Strategic and operational decision-making in expanding supply chains for LNG as a fuel

The European Union aims for a 40% reduction in greenhouse gas emissions by 2030, compared to 1990 levels, and recognizes the opportunities of Liquefied Natural Gas (LNG) as an alternative fuel for transportation to reach this goal. The lack of a mature supply chain for LNG as a fuel results in a need to invest in new (satellite) terminals, bunker barges and tanker trucks. This network design problem can be defined as a Two-Echelon Capacitated Location Routing Problem with Split Deliveries (2E-CLRPSP). An important feature of this problem is that direct deliveries are allowed from terminals, which makes the problem much harder to solve than the existing location routing literature suggests. In this paper, we improve the performance of a hybrid exact algorithm and apply our algorithm to a real world network design problem related to the expansion of the European supply chain for LNG as a fuel. We show that satellite terminals and bunker barges become an interesting option when demand for LNG grows and occurs further away from the import terminal. In those situations, the large investments associated with LNG satellites and bunker barges are offset by reductions in operational costs of the LNG tanker trucks

Research trends in combinatorial optimization

Acknowledgments This work has been partially funded by the Spanish Ministry of Science, Innovation, and Universities through the project COGDRIVE (DPI2017-86915-C3-3-R). In this context, we would also like to thank the Karlsruhe Institute of Technology. Open access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD

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 newmetaheuristic 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

Two-phase method and Lagrangian relaxation to solve the Bi-Objective Set Covering Problem

This paper deals with the Bi-Objective Set Covering Problem, which is a generalization of the well-known Set Covering Problem. The proposed approach is a two-phase heuristic method which has the particularity to be a constructive method using the primal-dual Lagrangian relaxation to solve single objective Set Covering problems. The results show that this algorithm finds several potentially supported and unsupported solutions. A comparison with an exact method (up to a medium size), shows that many Pareto-optimal solutions are retrieved and that the other solutions are well spread and close to the optimal ones. Moreover, the method developed compares favorably with the Pareto Memetic Algorithm proposed by Jaszkiewicz

A relax-and-price heuristic for the inventory-location-routing problem

This paper considers the problem of designing a supply chain assuming routing decisions. The objective is to select a subset of depots to open from a set of candidates, the inventory policies for a two-echelon system, and the set of routes to perform distribution from the upper echelon to the next by a homogeneous fleet of vehicles over a finite planning horizon considering deterministic demand. To solve the problem, a partition is proposed using a Dantzig–Wolfe formulation on the routing variables. A hybridization between column generation, Lagrangian relaxation, and local search is presented within a heuristic procedure. Results demonstrate the capability of the algorithm to compute high quality solutions and empirically estimate the improvement in the cost function of the proposed model at up to 9% compared to the sequential approach. Furthermore, the suggested pricing problem is a new variant of the shortest path problem with applications in urban transportation and telecommunications

Hybrid Heuristic for the Inventory Location-Routing Problem

Resolver el problema de localización y ruteo de inventarios optimiza simultáneamente el diseño de la cadena de suministro y sus costos operacionales. Los supuestos del modelo incluyen el hecho que los vehículos pueden visitar más de un cliente por ruta y se incluyen las decisiones de inventario para un sistema con múltiples depósitos, con múltiples puntos de venta para un horizonte de planeación discreto. El problema es determinar el conjunto de depósitosporabrir, las cantidades por enviar desde los proveedores a los depósitos y de los depósitos a los puntos de venta, y la secuencia en que los puntos de venta serán visitados por una flota de vehículos homogénea. Un modelo de programación entera-mixta se propone para describir el problema. Un método hibrido que involucra un enfoque exacto dentro de un esquema heurístico es presentado. Su desempeño se prueba con instancias del problema de localizacion y ruteo, y de ruteo de inventarios.Solving the Inventory Location-Routing Problem can been seen as an approach to optimize both a supply chain design and its operations costs. Assumptions consider that vehicles might visit more than one retailer per route and that inventory management decisions are included for a multi-depot, multi-retailer system with storage capacity over a discrete time planning horizon. The problem is to determine the set of candidate depots to open, the quantities to ship from suppliers to depots and from depots to retailers per period, and the sequence in which retailers are replenished by an homogeneous capacitated fleet of vehicles. A mixed-integer linear programming model is proposed to describe the problem. Since the model is not able to solve exactly the targeted instances within a reasonable computation time, a hybrid method, embedding an exact approach within a heuristic scheme, is presented. Its performance is tested over instances for the inventory location routing, location-routing and inventory-routing problems

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

International audienceMost 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