Dominique Feillet

An exact algorithm for the elementary shortest path problem with resource constraints: Application to some vehicle routing problems

In this article, we propose a solution procedure for the Elementary Shortest Path Problem with Resource Constraints (ESPPRC). A relaxed version of this problem in which the path does not have to be elementary has been the backbone of a number of solution procedures based on column generation for several important problems, such as vehicle routing and crew pairing. In many cases relaxing the restriction of an elementary path resulted in optimal solutions in a reasonable computation time. However, for a number of other problems, the elementary path restriction has too much impact on the solution to be relaxed or might even be necessary. We propose an exact solution procedure for the ESPPRC, which extends the classical label correcting algorithm originally developed for the relaxed (nonelementary) path version of this problem. We present computational experiments of this algorithm for our specific problem and embedded in a column generation scheme for the classical Vehicle Routing Problem with Time Windows.

Traveling Salesman Problems with Profits

Traveling salesman problems with profits (TSPs with profits) are a generalization of the traveling salesman problem (TSP), where it is not necessary to visit all vertices. A profit is associated with each vertex. The overall goal is the simultaneous optimization of the collected profit and the travel costs. These two optimization criteria appear either in the objective function or as a constraint. In this paper, a classification of TSPs with profits is proposed, and the existing literature is surveyed. Different classes of applications, modeling approaches, and exact or heuristic solution techniques are identified and compared. Conclusions emphasize the interest of this class of problems, with respect to applications as well as theoretical results.

An exact algorithm for team orienteering problems

Optimising routing of vehicles constitutes a major logistic stake in many industrial contexts. We are interested here in the optimal resolution of special cases of vehicle routing problems, known as team orienteering problems. In these problems, vehicles are guided by a reward that can be collected from customers, while the length of routes is limited. The main difference with classical vehicle routing problems is that not all customers have to be visited. The solution method we propose here is based on a Branch & Price algorithm. It is, as far as we know, the first exact method proposed for such problems, except for a preliminary work from Gueguen (Methodes de résolution exacte pour problémes de tournées de véhicules. Thése de doctorat, école Centrale Paris 1999) and a work from Butt and Ryan (Comput Oper Res 26(4):427–441 1999). It permits to solve instances with up to 100 customers.

Interior point stabilization for column generation

Interior point stabilization is an acceleration method for column generation algorithms. It addresses degeneracy and convergence difficulties by selecting a dual solution inside the optimal space rather than retrieving an extreme point. The method is applied to the case of the vehicle routing problem with time windows.

The capacitated team orienteering and profitable tour problems

In this paper, we study the capacitated team orienteering and profitable tour problems (CTOP and CPTP). The interest in these problems comes from recent developments in the use of the Internet for a better matching of demand and offer of transportation services. We propose exact and heuristic procedures for the CTOP and the CPTP. The computational results show that the heuristic procedures often find the optimal solution and in general cause very limited errors.

A Memetic Algorithm with a large neighborhood crossover operator for the Generalized Traveling Salesman Problem

The Generalized Traveling Salesman Problem (GTSP) is a generalization of the well-known Traveling Salesman Problem (TSP), in which the set of cities is divided into mutually exclusive clusters. The objective of the GTSP consists in visiting each cluster exactly once in a tour, while minimizing the sum of the routing costs. This paper addresses the solution of the GTSP using a Memetic Algorithm. The originality of our approach rests on the crossover procedure that uses a large neighborhood search. This algorithm is compared with other algorithms on a set of 54 standard test problems with up to 217 clusters and 1084 cities. Results demonstrate the efficiency of our algorithm in both solution quality and computation time.

A memetic algorithm for the Multi Trip Vehicle Routing Problem

We consider the Multi Trip Vehicle Routing Problem, in which a set of geographically scattered customers have to be served by a fleet of vehicles. Each vehicle can perform several trips during the working day. The objective is to minimize the total travel time while respecting temporal and capacity constraints.

A tutorial on column generation and branch-and-price for vehicle routing problems

This paper provides a tutorial on column generation and branch-and-price for vehicle routing problems. The main principles and the basic theory of the methods are first outlined. Some additional issues, including reinforcement of the relaxation or stabilization, complete the paper. For the sake of simplicity, this material is illustrated with the case of the vehicle routing problem with time windows.

Ant colony optimization for the traveling purchaser problem

The traveling purchaser problem (TPP) is a generalization of the traveling salesman problem where markets have to be visited to collect a set of commodities. Each market sells a number of commodities at a known price. The TPP consists in selecting a subset of markets purchasing every product, while minimizing the routing costs and the purchase costs. In this work, we address the solution of the TPP with an ant colony optimization procedure. We combine it with a local-search scheme exploring a new neighborhood structure. This procedure is evaluated on a set of benchmark instances from the literature and permits to improve most of the best-known solutions.

A branch and bound method for the job-shop problem with sequence-dependent setup times

Abstract This paper deals with the job-shop scheduling problem with sequence-dependent setup times. We propose a new method to solve the makespan minimization problem to optimality. The method is based on iterative solving via branch and bound decisional versions of the problem. At each node of the branch and bound tree, constraint propagation algorithms adapted to setup times are performed for domain filtering and feasibility check. Relaxations based on the traveling salesman problem with time windows are also solved to perform additional pruning. The traveling salesman problem is formulated as an elementary shortest path problem with resource constraints and solved through dynamic programming. This method allows to close previously unsolved benchmark instances of the literature and also provides new lower and upper bounds.