In this paper, we investigate the Steiner Tree Problem with Delays (STPD), which is a generalized version of the Steiner tree problem applied to multicast rout- ing. For this challenging combinatorial optimization problem, we present an enhanced directed cut-based MIP formulation and an exact solution method based on a branch- and-cut approach. Our computational study reveals that the proposed approach can optimally solve hard dense instances.

In this paper we deal with the definition of a decision model for a producer operating in a multi-auction electricity market. The decisions to be taken concern the commitment of the generation plants and the quantity of energy required to offer to each auction and to cover the bilateral contracts. We propose a Multistage Stochastic Programming model in which the randomness of the clearing prices is represented by means of a scenario tree. The risk is modelled using a Conditional Value at Risk term in the objective function. Experimental results are reported to show the validity of our model and to discuss the influence of the risk parameters on the optimal value.

The Steiner Tree Problem with Delays (STPD) is a variant of the well-known Steiner Tree Problem in which the delay on each path between a source node and a terminal node is limited by a given maximum value. We propose a Branch-and-Cut algorithm for solving this problem using a formulation based on lifted Miller-Tucker-Zemlin subtour elimination constraints. The effectiveness of the proposed algorithm is assessed through computational experiments carried out on dense benchmark instances.

This paper deals with Level of Repair Analysis (LORA), which aims at determining appropriated repair policies together with the optimal location of facilities and equipments for the maintenance of complex engineering systems. The aim is to model and solve a single echelon and multi indenture LORA variant, which is more general and realistic than other problems addressed previously. The solution of this problem is faced through the use of an optimization via simulation approach. In particular, we propose a heuristic procedure to explore the search space efficiently through an Approximated Neighbourhood Evaluation (ANE) model, relying on the estimation (via simulation) of a reduced number of parameters.

Urban waste management is becoming an increasingly complex task, absorbing a huge amount of resources, and having a major environmental impact. The design of a waste management system consists in various activities, and one of these is related to the location of waste collection sites. In this paper, we propose an integer programming model that helps decision makers in choosing the sites where to locate the unsorted waste collection bins in a residential town, as well as the capacities of the bins to be located at each collection site. This model helps in assessing tactical decisions through constraints that force each collection area to be capacitated enough to fit the expected waste to be directed to that area, while taking into account Quality of Service constraints from the citizens’ point of view. Moreover, we propose an effective constructive heuristic approach whose aim is to provide a good solution quality in an extremely reduced computational time. Computational results on data related to the city of Nardò, in the south of Italy, show that both exact and heuristic approaches provide consistently better solutions than that currently implemented, resulting in a lower number of activated collection sites, and a lower number of bins to be used.

In this paper we deal with a probabilistic extension of the minimum power multicast (MPM) problem for wireless networks. The deterministic MPM problem consists in assigning transmission powers to the nodes, so that a multihop connection can be established between a source and a given set of destination nodes and the total power required is minimized. We present an extension to the basic problem, where node failure probabilities for the transmission are explicitly considered. This model reflects the necessity of taking uncertainty into account in the availability of the hosts. The novelty of the probabilistic minimum power multicast (PMPM) problem treated in this paper consists in the minimization of the assigned transmission powers, imposing at the same time a global reliability level to the solution network. An integer linear programming formulation for the PMPM problem is presented. Furthermore, an exact algorithm based on an iterative row and column generation procedure, as well as a heuristic method are proposed. Computational experiments are finally presented.

The scope of this paper is to optimize the performance of a multi-indenture maintenance engineering systems. We focus here on modelling and solving a Level of Repair Analysis (LORA) problem variant with simultaneous determination of resources capacity and spare parts. A solution approach based on optimization via simulation is developed. The simulation phase determines the performance of the whole system taking into account the dynamics of the maintenance processes. The optimization phase explores the search space efficiently through a local search method in order to define a new solution with better performance. Computational results collected while solving an illustrative test problem are reported and discussed.

The paper considers the problem of maximizing the profits of a retailer operating in the Italian electricity market. The problem consists in selecting the contracts portfolio and in defining the bidding strategy in the wholesales market while respecting the technical and regulatory constraints. A novel solution method based on a enhanced discovery of the search domain in the simulated annealing technique has been developed for its solution and a set of realistic test problems have been generated for its validation. The experimental results show that our method outperforms the standard simulated annealing by an improvement gap of 20,48% in average.

Many features of the wireless ad hoc networks (WAHNs) like resource limitation, multi-hop communication, dynamic topology and lack of infrastructure make them very attractive and rise many challenging optimization problems. In this paper we propose original preprocessing techniques applied to the Minimum Power Multicasting (MPM) problem in WAHNs in order to reduce the size of the instances and, consequently, the time required to solve them. The experimental results prove that these reduction procedures, based on the graph model representation, are a promising way of speeding up a possible solution method for the MPM problem already developed in the literature.

This chapter considers a price-taker power producer that trades in an electricity pool and provides models for weekly scheduling, contracting, and daily offering. On a weekly basis, a stochastic programming model is formulated to derive the on–off schedule of the production units, the contracting for the entire week and the offering curves for Monday day-ahead market. On a daily basis, a different stochastic programming model is formulated to derive the offering curve in the day-ahead markets of weekdays other than Monday. As a spinoff of the daily model, offering curves for adjustment markets within each day are also derived. Two illustrative examples clarify the models proposed.

In this paper we introduce the Periodic Petrol Station Replenishment Problem (PPSRP) over a T-day planning horizon and describe four heuristic methods for its solution. Even though all the proposed heuristics belong to the common partitioning-then-routing paradigm, they differ in assigning the stations to each day of the horizon. The resulting daily routing problems are then solved exactly until achieving optimalization. Moreover, an improvement procedure is also developed with the aim of ensuring a better quality solution. Our heuristics are tested and compared in two real-life cases, and our computational results show encouraging improvements with respect to a human planning solu-tion

In this paper we propose a parallel implementation for the flood propagation method Flo2DH. The model is built on a finite element spatial approximation combined with a Newton algorithm that uses a direct LU linear solver. The parallel implementation has been developed by using the standard MPI protocol and has been tested on a set of real world problems.

In this paper we describe some results on the linear integer programming formulation of the Probabilistic Minimum Power Multicast (PMPM) problem for wireless networks. The PMPM problem consists in optimally assigning transmission powers to the nodes of a given network in order to establish a multihop connection between a source node and a set of destination nodes. The nodes are subject to failure with some probability, however the assignment should be made so that the reliability of the connection is above a given threshold level. This model reflects the necessity of taking into account the uncertainty of hosts’ availability in a telecommunication network.