Fabien Lehuédé

The Dial-A-Ride Problem with Transfers

The Dial-A-Ride Problem with Transfers (DARPT) consists in defining a set of routes that satisfy transportation requests of users between a set of pickup points and a set of delivery points, in the presence of ride time constraints. Users may change vehicles during their trip. This change of vehicle, called a transfer, is made at specific locations called transfer points. Solving the DARPT involves modeling and algorithmic difficulties. In this paper we provide a solution method based on an Adaptive Large Neighborhood Search (ALNS) metaheuristic and explain how to check the feasibility of a request insertion. The method is evaluated on real-life and generated instances. Experiments show that savings due to transfers can be up to 8% on real-life instances.

An adaptive large neighborhood search for the two-echelon multiple-trip vehicle routing problem with satellite synchronization

The two-echelon vehicle routing problem (2E-VRP) consists in making deliveries to a set of customers using two distinct fleets of vehicles. First-level vehicles pick up requests at a distribution center and bring them to intermediate sites. At these locations, the requests are transferred to second-level vehicles, which deliver them. This paper addresses a variant of the 2E-VRP that integrates constraints arising in city logistics such as time window constraints, synchronization constraints, and multiple trips at the second level. The corresponding problem is called the two-echelon multiple-trip vehicle routing problem with satellite synchronization (2E-MTVRP-SS). We propose an adaptive large neighborhood search to solve this problem. Custom destruction and repair heuristics and an efficient feasibility check for moves have been designed and evaluated on modified benchmarks for the VRP with time windows.

Efficient feasibility testing for request insertion in the pickup and delivery problem with transfers

The Pickup and Delivery Problem with Transfers (PDPT) consists of defining a set of minimum cost routes in order to satisfy a set of transportation requests, allowing them to change vehicles at specific locations. In this problem, routes are strongly interdependent due to request transfers. Then it is critical to efficiently check if inserting a request into a partial solution is feasible or not. In this article, we present a method to perform this check in constant time.

Optimization of a city logistics transportation system with mixed passengers and goods

In this paper, we propose a mathematical model and an adaptive large neighborhood search to solve a two-tiered transportation problem. This problem arises in a prospective study that aims at designing an innovative distribution system for goods in congested city cores. In the first tier, goods are transported in city buses from a consolidation and distribution center to a set of bus stops. The main idea is to use the buses spare capacity to drive the goods to the city core. In the second tier, final customers are distributed by a fleet of near-zero emissions city freighters. This system requires transferring the goods from buses to city freighters at the bus stops. We model the corresponding optimization problem as a variant of the pickup and delivery problem with transfers and solve it with an adaptive large neighborhood search. To evaluate its results, lower bounds are calculated with a column generation approach. The algorithm is assessed on data sets derived from a field study in the medium-sized city of La Rochelle in France.

A branch-and-cut-and-price approach for the pickup and delivery problem with shuttle routes

The main objective of this communication is to show how realistic-sized instances of the Pickup and Delivery Problem with Transfers (PDPT) can be solved to optimality with a branch-and-cut-and-price method, under realistic hypotheses. We introduce the Pickup and Delivery Problem with Shuttle routes (PDP-S) which is a special case of the PDPT relying on a structured network with two categories of routes. {\em Pickup routes} visit a set of pickup points independently of their delivery points and end at one delivery point. {\em Shuttle routes} are direct trips between two delivery points. We propose a path based formulation of the PDP-S and solve it with a branch-and-cut-and-price algorithm. We show that this approach is able to solve real-life instances with up to 87 transportation requests.

A multi-criteria large neighbourhood search for the transportation of disabled people

This paper addresses the problem of optimizing the transportation of disabled persons from home to specialized centres or schools. It is modelled as a Dial-a-ride problem (DARP), where several people share the same destination. Particular emphasis is placed on the objective function in order to consider several potentially conflicting interests. We propose a multi-criteria model from Multi-attribute Utility Theory based on the Choquet integral. The resulting multi-criteria DARP is then solved with a large neighbourhood search algorithm. This method includes classical destroy and repair heuristics as well as new operators exploiting the shared destination feature and criterion-specific operators. The algorithm is evaluated on a set of 14 real-world instances in the field of health care logistics, with up to 200 requests and 51 destination points.

A new consistent vehicle routing problem for the transportation of people with disabilities

In this article, we address a problem of the transportation of people with disabilities where customers are served on an almost daily basis and expect some consistency in the service. We introduce an original model for the time-consistency of the service, based on so-called time-classes. We then define a new multiday vehicle routing problem VRP that we call the Time-Consistent VRP. We address the solution of this new problem with a large neighborhood search heuristic. Each iteration of the heuristic requires solving a complex VRP with multiple time windows and no waiting time which we tackle with a heuristic branch-and-price method. Computational tests are conducted on benchmark sets and modified real-life instances. Results demonstrate the efficiency of the method and highlight the impact of time-consistency on travel costs.

Preferred solutions computed with a label setting algorithm based on Choquet integral for multi-objective shortest paths

The problem investigated in this paper concerns the integration of a decision maker preference model within a labeling algorithm for the multi-objective shortest path problem. The aim is to use a preference model built a priori for computing efficiently exact preferred solutions. The approach is based on the Choquet integral, which can model not only relative importances but also interactions between criteria. The paper introduces Choquet dominance rules, which replaces the Pareto dominance. The rules are integrated within the label setting algorithm originally proposed in 1984 by Martins. Numerical experiments report significant performance improvements, and conclude on the efficiency of the rules for reducing the search space.

A two-phase heuristic for full truckload routing and scheduling with split delivery and resource synchronization in public works

This paper presents a two-phase method to solve a routing and scheduling problem that arises in public works. In this problem, the quantity of demands generally exceeds the capacity of a truck. As a result, demands have to be split into full truckloads, taking into account the various capacities in the heterogeneous fleet of vehicles that perform the transportation. Full truckload routes have to be designed and scheduled according to construction or loading constraints on pickup and delivery sites. In the first phase, we propose a linear program to split demands into full truckload requests. The second phase is a heuristic that solves a heterogeneous full truckload pickup and delivery problem with time windows and resource synchronization. The method is evaluated on instances from a real case study.

A Lower Bound of the Choquet Integral Integrated Within Martins’ Algorithm

The problem investigated in this work concerns the integration of a decision-maker preference model within an exact algorithm in multiobjective combinatorial optimization. Rather than computing the complete set of efficient solutions and choosing a solution afterwards, our aim is to efficiently compute one solution satisfying the decision maker preferences elicited a priori. The preference model is based on the Choquet integral. The reference optimization problem is the multiobjective shortest path problem, where Martins’ algorithm is used. A lower bound of the Choquet integral is proposed that aims to prune useless partial paths at the labeling stage of the algorithm. Various procedures exploiting the proposed bound are presented and evaluated on a collection of benchmarks. Numerical experiments show significant improvements compared to the exhaustive enumeration of solutions.