Roberto Wolfler Calvo

Solving the capacitated location-routing problem by a GRASP complemented by a learning process and a path relinking

As shown in recent researches, the costs in distribution systems may be excessive if routes are ignored when locating depots. The location routing problem (LRP) overcomes this drawback by simultaneously tackling location and routing decisions. This paper presents a new metaheuristic to solve the LRP with capacitated routes and depots. A first phase executes a GRASP, based on an extended and randomized version of Clarke and Wright algorithm. This phase is implemented with a learning process on the choice of depots. In a second phase, new solutions are generated by a post-optimization using a path relinking. The method is evaluated on sets of randomly generated instances, and compared to other heuristics and a lower bound. Solutions are obtained in a reasonable amount of time for such a strategic problem. Furthermore, the algorithm is competitive with a metaheuristic published for the case of uncapacitated depots.

Solving the Capacitated Location-Routing Problem by a Cooperative Lagrangean Relaxation-Granular Tabu Search Heuristic

Most of the time in a distribution system, depot location and vehicle routing are interdependent, and recent studies have shown that the overall system cost may be excessive if routing decisions are ignored when locating depots. The location-routing problem (LRP) overcomes this drawback by simultaneously tackling location and routing decisions. This paper presents a cooperative metaheuristic to solve the LRP with capacitated routes and depots. The principle is to alternate between a depot location phase and a routing phase, exchanging information on the most promising edges. In the first phase, the routes and their customers are aggregated into supercustomers, leading to a facility-location problem, which is then solved by a Lagrangean relaxation of the assignment constraints. In the second phase, the routes from the resulting multidepot vehicle-routing problem (VRP) are improved using a granular tabu search (GTS) heuristic. At the end of each global iteration, information about the edges most often used is recorded to be used in the following phases. The method is evaluated on three sets of randomly generated instances and compared with other heuristics and a lower bound. Solutions are obtained in a reasonable amount of time for such a strategic problem and show that this metaheuristic outperforms other methods on various kinds of instances.

Bike sharing systems: Solving the static rebalancing problem

Abstract This paper deals with a new problem that is a generalization of the many to many pickup and delivery problem and which is motivated by operating self-service bike sharing systems. There is only one commodity, initially distributed among the vertices of a graph, and a capacitated single vehicle aims to redistribute the commodity in order to reach a target distribution. Each vertex can be visited several times and also can be used as a buffer in which the commodity is stored for a later visit. This problem is NP-hard, since it contains several NP-hard problems as special cases (the TSP being maybe the most obvious one). Even finding a tractable exact formulation remains problematic. This paper presents efficient algorithms for solving instances of reasonable size, and contains several theoretical results related to these algorithms. A branch-and-cut algorithm is proposed for solving a relaxation of the problem. An upper bound of the optimal solution of the problem is obtained by a tabu search, which is based on some theoretical properties of the solution, once fixed the sequence of the visited vertices. The possibility of using the information provided by the relaxation receives a special attention, both from a theoretical and a practical point of view. It is proven that to build a feasible solution of the problem by using the one obtained by the relaxation is an NP-hard problem. Nevertheless, a tabu search initialized with the optimal solution of the relaxation often shows that it is the optimal one. The algorithms have been tested on a set of instances coming from the literature, proving their effectiveness.

A Branch-and-Cut method for the Capacitated Location-Routing Problem

Most of the time in a distribution system, depot location and vehicle routing are interdependent and recent researches have shown that the overall system cost may be excessive if routing decisions are ignored when locating depots. The location routing problem (LRP) overcomes this drawback by simultaneously tackling location and routing decisions. This paper presents two formulations of the location-routing problem with capacities on routes and depots and proposes an exact method based on a branch and cut approach using these formulations. The method is evaluated on two sets of randomly generated instances, and compared to heuristics and another lower bound

An effective memetic algorithm for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem (CCVRP) is a transportation problem which occurs when the objective is to minimize the sum of arrival times at customers, instead of the classical route length, subject to vehicle capacity constraints. This type of challenges arises whenever priority is given to the satisfaction of the customer need, e.g. vital goods supply or rescue after a natural disaster. The CCVRP generalizes the NP-hard traveling repairman problem (TRP), by adding capacity constraints and a homogeneous vehicle fleet. This paper presents the first upper and lower bounding procedures for this new problem. The lower bounds are derived from CCVRP properties. Upper bounds are given by a memetic algorithm using non-trivial evaluations of cost variations in the local search. Good results are obtained not only on the CCVRP, but also on the special case of the TRP, outperforming the only TRP metaheuristic published.

A memetic algorithm and a tabu search for the multi-compartment vehicle routing problem

A generalization of the traditional vehicle routing problem (VRP) is studied in this paper. Each customer may order several products, the vehicles are identical and have several compartments, each compartment being dedicated to one product. The demand of each customer for a product must be entirely delivered by one single vehicle. However, the different products required by a customer may be brought by several vehicles. Two algorithms to solve this problem are proposed: a memetic algorithm with a post-optimization phase based on path relinking, and a tabu search method. These algorithms are evaluated by adding compartments to classical VRP instances.

A distributed geographic information system for the daily car pooling problem

following the difficulty of public transport to adequately cover all passenger transportation needs, different innovative mobility services are emerging. Among those are car pooling services, which are based on the idea that sets of car owners having the same travel destination share their vehicles. Until now these systems have had a limited use due to lack of an efficient information processing and communication support. In this study an integrated system for the organization of a car pooling service is presented, using several current Information and Communication Technologies (ICTs) technologies: web, GIS and SMS. The core of the system is an optimization module which solves heuristically the specific routing problem. The system has been tested in a real-life case study.

The Team Orienteering Problem with Time Windows: An LP-based Granular Variable Neighborhood Search

The Team Orienteering Problem (TOP) is a known NP-hard problem that typically arises in vehicle routing and production scheduling contexts. In this paper we introduce a new solution method to solve the TOP with hard Time Window constraints (TOPTW). We propose a Variable Neighborhood Search (VNS) procedure based on the idea of exploring, most of the time, granular instead of complete neighborhoods in order to improve the algorithm’s efficiency without loosing effectiveness. The method provides a general way to deal with granularity for those routing problems based on profits and complicated by time constraints. Extensive computational results are reported on standard benchmark instances. Performance of the proposed algorithm is compared to optimal solution values, when available, or to best known solution values obtained by state-of-the-art algorithms. The method comes out to be, on average, quite effective allowing to improve the best know values for 25 test instances.

A Metaheuristic to Solve a Location-Routing Problem with Non-Linear Costs

The paper deals with a location-routing problem with non-linear cost functions. To the best of our knowledge, a mixed integer linear programming formulation for the addressed problem is proposed here for the first time. Since the problem is NP-hard exact algorithms are able to solve only particular cases, thus to solve more general versions heuristics are needed. The algorithm proposed in this paper is a combination of a p-median approach to find an initial feasible solution and a metaheuristic to improve the solution. It is a hybrid metaheuristic merging Variable Neighborhood Search (VNS) and Tabu Search (TS) principles and exploiting the synergies between the two. Computational results and conclusions close the paper.

An Exact Method for the Capacitated Location-Routing Problem

The capacitated location-routing problem (LRP) consists of opening one or more depots on a given set of a-priori defined depot locations, and designing, for each opened depot, a number of routes in order to supply the demands of a given set of customers. With each depot are associated a fixed cost for opening it and a capacity that limits the quantity that can be delivered to the customers. The objective is to minimize the sum of the fixed costs for opening the depots and the costs of the routes operated from the depots. This paper describes a new exact method for solving the LRP based on a set-partitioning-like formulation of the problem. The lower bounds produced by different bounding procedures, based on dynamic programming and dual ascent methods, are used by an algorithm that decomposes the LRP into a limited set of multicapacitated depot vehicle-routing problems (MCDVRPs). Computational results on benchmark instances from the literature show that the proposed method outperforms the current best-known exact methods, both for the quality of the lower bounds achieved and the number and the dimensions of the instances solved to optimality.