Showing papers by "Daniele Vigo published in 2016"

Intermediate Facilities in Freight Transportation Planning: A Survey

Abstract: Consolidation of freight and merging operations are essential for transportation companies to reduce costs and improve the level of service provided to customers. Such operations take place in intermediate facilities or terminals located between the origins and the destinations of freight. This survey reviews the main contributions from the operations research literature on freight transportation planning problems where the presence of intermediate facilities has a strong impact on the cost of the system and on how goods are delivered. In particular, we focus on the tactical planning issues arising in this context. We have identified three classes of problems with intermediate facilities: vehicle routing problems, transshipment problems, and service network design problems. For each class of problems we provide an overview of the main problem variants, survey the methods used for their solution, and indicate open research directions.

Enhanced exact solution methods for the Team Orienteering Problem

Abstract: 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.

Overview of Optimization Problems in Electric Car-Sharing System Design and Management

Abstract: Car-sharing systems are increasingly employing environmentally-friendly electric vehicles. The design and management of Ecar-sharing systems poses several additional challenges with respect to those based on traditional combustion vehicles, mainly related with the limited autonomy allowed by current battery technology. We review the main optimization problems arising in Ecar-sharing systems at strategic, tactical and operational levels, and discuss the existing approaches often developed for similar problems, for example in car-sharing systems with traditional vehicles. We also outline open problems and fruitful research directions.

Supplier's total cost of ownership evaluation: a data envelopment analysis approach

Abstract: Supplier Total Cost of Ownership (TCO) is a widely-known approach for determining the overall cost generated by a supplier relationship, but its adoption is still limited. The complex calculations involved – and in particular the activity-based costing procedure for computing the cost of managing the relationship – pose a major obstacle to widespread TCO implementation. The purpose of this work is to formulate a Data Envelopment Analysis application (denoted ‘TCO-based DEA’) that can act as a proxy for TCO, and to test its ability to approximate the results of TCO with less effort. The study is based on the analysis of two categories of suppliers (74 in total) of a medium-sized Italian mechanical engineering company. The results show that TCO-based DEA is able to significantly approximate the outcomes of TCO, for both the efficiency indexes and rankings of suppliers, whilst requiring substantially less effort to perform the analysis. To our knowledge, this is the first study to develop a DEA-based tool for approximating TCO and to test it in a real-world setting. The research shows significant potential within the supply chain management field. In particular, TCO-based DEA can be used for analysing suppliers’ performance, rationalising and reducing the supplier base, assisting the negotiation process.

From a Real Deployment to a Downscaled Testbed: A Methodological Approach

Abstract: This paper proposes a novel methodology for the spatial downscaling of real-world deployments of wireless networks, running protocols, and/or applications for the Internet of Things (IoT). These networks are often deployed in environments not easily accessible and highly unpredictable, where doing experiments is very expensive and time consuming. The latter calls for the need to develop downscaled testbeds, deployed in controlled environments, where tests can be conducted under predictable conditions. This paper presents a methodology to realize the downscaling of a real deployment on an experimental platform, called controllable testbed that has a much larger number of nodes with respect to the real one. The downscaling procedure proposed is based on the identification of the most appropriate subset of nodes of the controllable testbed, to be used to reproduce the channel gains between each node pair in the real world. The latter, in fact, results in obtaining the same network topologies, bringing to the same performance on average, when the same protocol stack and software are run. After the description of the procedure, an example of its implementation is provided. Comparison of results, in terms of packet loss rate (PLR), network throughput, and topologies achieved on the downscaled testbed and on the real-world deployment, is given; results show a very good fit and demonstrate the efficacy of the proposed methodology.

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

Abstract: This chapter focuses on the application of Operations Research approaches to energy distribution and production. As to the energy distribution, we consider the strategic problem of optimally expanding a district heating network to maximize the net present value. As to the energy production, the problem we consider is at a tactical level and concerns the definition of the best unit commitment plan to maximize the profit of the plant. The chapter presents two decision support systems designed to address the above mentioned problems and discusses their use in real-world applications.

Erratum—Exact Algorithms for the Clustered Vehicle Routing Problem

Abstract: The clustered vehicle routing problem (CluVRP) is a generalization of the capacitated vehicle routing problem (CVRP) were customers are partitioned into disjoint clusters. As in the CVRP, all the customers must be visited exactly once, but a vehicle visiting one customer in a cluster must visit all the remaining customers therein before leaving it. Hence, when the first and last customer to be visited in a cluster C are p and q, finding the optimal sequence to visit the remaining customers in the cluster is equal to finding the minimum cost Hamiltonian path with p and q as endpoints. Battarra et al. (2014) presented a new compact integer programming formulation for the CluVRP. This formulation exploits the special substructure of the clusters such that only inter-cluster connections have to be determined. The authors show that this formulation provides a stronger linear relaxation than the CluVRP formulation based on the tradional CVRP two-index formulation. In the sequence of propositions that show the equivalence of the new formulation with the traditional CluVRP formulation, an erroneous notation was introduced in Battarra et al. (2014). In particular, this appears in the proof of Lemma 2, which is rewritten here in correct form. We emphasize that the incorrect notation in the proof does not affect the overall validity of Lemma 2, hence has no effect on the correctness of the paper and on the results reported therein. Let „4S5 denote the set of edges connecting the vertices in a vertex set S ⊆ V to those outside the set, and the set of edges inside a vertex set S by E4S5. The decision variables x ij and x ∗∗ ij are equal to the number of times a vehicle traverses edge 4i1 j5 for a solution of the traditional and the new formulation, respectively. The values of x ij are determined through the following transformation from x ij . For each inter-cluster edge 4i1 j5 ∈ Ē, set x ij = x ∗∗ ij . Initialize x ij = 0 for all intra-cluster edges 4i1 j5 ∈ E. For all 4p1 q5 ∈ E and for all 4i1 j5 ∈ P4p1q5, increment x ij by x ∗∗ pq . For the details of the CluVRP formulations, we refer the reader to Battarra et al. (2014). Lemma 2. Given a set of customer vertices S ⊂ V \809, with C being the minimal set of clusters covering S, i.e., ∀i ∈ S, ∃C ∈ C: i ∈C, the inequality ∑