The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is a challenging NP-hard optimization problem, in which each of several customers demands both delivery and pickup operations and must be visited once by a single vehicle from a fleet. In this article, we present a Very Large-Scale Neighborhood Search (VLSN) to tackle the VRPSPD with heterogeneous fleets, whereby different large neighborhood structures are employed within a simple descent method. We use benchmark instances from literature and present very competitive results, improving several of the best-known solutions and consistently outperforming current state-of-the-art algorithms.

We aim at dimensioning fixed broadband microwave wireless networks under unreliable channel conditions. As the transport capacity of microwave links is prone to variations due to, e.g., weather conditions, such a dimensioning requires special attention. It can be formulated as the determination of the minimum cost bandwidth assignment of the links in the network for which traffic requirements can be met with high probability, while taking into account that transport link capacities vary depending on channel conditions. The proposed optimization model represents a major step forward since we consider dynamic routing. Experimental results show that the resulting solutions can save up to 45% of the bandwidth cost compared to the case where a bandwidth over-provisioning policy is uniformly applied to all links in the network planning. Comparisons with previous work also show that we can solve much larger instances in significantly shorter computing times, with a comparable level of reliability.

In this paper, we extend our former investigation on conceiving reliable fixed point-to-point wireless networks under outage probability constraints. We consider the problem of determining the minimum cost bandwidth assignment of a network, while guaranteeing a reliability level of the solution. If the optimal bandwidth assignment and routing of traffic demands are accomplished, the reliability criterion requires that network flows remain feasible with high probability, regarding that the performance of microwave links is prone to variations due to external factors, e.g., weather. We introduce a chance-constrained programming approach to tackle this problem and we present reformulations to standard integer linear programming models, including a budget-constrained formulation. To improve the solving performance, we propose new valid inequalities and a primal heuristic. Computational results present a performance analysis of the valid inequalities and the heuristic. Further, the outperformance of the novel model compared to more traditional approaches is documented.

We consider an integrated biomass inventory and energy production problem that arises from scenario analysis in national energy planning. It addresses together two decision stages that have previously been kept distinct: the decisions on the purchase of biomass and the decisions on the production of electricity and heat of each power plant. We model the problem as a stochastic mixed 0-1 integer linear programming problem. Since practical instances of this problem are very large, we experimentally assess a relaxed formulation to obtain near-optimal solutions. In addition, we study a Benders decomposition that exploits the problem structure of the relaxed formulation. In a distributed computing architecture this decomposition allows to increase the number of included scenarios and thus to better address the uncertainty of the data. Computational results indicate that at parity of information the approximation provided by the relaxed model is good. However, by allowing to increase the amount of information treated it can provide more accurate predictions. On a multicore computing architecture a state-of-the-art MIP solver operating on the undecomposed model is sufficient to achieve similar performance as the Benders decomposition. However, the use of the solver in a distributed computing environment is not obvious and the Benders decomposition is a more easily implementable and scalable approach.

In this paper, we investigate on conceiving reliable fixed broadband wireless networks under outage probability constraints. We introduce a joint model of data routing and bandwidth assignment that minimizes the total renewal fees of licenses. This problem differs from classical capacity planning since the capacity of microwave links is prone to variations and, hence, we must deal with random parameters to guarantee a desirable reliability level of the solution. We introduce a chance-constrained programming approach to tackle this problem and derive integer linear programming (ILP) counterparts. We further propose cutset-based valid inequalities to enhance the performance of ILP solvers. Computational results illustrate the price of reliability and present a comparative study on the performance of the different formulations.

In this paper, we investigate on minimizing the energy consumption of a fixed broadband wireless network through a joint optimization of data routing and radio configuration. Every link holds a set of power-efficient configurations, each of them associating a capacity with its energy cost. The optimization problem involves deciding the network's configuration and flows that minimize the total energy consumption. An exact mathematical formulation of the problem is presented. It relies on a minimum cost multicommodity flow with step increasing cost functions. We then propose a piecewise linear convex function that provides a good approximation of the energy consumption on the links, and present a relaxation of the previous formulation that exploits the convexity of the cost functions. This yields lower bounds on the energy consumption, and finally a heuristic algorithm based on the fractional optimum is employed to produce feasible solutions. Our models are validated through extensive experiments.