Andrea Bettinelli

A branch-and-price algorithm for the multi-depot heterogeneous-fleet pickup and delivery problem with soft time windows

The growing cost of transportation and distribution pushes companies, especially small and medium transportation enterprises, to form partnership and to exploit economies of scale. On the other hand, to increase their competitiveness on the market, companies are asked to consider preferences of the customers as well. Therefore, tools for logistics management need to manage collective resources, as many depots and heterogeneous fleets, providing flexible preference handling at the same time. In this paper we tackle a pickup and delivery vehicle routing problem involving such aspects; customers place preferences on visiting time (represented as soft time windows), and their violation is allowed at a price. Our interest in this problem stems from an ongoing industrial project. First we propose an exact branch-and-price algorithm, having as a core advanced dynamic programming techniques. Then we analyze through a computational campaign the impact of soft time windows management on the optimal solution in terms of both routing and overall distribution costs. Our experiments show that our approach can solve instances of real size, and clarify the practical usefulness of soft time windows management.

Enhanced exact solution methods for the Team Orienteering Problem

The Team Orienteering Problem (TOP) is one of the most investigated problems in the family of vehicle routing problems with profits. In this paper, we propose a Branch-and-Price approach to find proven optimal solutions to TOP. The pricing sub-problem is solved by a bounded bidirectional dynamic programming algorithm with decremental state space relaxation featuring a two-phase dominance rule relaxation. The new method is able to close 17 previously unsolved benchmark instances. In addition, we propose a Branch-and-Cut-and-Price approach using subset-row inequalities and show the effectiveness of these cuts in solving TOP.

A branch-and-price algorithm for the variable size bin packing problem with minimum filling constraint

In this paper we consider a variation of the bin packing problem in which bins of different types have different costs and capacities. Furthermore, each bin has to be filled at least to a certain level, which depends on its type. We present a set partitioning formulation and an exact optimization algorithm which exploits column generation and specialized heuristics. We compare our algorithm with the general purpose solver ILOG CPLEX, running on two compact ILP formulations and we report on experimental results on instances we have generated from data-sets for the variable size bin packing problem.

A branch-and-price algorithm for the two-dimensional level strip packing problem

The two-dimensional level strip packing problem (2LSPP) consists in packing rectangular items of given size into a strip of given width divided into levels. Items packed into the same level cannot be put on top of one another and their overall width cannot exceed the width of the strip. The objective is to accommodate all the items while minimizing the overall height of the strip. The problem is

Simulation and optimization models for emergency medical systems planning

{{\mathcal{NP}}}

Decision Support Systems for Energy Production Optimization and Network Design in District Heating Applications

-hard and arises from applications in logistics and transportation. We present a set covering formulation of the 2LSPP suitable for a column generation approach, where each column corresponds to a feasible combination of items inserted into the same level. For the exact optimization of the 2LSPP we present a branch-and-price algorithm, in which the pricing problem is a penalized knapsack problem. Computational results are reported for benchmark instances with some hundreds items.

Community detection with the weighted parsimony criterion

We study the knapsack problem with conflict graph (KPCG), an extension of the 0-1 knapsack problem, in which a conflict graph describing incompatibilities between items is given. The goal of the KPCG is to select the maximum profit set of compatible items while satisfying the knapsack capacity constraint. We present a new branch-and-bound approach to derive optimal solutions to the KPCG in short computing times. Extensive computational experiments are reported showing that, for instances with graph density of 10% and larger, the proposed method outperforms a state-of-the-art approach and mixed-integer programming formulations tackled through a general purpose solver. The online supplement is available at https://doi.org/10.1287/ijoc.2016.0742.

A branch-and-cut-and-price algorithm for the multi-depot heterogeneous vehicle routing problem with time windows

The authors address strategic planning problems for emergency medical systems (EMS). In particular, the three following critical decisions are considered: i) how many ambulances to deploy in a given territory at any given point in time, to meet the forecasted demand, yielding an appropriate response time; ii) when ambulances should be used for serving nonurgent requests and when they should better be kept idle for possible incoming urgent requests; iii) how to define an optimal mix of contracts for renting ambulances from private associations to meet the forecasted demand at minimum cost. In particular, analytical models for decision support, based on queuing theory, discrete-event simulation, and integer linear programming were presented. Computational experiments have been done on real data from the city of Milan, Italy.