Column generation with dynamic duty selection for railway crew rescheduling

The Dutch railway network experiences about three large disruptions per day on average. In this paper, we present an algorithm to reschedule the crews when such a disruption occurs. The algorithm is based on column generation techniques combined with Lagrangian heuristics. Since the number of duties is very large in practical instances, we first define a core problem of tractable size. If some tasks remain uncovered in the solution of the core problem, we perform a neighborhood exploration to improve the solution. Computational experiments with real-life instances show that our method is capable of producing good solutions within a couple of minutes of Computation time

Comparison of heuristic approaches for the multiple depot vehicle scheduling problem

Given a set of timetabled tasks, the multi-depot vehicle scheduling problem is a well-known problem that consists of determining least-cost schedules for vehicles assigned to several depots such that each task is accomplished exactly once by a vehicle. In this paper, we propose to compare the performance of five different heuristic approaches for this problem, namely, a heuristic \\mip solver, a Lagrangian heuristic, a column generation heuristic, a large neighborhood search heuristic using column generation for neighborhood evaluation, and a tabu search heuristic. The first three methods are adaptations of existing methods, while the last two are novel approaches for this problem. Computational results on randomly generated instances show that the column generation heuristic performs the best when enough computational time is available and stability is required, while the large neighborhood search method is the best alternative when looking for a compromise between computational time and solution quality

Branch-Price-and-Cut Algorithms for the Pickup and Delivery Problem with Time Windows and Last-in-First-Out Loading

This paper proposes models and algorithms for the pickup and delivery vehicle routing problem with time windows and last-in-first-out (LIFO) loading constraints (PDPTWL). The LIFO loading rule ensures that no handling is required prior to unloading an item from a vehicle: a linear stack loading structure is maintained and an item can only be delivered if it is the last one in the stack. Three exact branch-price-and-cut algorithms are proposed for this problem. The first incorporates the LIFO constraints in the master problem. The second one handles the LIFO constraints directly in the shortest path pricing problem. It applies a dynamic programming algorithm relying on an ad hoc dominance criterion. The third algorithm is a hybrid between the first two methods. Known valid inequalities are adapted to the PDPTWL and the impact of different path relaxations on the total computation time is investigated. Computational results obtained on instances derived from known instances of the pickup and delivery problem with time windows (PDPTW) are reported. </jats:p

A Branch-Price-and-Cut Algorithm for the Inventory-Routing Problem

The inventory-routing problem (IRP) integrates two well-studied problems, namely, inventory management and vehicle routing. Given a set of customers to service over a multiperiod horizon, the IRP consists of determining when to visit each customer, which quantity to deliver in each visit, and how to combine the visits in each period into feasible routes such that the total routing and inventory costs are minimized. In this paper, we propose an innovative mathematical formulation for the IRP and develop a state-of-the-art branch-price-and-cut algorithm for solving it. This algorithm incorporates known and new families of valid inequalities, including an adaptation of the well-known capacity inequalities, as well as an ad hoc labeling algorithm for solving the column generation subproblems. Through extensive computational experiments on a widely used set of 640 benchmark instances involving between two and five vehicles, we show that our branch-price-and-cut algorithm clearly outperforms a state-of-the-art branch-and-cut algorithm on the instances with four and five vehicles. In this instance set, 238 were still open before this work and we proved optimality for 54 of them. </jats:p