Paolo Toth

A Survey of Optimization Models for Train Routing and Scheduling

The aim of this paper is to present a survey of recent optimization models for the most commonly studied rail transportation problems. For each group of problems, we propose a classification of models and describe their important characteristics by focusing on model structure and algorithmic aspects. The review mainly concentrates on routing and scheduling problems since they represent the most important portion of the planning activities performed by railways. Routing models surveyed concern the operating policies for freight transportation and railcar fleet management, whereas scheduling models address the dispatching of trains and the assignment of locomotives and cars. A brief discussion of analytical yard and line models is also presented. The emphasis is on recent contributions, but several older yet important works are also cited.

Exact algorithms for the vehicle routing problem, based on spanning tree and shortest path relaxations

We consider the problem of routing vehicles stationed at a central facility (depot) to supply customers with known demands, in such a way as to minimize the total distance travelled. The problem is referred to as the vehicle routing problem (VRP) and is a generalization of the multiple travelling salesman problem that has many practical applications.We present tree search algorithms for the exact solution of the VRP incorporating lower bounds computed from (i) shortest spanningk-degree centre tree (k-DCT), and (ii)q-routes. The final algorithms also include problem reduction and dominance tests.Computational results are presented for a number of problems derived from the literature. The results show that the bounds derived from theq-routes are superior to those fromk-DCT and that VRPs of up to about 25 customers can be solved exactly.

The Granular Tabu Search and Its Application to the Vehicle-Routing Problem

We describe a new variant, calledgranular tabu search, of the well-known tabu-search approach. The method uses an effective intensification/diversification tool that can be successfully applied to a wide class of graph-theoretic and combinatorial-optimization problems. Granular tabu search is based on the use of drastically restricted neighborhoods, not containing the moves that involve only elements that are not likely to belong to good feasible solutions. These restricted neighborhoods are calledgranular, and may be seen as an efficient implementation of candidate-list strategies proposed for tabu-search algorithms. Results of computational testing of the proposed approach on the well-known symmetric capacitated and distance-constrained vehicle-routing problem are discussed, showing that the approach is able to determine very good solutions within short computing times.

A Heuristic Method for the Set Covering Problem

We present a Lagrangian-based heuristic for the well-known Set Covering Problem (SCP). The algorithm was initially designed for solving very large scale SCP instances, involving up to 5,000 rows and 1,000,000 columns, arising from crew scheduling in the Italian Railway Company, Ferrovie dello Stato SpA. In 1994 Ferrovie dello Stato SpA, jointly with the Italian Operational Research Society, organized a competition, called FASTER, intended to promote the development of algorithms capable of producing good solutions for these instances, since the classical approaches meet with considerable difficulties in tackling them. The main characteristics of the algorithm we propose are (1) a dynamic pricing scheme for the variables, akin to that used for solving large-scale LPs, to be coupled with subgradient optimization and greedy algorithms, and (2) the systematic use of column fixing to obtain improved solutions. Moreover, we propose a number of improvements on the standard way of defining the step-size and the ascent direction within the subgradient optimization procedure, and the scores within the greedy algorithms. Finally, an effective refining procedure is proposed. Our code won the first prize in the FASTER competition, giving the best solution value for all the proposed instances. The algorithm was also tested on the test instances from the literature: in 92 out of the 94 instances in our test bed we found, within short computing time, the optimal (or the best known) solution. Moreover, among the 18 instances for which the optimum is not known, in 6 cases our solution is better than any other solution found by previous techniques.

Modeling and Solving the Train Timetabling Problem

The train timetabling problem aims at determining a periodic timetable for a set of trains that does not violate track capacities and satisfies some operational constraints. In particular, we concentrate on the problem of a single, one-way track linking two major stations, with a number of intermediate stations in between. Each train connects two given stations along the track (possibly different from the two major stations) and may have to stop for a minimum time in some of the intermediate stations. Trains can overtake each other only in correspondence of an intermediate station, and a minimum time interval between two consecutive departures and arrivals of trains in each station is specified.In this paper, we propose a graph theoretic formulation for the problem using a directed multigraph in which nodes correspond to departures/arrivals at a certain station at a given time instant. This formulation is used to derive an integer linear programming model that is relaxed in a Lagrangian way. A novel feature of our model is that the variables in the relaxed constraints are associated only with nodes (as opposed to arcs) of the aforementioned graph. This allows a considerable speed-up in the solution of the relaxation. The relaxation is embedded within a heuristic algorithm which makes extensive use of the dual information associated with the Lagrangian multipliers. We report extensive computational results on real-world instances provided from Ferrovie dello Stato SpA, the Italian railway company, and from Ansaldo Segnalamento Ferroviario SpA.

Algorithms for the Set Covering Problem

The Set Covering Problem (SCP) is a main model for several important applications, including crew scheduling in railway and mass-transit companies. In this survey, we focus our attention on the most recent and effective algorithms for SCP, considering both heuristic and exact approaches, outlining their main characteristics and presenting an experimental comparison on the test-bed instances of Beasleys OR Library.

A Branch-and-Cut Algorithm for the Symmetric Generalized Traveling Salesman Problem

We consider a variant of the classical symmetric Traveling Salesman Problem in which the nodes are partitioned into clusters and the salesman has to visit at least one node for each cluster. This NP-hard problem is known in the literature as the symmetric Generalized Traveling Salesman Problem (GTSP), and finds practical applications in routing, scheduling and location-routing. In a companion paper (Fischetti et al. [Fischetti, M., J. J. Salazar, P. Toth. 1995. The symmetric generalized traveling salesman polytope. Networks 26 113–123.]) we modeled GTSP as an integer linear program, and studied the facial structure of two polytopes associated with the problem. Here we propose exact and heuristic separation procedures for some classes of facet-defining inequalities, which are used within a branch-and-cut algorithm for the exact solution of GTSP. Heuristic procedures are also described. Extensive computational results for instances taken from the literature and involving up to 442 nodes are reported.

Vehicle Routing: Problems, Methods, and Applications, Second Edition

Vehicle routing problems, among the most studied in combinatorial optimization, arise in many practical contexts (freight distribution and collection, transportation, garbage collection, newspaper delivery, etc.). Operations researchers have made significant developments in the algorithms for their solution, and Vehicle Routing: Problems, Methods, and Applications, Second Edition reflects these advances. The text of the new edition is either completely new or significantly revised and provides extensive and complete state-of-the-art coverage of vehicle routing by those who have done most of the innovative research in the area; it emphasizes methodology related to specific classes of vehicle routing problems and, since vehicle routing is used as a benchmark for all new solution techniques, contains a complete overview of current solutions to combinatorial optimization problems. It also includes several chapters on important and emerging applications, such as disaster relief and green vehicle routing. Audience: This book is intended for both researchers and graduate level students in operations research and applied mathematics. Practitioners will find this book particularly useful. Readers need a basic knowledge of the main methods for the solution of combinatorial optimization problems.

State‐space relaxation procedures for the computation of bounds to routing problems

It is well-known that few combinatorial optimization problems can be solved effectively by dynamic programming alone, since the number of vertices of the state space graph is enormous. What we are proposing here is a general relaxation procedure whereby the state-space associated with a given dynamic programming recursion is relaxed in such a way that the solution to the relaxed recursion provides a bound which could be embedded in general branch and bound schemes for the solution of the problem. This state space relaxation method is analogous to Langrangian relaxation in integer programming. This paper gives a survey of this new methodology, and gives, as examples, applications to the traveling salesman problem (TSP), the timeconstrained TSP and the vehicle routing problem (VRP). Valid state space relaxations are discussed for these problems and several bounds are derived in each case. Subgradient optimization and “state space ascent” are discussed as methods of maximizing the resulting lower bounds. More details of the procedures surveyed in this paper can be found in [2 ,3 ,41.

Models, relaxations and exact approaches for the capacitated vehicle routing problem

In this paper we review the exact algorithms based on the branch and bound approach proposed in the last years for the solution of the basic version of the vehicle routing problem (VRP), where only the vehicle capacity constraints are considered. These algorithms have considerably increased the size of VRPs that can be solved with respect to earlier approaches. Moreover, at least for the case in which the cost matrix is asymmetric, branch and bound algorithms still represent the state-of-the-art with respect to the exact solution. Computational results comparing the performance of different relaxations and algorithms on a set of benchmark instances are presented. We conclude by examining possible future directions of research in this field.