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

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

Solución de problemas estocásticos de localización-ruteo

96 Páginas.El problema de localización-ruteo estocástico (SLRP por sus siglas en inglés) es un problema muy común en empresas de manufactura, comercializadoras y transportadoras. El problema consiste en simultáneamente localizar uno o varios depósitos centrales entre un conjunto de ubicaciones potenciales, determinar un tamaño de flota y diseñar rutas para cada unos de los vehículos para visitar un conjunto de clientes considerando la incertidumbre que existe en algunos aspectos de la operación. En las soluciones presentadas en la literatura para este tipo de problemas se ha considerado mayoritariamente soluciones determinísticas o las soluciones estocásticas presentadas solo consideran en su mayoría la demanda como componente estocástico del sistema. La presente investigación propone un modelo para resolver la versión estocástica con incertidumbre en los costos de transporte y velocidades de los vehículos a través de un enfoque jerárquico de dos fases basado tanto en optimización como en simulación de eventos discretos. Se presenta una estrategia de selección aleatoria en la fase de localización; la fase de ruteo se resuelve empleando un algoritmo basado en colonia de hormigas, y finalmente se incluye al modelo el comportamiento estocástico del sistema a través de simulación de eventos discretos. Se presenta un análisis comparativo para validar la calidad de las soluciones obtenidas por el algoritmo y se realiza un estudio experimental permitiendo el análisis estadístico de resultados. Los resultados obtenidos permiten validar el presente enfoque como una buena herramienta de apoyo a la toma de decisiones para la localización de centros de distribución, la determinación de flotas de vehículos, la asignación de zonas de servicio y el ruteo de vehículos

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

A Metaheuristic for the Periodic Location-Routing Problem

International audienceThe well-known Vehicle Routing Problem (VRP) has been generalized toward tactical or strategic decision levels of companies but not both. The tactical extension or Periodic VRP (PVRP) plans trips over a multi-period horizon, subject to frequency constraints. The strategic extension or Location-Routing Problem (LRP) tackles location and routing decisions simultaneously as in most distribution systems interdependence between these decisions leads to low-quality solutions if depots are located first, regardless the future routes. Our goal is to combine for the first time the PVRP and LRP into the Periodic LRP or PLRP. A metaheuristic is proposed to solve large size instances of the PLRP. It is based on our Randomized Extended Clarke and Wright Algorithm (RECWA) for the LRP and it tries to take into consideration several decision levels when making a choice during the construction of a solution. The method is evaluated on three sets of instances and results are promising. Solutions are compared to the literature on particular cases such as one-day horizon (LRP) or one available depot (PVRP)