic approximations the expected optimal value of the allocation variables is shown to be a multinomial Logit model.

Service Network Design for Freight Railway Transportation: The Italian Case

In this paper, we present a case study on freight railway transportation in Italy, which is a by-product of research collaboration with a major Italian railway company. We highlight the main features of the Italian reality and propose a customized mathematical model to design the service network, that is, the set of origin-destination connections. More specifically, the model suggests the services to provide, the number of trains travelling on each connection, the number of cars and their type. We consider both full and empty freight car movements and take handling costs into account. All decisions are taken in order to minimize the total costs. The quality of service is guaranteed by satisfying all the transportation demand and by implicitly minimizing the waiting time of cars at intermediate railway stations. Our approach yields to a multi-commodity network design problem with a concave cost function. To solve this problem, we implement a specialized tabu search procedure. Computational results on realistic instances show a significant improvement over current practice.

Modeling demand uncertainty in two-tier city logistics tactical planning

We consider the complex and not-yet-studied issue of building the tactical plan of a two-tiered city logistics system while explicitly accounting for the uncertainty in the forecast demand. We describe and formally define the problem and then propose a general modeling framework, which takes the form of a two-stage stochastic programming formulation, the first stage selecting the first-tier service network design and the general workloads of the intertier transfer facilities, and the second stage determines the actual vehicle routing on the second tier as well as some limited adjustments of the first-stage service design decisions. Four different strategies of adapting the plan to the observed demand are introduced together with the associated recourse formulations. These strategies are then experimentally compared through an evaluation procedure that, based on Monte Carlo principles, mimics the decision process of a priori planning followed by repetitively applying the adjusted plan to the periods of the...

Optimizing blood assignment in a donation-transfusion system

A multi-product, multi-period, multi-objective linear programming model has been built as a contribution to good management of a blood donation‐transfusion system in order to determine the best assignment of blood resources to demand, which minimizes the quantity of blood imported from outside the system and stabilizes the quantities assigned daily. The model has been applied to the Italian Red Cross (CRI) blood donation‐transfusion system in Rome and to each hospital belonging to such a system, producing interesting results.

The dynamic multilevel assignment problem as a stochastic extremal process

This paper considers the multilevel assignment problem (i.e. the assignment problem where the supply alternatives are ranked in hierarchical levels) under the assumption that the utility components for each pairwise matching are stochastic. A dynamic version of the multilevel stochastic assignment model is developed, where both demand and supply evaluate alternatives according to a stochastic extremal process, i.e. a process where the maximum of a sequence of random variables is taken into account. The probability distributions of the random variables which describe the joint dynamic behaviour of demand and supply are found. It is also shown that the assignment probabilities assume the structure of a nested-logit model.

A LP MODEL FOR BLOOD USAGE PLANNING

AbstractA multiobjective multiperiodal linear programming model has been built as a contribution to a good management of a blood donations-transfusions system in order to make the best usage of the blood resource and to minimise the quantity of blood imported from outside the system. The model has been applied to the Italian Red Cross (CRI) blood donations-transfusions system in Rome and to each hospital belonging to such a system, producing interesting results.

A Study on Travel Time Stochasticity in Service Network Design with Quality Targets.

The scope of this paper is to advance the investigation into the importance of introducing uncertainty in service network design (SND) formulations by examining the uncertainty of travel times, a phenomenon that has been little studied up to now. The topic of our research thus is the stochastic scheduled service network design problem with service-quality targets and uncertainty on travel times, an important problem raising in the tactical planning process of consolidation-based freight carriers. Quality-service targets relate to the on-time operation of services and delivery of commodity flows to destinations. The problem is formulated as a two-stage mixed-integer linear stochastic model defined over a space-time network, with service targets modelled through penalties. Its aim is to define a cost-efficient transportation plan such that the chosen quality-service targets are respected as much as possible over time. An extensive experimental campaign is proposed using a large set of random generated instances with the scope of enhancing the understanding of the relations between the characteristics of a service network and its robustness, in terms of respect of the service schedule and delivery due dates, given business-as-usual fluctuations of travel times. Several analyses are reported identifying the features that appear in stochastic solutions to hedge against or, at least, reduce the bad effects of travel time uncertainty on the performance of a service network.