The location routing problem with facility sizing decisions

The location routing problem (LRP) integrates operational decisions on vehicle routing operations with strategic decisions on the location of the facilities or depots from which the distribution will take place. In other words, it combines the well-known vehicle routing problem (VRP) with the facility location problem (FLP). Hence, the LRP is an NP-hard combinatorial optimization problem, which justifies the use of metaheuristic approaches whenever large-scale instances need to be solved. In this paper, we explore a realistic version of the LRP in which facilities of different capacities are considered, i.e., the manager has to consider not only the location but also the size of the facilities to open. In order to tackle this optimization problem, three mixed-integer linear formulations are proposed and compared. As expected, they have been proved to be cost- and time- inefficient. Hence, a biased-randomized iterated local search algorithm is proposed. Classical instances for the LRP with homogeneous facilities are naturally extended to test the performance of our approach.Peer ReviewedPostprint (published version

Combining heuristics with simulation and fuzzy logic to solve a flexible-size location routing problem under uncertainty

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033). In addition, it has received the support of the Doctoral School at the Universitat Oberta de Catalunya (Spain) and the Universidad de La Sabana (INGPhD-12-2020).Peer ReviewedPostprint (published version

Incorporating biodiversity responses to land use change scenarios for preventing emerging zoonotic diseases in areas of unknown host-pathogen interactions

The need to reconcile food production, the safeguarding of nature, and the protection of public health is imperative in a world of continuing global change, particularly in the context of risks of emerging zoonotic disease (EZD). In this paper, we explored potential land use strategies to reduce EZD risks using a landscape approach. We focused on strategies for cases where the dynamics of pathogen transmission among species were poorly known and the ideas of “land-use induced spillover” and “landscape immunity” could be used very broadly. We first modeled three different land-use change scenarios in a region of transition between the Cerrado and the Atlantic Forest biodiversity hotspots. The land-use strategies used to build our scenarios reflected different proportions of native vegetation cover, as a proxy of habitat availability. We then evaluated the effects of the proportion of native vegetation cover on the occupancy probability of a group of mammal species and analyzed how the different land-use scenarios might affect the distribution of species in the landscape and thus the risk of EZD. We demonstrate that these approaches can help identify potential future EZD risks, and can thus be used as decision-making tools by stakeholders, with direct implications for improving both environmental and socio-economic outcomes

Global collision-risk hotspots of marine traffic and the world’s largest fish, the whale shark

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Womersley, F. C., Humphries, N. E., Queiroz, N., Vedor, M., da Costa, I., Furtado, M., Tyminski, J. P., Abrantes, K., Araujo, G., Bach, S. S., Barnett, A., Berumen, M. L., Bessudo Lion, S., Braun, C. D., Clingham, E., Cochran, J. E. M., de la Parra, R., Diamant, S., Dove, A. D. M., Dudgeon, C. L., Erdmann, M. V., Espinoza, E., Fitzpatrick, R., González Cano, J., Green, J. R., Guzman, H. M., Hardenstine, R., Hasan, A., Hazin, F. H. V., Hearn, A. R., Hueter, R. E., Jaidah, M. Y., Labaja, J., Ladinol, F., Macena, B. C. L., Morris Jr., J. J., Norman, B. M., Peñaherrera-Palmav, C., Pierce, S. J., Quintero, L. M., Ramırez-Macías, D., Reynolds, S. D., Richardson, A. J., Robinson, D. P., Rohner, C. A., Rowat, D. R. L., Sheaves, M., Shivji, M. S., Sianipar, A. B., Skomal, G. B., Soler, G., Syakurachman, I., Thorrold, S. R., Webb, D. H., Wetherbee, B. M., White, T. D., Clavelle, T., Kroodsma, D. A., Thums, M., Ferreira, L. C., Meekan, M. G., Arrowsmith, L. M., Lester, E. K., Meyers, M. M., Peel, L. R., Sequeira, A. M. M., Eguıluz, V. M., Duarte, C. M., & Sims, D. W. Global collision-risk hotspots of marine traffic and the world’s largest fish, the whale shark. Proceedings of the National Academy of Sciences of the United States of America, 119(20), (2022): e2117440119, https://doi.org/10.1073/pnas.2117440119.Marine traffic is increasing globally yet collisions with endangered megafauna such as whales, sea turtles, and planktivorous sharks go largely undetected or unreported. Collisions leading to mortality can have population-level consequences for endangered species. Hence, identifying simultaneous space use of megafauna and shipping throughout ranges may reveal as-yet-unknown spatial targets requiring conservation. However, global studies tracking megafauna and shipping occurrences are lacking. Here we combine satellite-tracked movements of the whale shark, Rhincodon typus, and vessel activity to show that 92% of sharks’ horizontal space use and nearly 50% of vertical space use overlap with persistent large vessel (>300 gross tons) traffic. Collision-risk estimates correlated with reported whale shark mortality from ship strikes, indicating higher mortality in areas with greatest overlap. Hotspots of potential collision risk were evident in all major oceans, predominantly from overlap with cargo and tanker vessels, and were concentrated in gulf regions, where dense traffic co-occurred with seasonal shark movements. Nearly a third of whale shark hotspots overlapped with the highest collision-risk areas, with the last known locations of tracked sharks coinciding with busier shipping routes more often than expected. Depth-recording tags provided evidence for sinking, likely dead, whale sharks, suggesting substantial “cryptic” lethal ship strikes are possible, which could explain why whale shark population declines continue despite international protection and low fishing-induced mortality. Mitigation measures to reduce ship-strike risk should be considered to conserve this species and other ocean giants that are likely experiencing similar impacts from growing global vessel traffic.Funding for data analysis was provided by the UK Natural Environment Research Council (NERC) through a University of Southampton INSPIRE DTP PhD Studentship to F.C.W. Additional funding for data analysis was provided by NERC Discovery Science (NE/R00997/X/1) and the European Research Council (ERC-AdG-2019 883583 OCEAN DEOXYFISH) to D.W.S., Fundação para a Ciência e a Tecnologia (FCT) under PTDC/BIA/28855/2017 and COMPETE POCI-01–0145-FEDER-028855, and MARINFO–NORTE-01–0145-FEDER-000031 (funded by Norte Portugal Regional Operational Program [NORTE2020] under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund–ERDF) to N.Q. FCT also supported N.Q. (CEECIND/02857/2018) and M.V. (PTDC/BIA-COM/28855/2017). D.W.S. was supported by a Marine Biological Association Senior Research Fellowship. All tagging procedures were approved by institutional ethical review bodies and complied with all relevant ethical regulations in the jurisdictions in which they were performed. Details for individual research teams are given in SI Appendix, section 8. Full acknowledgments for tagging and field research are given in SI Appendix, section 7. This research is part of the Global Shark Movement Project (https://www.globalsharkmovement.org)

Global Spatial Risk Assessment of Sharks Under the Footprint of Fisheries

Effective ocean management and conservation of highly migratory species depends on resolving overlap between animal movements and distributions and fishing effort. Yet, this information is lacking at a global scale. Here we show, using a big-data approach combining satellite-tracked movements of pelagic sharks and global fishing fleets, that 24% of the mean monthly space used by sharks falls under the footprint of pelagic longline fisheries. Space use hotspots of commercially valuable sharks and of internationally protected species had the highest overlap with longlines (up to 76% and 64%, respectively) and were also associated with significant increases in fishing effort. We conclude that pelagic sharks have limited spatial refuge from current levels of high-seas fishing effort. Results demonstrate an urgent need for conservation and management measures at high-seas shark hotspots and highlight the potential of simultaneous satellite surveillance of megafauna and fishers as a tool for near-real time, dynamic management

A simheuristic algorithm for the capacitated location routing problem with stochastic demands

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Simulation on 30 Oct 2019, available online: http://www.tandfonline.com/10.1080/17477778.2019.1680262[EN] The capacitated location routing problem (CLRP) integrates a facility location problem with a multi-depot vehicle routing problem. We consider the CLRP with stochastic demands, whose specific values are only revealed upon reaching each customer. The main goal is to minimise the expected costs of: (i) opening facilities, (ii) using a fleet of vehicles, (iii) executing a routing plan, and (iv) applying corrective actions. The latter are required whenever a route failure occurs due to unexpected high demands. We propose a simheuristic algorithm hybridizing simulation with an iterated local search metaheuristic, aimed at: (i) proposing a safety-stock policy to diminish the likelihood of route failure; and, (ii) estimating the expected cost and the reliability of each "elite" solution. We assess our approach on classical CLRP benchmarks, which are later extended to consider demand uncertainty. Finally, we also discuss the effects of the safety-stock policy on costs and reliability.This work has been partially supported by Rhenus Freight Logistics GmbH & Co. KG. We also acknowledge the support of the Erasmus+ Program (2019-I-ES01-KA103- 062602).Quintero-Araujo, CL.; Guimarans, D.; Juan, AA. (2021). A simheuristic algorithm for the capacitated location routing problem with stochastic demands. Journal of Simulation. 15(3):217-234. https://doi.org/10.1080/17477778.2019.168026221723415

A simheuristic algorithm for the capacitated location routing problem with stochastic demands

19 páginasThe capacitated location routing problem (CLRP) integrates a facility location problem with a multi-depot vehicle routing problem. We consider the CLRP with stochastic demands, whose specific values are only revealed upon reaching each customer. The main goal is to minimise the expected costs of: (i) opening facilities, (ii) using a fleet of vehicles, (iii) executing a routing plan, and (iv) applying corrective actions. The latter are required whenever a route failure occurs due to unexpected high demands. We propose a simheuristic algorithm hybridizing simulation with an iterated local search metaheuristic, aimed at: (i) proposing a safety-stock policy to diminish the likelihood of route failure; and, (ii) estimating the expected cost and the reliability of each “elite” solution. We assess our approach on classical CLRP benchmarks, which are later extended to consider demand uncertainty. Finally, we also discuss the effects of the safety-stock policy on costs and reliability

El problema de enrutamiento de ubicación usando vehículos eléctricos con distancia restringida

16 páginasThe introduction of Electric Vehicles (EVs) in modern fleets facilitates a shift towards greener road transportation practices. However, the driving ranges of EVs are limited by the duration of their batteries, which raises some operational challenges. This paper discusses the Location Routing Problem with a Constrained Distance (LRPCD), which is a natural extension of the Location Routing Problem when EVs are utilized. A fast multi-start heuristic and a metaheuristic are proposed to solve the LRPCD. The former combines biased-randomization techniques with the well-known Tillman’s heuristic for the Multi-Depot Vehicle Routing Problem. The latter incorporates the biased-randomized approach into the Variable Neighborhood Search (VNS) framework. A series of computational experiments show that the multi-start heuristic is able to generate good-quality solutions in just a few seconds, while the biased-rendomized VNS metaheuristic provides higher-quality solutions by employing more computational time.La introducción de vehículos eléctricos (EV) en flotas modernas facilita un cambio hacia prácticas de transporte por carretera más ecológicas. Sin embargo, la autonomía de conducción de los vehículos eléctricos está limitada por la duración de sus baterías, lo que plantea algunos desafíos operativos. Este documento analiza el problema de enrutamiento de ubicación con una distancia restringida (LRPCD), que es una extensión natural del problema de enrutamiento de ubicación cuando se utilizan vehículos eléctricos. Se propone una heurística rápida de inicio múltiple y una metaheurística para resolver el LRPCD. El primero combina técnicas de aleatorización sesgada con la conocida heurística de Tillman para el problema de enrutamiento de vehículos de varios depósitos. Este último incorpora el enfoque aleatorizado sesgado en la búsqueda de vecindario variable .(VNS) marco. Una serie de experimentos computacionales muestran que la heurística de inicio múltiple puede generar soluciones de buena calidad en solo unos segundos, mientras que la metaheurística VNS aleatoria y sesgada proporciona soluciones de mayor calidad al emplear más tiempo computacional

A biased‐randomized metaheuristic for the capacitated location routing problem

20 páginasThe location routing problem (LRP) involves the three key decision levels in supply chain design, that is,strategic, tactical, and operational levels. It deals with the simultaneous decisions of (a) locating facilities(e.g., depots or warehouses), (b) assigning customers to facilities, and (c) defining routes of vehicles departingfrom and finishing at each facility to serve the associated customers’ demands. In this paper, a two-phasemetaheuristic procedure is proposed to deal with the capacitated version of the LRP (CLRP). Here, decisionsmust be made taking into account limited capacities of both facilities and vehicles. In the first phase (selectionof promising solutions), we determine the depots to be opened, perform a fast allocation of customers to opendepots, and generate a complete CLRP solution using a fast routing heuristic. This phase is executed severaltimes in order to keep the most promising solutions. In the second phase (solution refinement), for each of theselected solutions we apply a perturbation procedure to the customer allocation followed by a more intensiverouting heuristic. Computational experiments are carried out using well-known instances from the literature.Results show that our approach is quite competitive since it offers average gaps below 0.4% with respect tothe best-known solutions (BKSs) for all tested sets in short computational times

Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm