Philippe Lacomme

Bio: Philippe Lacomme is an academic researcher from University of Auvergne. The author has contributed to research in topics: Vehicle routing problem & Arc routing. The author has an hindex of 24, co-authored 133 publications receiving 2346 citations. Previous affiliations of Philippe Lacomme include Centre national de la recherche scientifique & University of Technology of Troyes.

Competitive Memetic Algorithms for Arc Routing Problems

Abstract: The Capacitated Arc Routing Problem or CARP arises in applications like waste collection or winter gritting Metaheuristics are tools of choice for solving large instances of this NP-hard problem The paper presents basic components that can be combined into powerful memetic algorithms (MAs) for solving an extended version of the CARP (ECARP) The best resulting MA outperforms all known heuristics on three sets of benchmark files containing in total 81 instances with up to 140 nodes and 190 edges In particular, one open instance is broken by reaching a tight lower bound designed by Belenguer and Benavent, 26 best-known solutions are improved, and all other best-known solutions are retrieved

A Genetic Algorithm for the Capacitated Arc Routing Problem and Its Extensions

Abstract: The NP-hard Capacitated Arc Routing Problem (CARP) allows to model urban waste collection or road gritting, for instance. Exact algorithms are still limited to small problems and metaheuristics are required for large scale instances. The paper presents the first genetic algorithm (GA) published for the CARP. This hybrid GA tackles realistic extensions like mixed graphs or prohibited turns. It displays excellent results and outperforms the best metaheuristics published when applied to two standard sets of benchmarks: the average deviations to lower bounds are 0.24 % and 0.69 % respectively, a majority of instances are solved to optimality, and eight best known solutions are improved.

Order-first split-second methods for vehicle routing problems: A review

Abstract: Cluster-first route-second methods like the sweep heuristic (Gillett and Miller, 1974) are well known in vehicle routing. They determine clusters of customers compatible with vehicle capacity and solve a traveling salesman problem for each cluster. The opposite approach, called route-first cluster-second, builds a giant tour covering all customers and splits it into feasible trips. Cited as a curiosity for a long time but lacking numerical evaluation, this technique has nevertheless led to successful metaheuristics for various vehicle routing problems in the last decade. As many implementations consider an ordering of customers instead of building a giant tour, we propose in this paper the more general name of ordering-first split-second methods. This article shows how this approach can be declined for different vehicle routing problems and reviews the associated literature, with more than 70 references.

The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery

Abstract: Once limited to the military domain, unmanned aerial vehicles are now poised to gain widespread adoption in the commercial sector. One such application is to deploy these aircraft, also known as drones, for last-mile delivery in logistics operations. While significant research efforts are underway to improve the technology required to enable delivery by drone, less attention has been focused on the operational challenges associated with leveraging this technology. This paper provides two mathematical programming models aimed at optimal routing and scheduling of unmanned aircraft, and delivery trucks, in this new paradigm of parcel delivery. In particular, a unique variant of the classical vehicle routing problem is introduced, motivated by a scenario in which an unmanned aerial vehicle works in collaboration with a traditional delivery truck to distribute parcels. We present mixed integer linear programming formulations for two delivery-by-drone problems, along with two simple, yet effective, heuristic solution approaches to solve problems of practical size. Solutions to these problems will facilitate the adoption of unmanned aircraft for last-mile delivery. Such a delivery system is expected to provide faster receipt of customer orders at less cost to the distributor and with reduced environmental impacts. A numerical analysis demonstrates the effectiveness of the heuristics and investigates the tradeoffs between using drones with faster flight speeds versus longer endurance.

Agent-Based Systems for Intelligent Manufacturing: A State-of-the-Art Survey

Abstract: Agent technology has been considered as an important approach for developing distributed intelligent manufacturing systems. A number of researchers have attempted to apply agent technology to manufacturing enterprise integration, supply chain management, manufacturing planning, scheduling and control, materials handling, and holonic manufacturing systems. This paper gives a brief survey of some related projects in this area, and discusses some key issues in developing agent-based manufacturing systems such as agent technology for enterprise integration and supply chain management, agent encapsulation, system architectures, dynamic system reconfiguration, learning, design and manufacturability assessments, distributed dynamic scheduling, integration of planning and scheduling, concurrent scheduling and execution, factory control structures, potential tools and standards for developing agent-based manufacturing systems. An extensive annotated bibliography is provided.

Survey of Green Vehicle Routing Problem: Past and future trends

Abstract: Green Logistics has emerged as the new agenda item in supply chain management. The traditional objective of distribution management has been upgraded to minimizing system-wide costs related to economic and environmental issues. Reflecting the environmental sensitivity of vehicle routing problems (VRP), an extensive literature review of Green Vehicle Routing Problems (GVRP) is presented. We provide a classification of GVRP that categorizes GVRP into Green-VRP, Pollution Routing Problem, VRP in Reverse Logistics, and suggest research gaps between its state and richer models describing the complexity in real-world cases. The purpose is to review the most up-to-date state-of-the-art of GVRP, discuss how the traditional VRP variants can interact with GVRP and offer an insight into the next wave of research into GVRP. It is hoped that OR/MS researchers together with logistics practitioners can be inspired and cooperate to contribute to a sustainable industry.