Sequential and parallel large neighborhood search algorithms for the periodic location routing problem

We propose a large neighborhood search (LNS) algorithm to solve the periodic location routing problem (PLRP). The PLRP combines location and routing decisions over a planning horizon in which customers require visits according to a given frequency and the specific visit days can be chosen. We use parallelization strategies that can exploit the availability of multiple processors. The computational results show that the algorithms obtain better results than previous solution methods on a set of standard benchmark instances from the literature

The Bi-objective Periodic Closed Loop Network Design Problem

© 2019 Elsevier Ltd. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Reverse supply chains are becoming a crucial part of retail supply chains given the recent reforms in the consumers’ rights and the regulations by governments. This has motivated companies around the world to adopt zero-landfill goals and move towards circular economy to retain the product’s value during its whole life cycle. However, designing an efficient closed loop supply chain is a challenging undertaking as it presents a set of unique challenges, mainly owing to the need to handle pickups and deliveries at the same time and the necessity to meet the customer requirements within a certain time limit. In this paper, we model this problem as a bi-objective periodic location routing problem with simultaneous pickup and delivery as well as time windows and examine the performance of two procedures, namely NSGA-II and NRGA, to solve it. The goal is to find the best locations for a set of depots, allocation of customers to these depots, allocation of customers to service days and the optimal routes to be taken by a set of homogeneous vehicles to minimise the total cost and to minimise the overall violation from the customers’ defined time limits. Our results show that while there is not a significant difference between the two algorithms in terms of diversity and number of solutions generated, NSGA-II outperforms NRGA when it comes to spacing and runtime.Peer reviewedFinal Accepted Versio

Metodología para crear rutas alimentadoras en sistemas de transporte masivo

Se propone una metodología para identificar rutas alimentadoras en zonas no conectadas para un sistema de transporte masivo, con el fin de aumentar la cobertura del servicio y mejorar el nivel de ocupación del sistema. La metodología propuesta consta de dos etapas: 1) estructurar escenarios de áreas no conectadas al sistema de transporte y 2) combinar técnicas heurísticas y exactas para resolver el problema de rutas alimentadoras. La metodología considera dentro de sus restricciones la duración de la ruta y la capacidad del vehículo alimentador. Para su modelamiento se establece una analogía entre los problemas del transporte de pasajeros y el problema de localización y ruteo, Location Routing Problem (LRP), que usualmente es aplicado a problemas de transporte de mercancías. La metodología de solución propuesta es una matheurística que combina las heurísticas Lin-Kernighan-Helsgaun (LKH) y ahorros con el algoritmo de ramificación y corte, Branch-and-Cut, aplicado sobre un modelo lineal entero mixto de partición de conjuntos (Set Partitioning) para LRP. Esta propuesta metodológica es validada con casos de prueba reales del sistema de transporte masivo de la ciudad de Pereira (Megabús), donde se consideran algunas zonas no conectadas del Área Metropolitana Centro Occidente, localizada en el eje cafetero colombiano

A Periodic Location Routing Problem for Collaborative Recycling

Motivated by collaborative recycling efforts for non-profit agencies, we study a variant of the periodic location routing problem, in which one decides the set of open depots from the customer set, the capacity of open depots, and the visit frequency to nodes, in an effort to design networks for collaborative pickup activities. We formulate this problem, highlighting the challenges introduced by these decisions. We examine the relative dfficulty introduced with each decision through exact solutions and a heuristic approach which can incorporate extensions of model constraints and solve larger instances. The work is motivated by a project with a network of hunger relief agencies (e.g., food pantries, soup kitchens and shelters) focusing on collaborative approaches to address their cardboard recycling challenges collectively. We present a case study based on data from the network. In this novel setting, we evaluate collaboration in terms of participation levels and cost impact. These insights can be generalized to other networks of organizations that may consider pooling resources

Análisis de viabilidad para implementar una red de logística Inversa en aerogeneradores de los parques eólicos

Partiendo de una revisión literaria sobre la logística inversa y la importancia de la aplicación de esta, se encontró que la problemática que se está viviendo actualmente con el cambio climático y con lo mucho que está sufriendo nuestro planeta es realmente grave; de esto nace la necesidad de proponer una solución alterna a la disposición de los desechos que se generan por los aerogeneradores, los cuales son producto una alternativa que busca ser amigable con el medio ambiente, pero que se identificó a lo largo de este proyecto que causa graves daños en el largo plazo al ambiente dado que no se tiene claro una forma óptima de reciclar estos dispositivos que generan bastantes desechos al final de su vida útil..

Beitrag zum Einsatz von Forecast-Methoden zur Modellierung dynamischer Location-Routing Probleme mit stochastischer Nachfrage

Die Logistikbranche ist mit 2,75 Millionen Angestellten und einem Umsatz von ca. 223 Millionen Euro eine der größten Wirtschaftsbranchen in Deutschland. Die Anforderungen an die Logistik steigen stetig. Das erfordert immer schnellere Belieferungskonzepte z.B. durch Lieferungen im Onlinekauf bereits am nächsten Tag oder präzisere Planung durch Just-In-Time Lieferung zu möglichst geringen Preisen. Die Ansprüche an schnelle Verteilungssysteme erzeugen höhere Kosten im Unternehmen und nehmen auch als Wettbewerbsvorteil einen immer größeren Stellenwert ein. Das Ziel des Managements von Industrieunternehmen ist es deshalb, die Senkung von Kosten durch Auslegung von effektiven und effizienten Vertriebssystemen und Netzwerken zu gewährleisten. Damit gelangen Standort- und/oder Tourenplanung zunehmend in den Fokus des Interesses. Die Optimierung von Standorten und Touren ist in vielen Fällen aber voneinander inhaltlich abhängig, da zum Beispiel bei der Warenverteilung die Transportkosten von dem Standort des Logistikzentrums, also dem Start- und Endpunkt der Tour, abhängen und dadurch ein Einfluss auf die Lösung des jeweils anderen Problems entsteht. Hier setzt die Theorie der kombinierten Standort-Tourenplanung an, bei der gleichzeitig durch die Kombination von möglichen Standorten und möglichen Touren das Optimum gesucht wird. Dieser Ansatz wird in der wissenschaftlicher Literatur als Location-Routing Problem (LRP) bezeichnet. In der vorliegenden Arbeit werden erstmalig dynamische (mit Berücksichtigung mehrerer Planungsperioden) Modelle des LRP mit einer deterministischen (bekannter) und stochastischen (unsicherer) Nachfrage aufgestellt und untersucht. Zur Modellierung solcher kombinatorischer Optimierungsprobleme werden konkrete Beispielfälle, sogenannte Instanzen, verwendet. Für neue wissenschaftliche Ansätze existieren jedoch noch nicht genug Instanzbibliotheken, so dass vorhandene Instanzen für die eigene Problemstellung modifiziert oder eigene Instanzen generiert werden. Zu diesem Zweck wurde im Zuge dieser Arbeit für stochastische Nachfragen ein auf Prognosen der exponentiellen Glättung basiertes Tool zum Erstellen von synthetischen Zeitreihen entwickelt und neue Instanzen für das PLRP generiert. Anhand zuvor generierter Instanzen werden anschließend Modelle des PLRP mit der eigens für diese Arbeit entwickelten und auf dem Einsatz von genetischen Algorithmen basierten Optimierungssoftware AdL(e)R (Advanced Location Routing) analysiert, indem anhand von Szenarien die zuvor erläuterten Modelle des dynamischen LRP ausgewertet werden. Dabei werden die Forecast-Gesamtoptimierung, die vorperiodische Optimierung mit den Modellen mit realen Nachfragen verglichen. Im Anschluss erfolgt dann die Gegenüberstellung der beiden Methoden mit den dynamischen LRP-Modellen mit realen Nachfragen.The logistic sector with its 2,75 million employees and a sales volume of approx. 223 million Euros is one of the largest economic sectors in Germany. The requirements for logistics are steadily increasing. This entails increasingly rapid delivery concepts, e.g. in the form of online sales deliveries on the next day or more precise planning by just-in-time delivery at the lowest possible prices. In the companies, the demand for quicker distribution systems generate higher costs and also become more and more important as a competitive edge. Therefore, the objective of the managing of industrial enterprises is to ensure the reduction of costs by designing effective and efficient sales and distribution systems and networks. Thereby, location and/or route planning shift more and more into the focus of interest. In many cases, the optimization of locations and tours is interdependent regarding the content, as in the distribution of goods, for example, the costs of transport depend on the location of the logistic center, i.e. the start and end of the tour, and thereby an influence on the solution of the each with other problem arises. This is where the theory of combined location-tour planning applies where by simultaneously combining possible locations and possible tours the optimum is searched. In scientific literature this approach is called Location-Routing Problem (LRP). In the present paper, the dynamic models of the LRP (considering several periods of planning) are formed and investigated with a stochastic (uncertain) demand. So-called entities are used for modelling combinatorial optimization problems. Since there are still not enough entity libraries in the new problem categories in order to enable their easy transfer to new scientific approaches, existing entities are modified for the given problem and generated newly. For this reason, a forecast-based tool was developed for creating synthetic time courses for deterministic and stochastic demands, with which new entities are generated for the PLRP. On the basis of these new entities, new models of the PLRP are analyzed with the tool called AdL(e)R (Advanced Location Routing), which has specifically been developed for this paper. The program is based on the application of genetic algorithms. In the further course of the paper, lower bounds for the evaluation of the quality of heuristic solutions are identified and corresponding models for dynamic cases of the LRP formed. Finally, the aforementioned models of the dynamic LRP are evaluated by means of scenarios. Thereby, the Forecast total optimization and the optimization from the previous period are compared. Subsequently, the comparison of the two methods with the dynamic LRP models with real demands is carried out

An ELSxPath Relinking Hybrid for the Periodic Location-Routing Problem

International audienceThe well-known Vehicle Routing Problem (VRP) has been deeply studied over the last decades. Nowadays, generalizations of VRP are developed toward tactical or strategic decision levels of companies. The tactical extension plans a set of trips over a multiperiod horizon, subject to frequency constraints. The related problem is called the Periodic VRP (PVRP). On the other hand, the strategic extension is motivated by interdependent depot location and routing decisions in most distribution systems. Low-quality solutions are obtained if depots are located first, regardless the future routes. In the Location-Routing Problem (LRP), location and routing decisions are simultaneously tackled. The goal here is to combine the PVRP and LRP into an even more realistic problem covering all decision levels: the Periodic LRP or PLRP. A hybrid evolutionary algorithm is proposed to solve large size instances of the PLRP. First, an individual representing an assignment of customers to combinations of visit days is randomly generated. Then, a heuristic based on the Randomized Extended Clarke and Wright Algorithm (RECWA) creates feasible solutions. The evolution operates through an Evolutionary Local Search (ELS) on visit days assignments. The algorithm is hybridized with a Path Relinking between individuals from an elite list. The method is evaluated on three sets of instances and solutions are compared to the literature on particular cases such as one-day horizon (LRP) or one depot (PVRP). This metaheuristic outperforms the previous methods for the PLRP