Candidate Locations Research Articles

A COMBINATION OF AHP AND GRA FOR FURNITURE MANUFACTURING FACILITY LOCATION SELECTION IN A HYBRID FUZZY ENVIRONMENT

This paper aims to solve the location selection problem for a newly established furniture facility. The choice of location is directly related to the costs businesses should bear to continue their operations throughout their life cycle. Therefore, the location of the establishment is very critical for enterprises. For this problem, we propose a combined method with fuzzy analytic hierarchy process (F AHP) and fuzzy grey relation analysis (F-GRA). F-GRA is used to rank the alternatives, while the priority weights of the criteria are estimated via F-AHP. The use of multi-criteria decision-making methods is not only for cost optimization but also optimization of many parameters (technical, social, economic, demographic, etc.) simultaneously to ensure the sustainability of enterprises. In this problem, the main factors considered are closeness, environment, infrastructure, and economy, and the candidate locations are selected as Antalya, Duzce, Gaziantep, Kayseri, Kocaeli, cities in Turkey. The application steps of the combined method are defined, and the result of the case problem is represented.

Truthful two-facility location with candidate locations

We study a truthful two-facility location problem in which a set of agents have private positions on the line of real numbers and known approval preferences over two different facilities. Given the locations of the two facilities, the cost of an agent is the total distance from the facilities she approves. The goal is to decide where to place the facilities from a given finite set of candidate locations so as to (a) approximately optimize desired social objectives, and (b) incentivize the agents to truthfully report their private positions. We focus on the class of deterministic strategyproof mechanisms and show bounds on their approximation ratio in terms of the social cost (i.e., the total cost of the agents) and the max cost for several classes of instances depending on the preferences of the agents over the facilities.

Gut-to-brain regulation of Drosophila aging through neuropeptide F, insulin, and juvenile hormone

Dietary restriction (DR) slows aging in many animals, while in some cases, the sensory signals from diet alone are sufficient to retard or accelerate lifespan. The digestive tract is a candidate location to sense nutrients, where neuropeptides secreted by enteroendocrine cells (EEC) produce systemic signals in response to food. Here, we measure how Drosophila neuropeptide F (NPF) is secreted into adult circulation by EEC and find that specific EEC differentially respond to dietary sugar and yeast. Female lifespan is increased when gut NPF is genetically depleted, and this manipulation is sufficient to blunt the longevity benefit conferred by DR. Depletion of NPF receptors at insulin-producing neurons of the brain also increases female lifespan, consistent with observations where loss of gut NPF decreases neuronal insulin secretion. The longevity conferred by repressing gut NPF and brain NPF receptors is reversed by treating adults with a juvenile hormone (JH) analog. JH is produced by the adult corpora allata, and inhibition of the insulin receptor at this tissue decreases JH titer and extends lifespan in both males and females, while this longevity is restored to wild type by treating adults with a JH analog. Overall, EEC of the gut modulate Drosophila aging through interorgan communication mediated by a gut-brain-corpora allata axis, and insulin produced in the brain impacts lifespan through its control of JH titer. These data suggest that we consider how human incretins and their analogs, which are used to treat obesity and diabetes, may impact aging.

Application of multi-criteria decision making (MCDM) model for solar plant location selection

This study aims to systematically evaluate and identify the most suitable locations for establishing solar farms in the United Kingdom. It employs a comprehensive Multi-Criteria Decision-Making (MCDM) model that incorporates the Fuzzy Analytic Hierarchy Process (FAHP) method. Eight potential locations have been identified and selected for their climatic conditions and other pertinent parameters. These potential locations underwent additional assessment considering several factors including temperature, annual hours of sunshine, absolute humidity, proximity to major roads, distance from the power network, and potential demand. The data was normalized to eliminate scale effects and improve comparability. A MATLAB program has been developed with universality and user-friendliness in mind. Its purpose is to establish location selection factors graphs according to application-specific criteria and generate the matrix representation of these graphs for each potential candidate location. The permanent matrix, a composite metric synthesizing six criteria, identifies Torquay as the optimal location with a permanent value of 18.16. This quantitative approach, intersected with qualitative assessments, presents a clear hierarchy of site suitability. The model's simplicity, scalability, and proven accuracy make it a valuable tool for assessing renewable energy project viability in diverse regions.

Population-based algorithm for discrete facility location with ranking of candidate locations

Facility location problems are mathematical optimization problems that involve finding the best locations for facilities (e.g., factories, warehouses, stores) to serve customers within a given geographic area. The goal is typically to minimize costs, maximize efficiency, or optimize other objectives. Facility location problems can vary in several ways, including customer behavior rules, the type of search space, and constraints on locations for new facilities being located. These variations directly impact the complexity of the problem and the appropriate solution methods that can be used to tackle the problem. This research is focused on the discrete competitive facility location problem for an entering firm, which is a crucial scenario for new firms entering the existing market. The goal is to strategically locate new facilities to maximize their profit, while considering existing competitors. A new random search heuristic algorithm to approximate the optimal solution for discrete competitive facility location problems for firm expansion has been developed. The algorithm extends its precursor that ranks potential locations for the new facilities depending on their usefulness and uselessness in creating new solutions in the past. The new algorithm uses a population to handle and reuse the best solutions found so far and new strategies for ranking potential locations, considering features of the solutions in the population. The designed algorithm has been investigated by solving competitive facility location problems actual for an entering firms using real geographical data.

Considering traffic characteristics: Roadside unit deployment optimization algorithm based on dynamic division of road network subareas

AbstractGiven that the overall coverage deployment method fails to meet information needs in important areas, there are redundancies and deficiencies in the information provided. To enhance communication stability for roadside units (RSUs), improve information coverage at critical intersections and optimize algorithm efficiency. Here, a method for deploying RSUs is proposed that aims to optimize revenue in road network subareas. The road network is divided into several subareas based on critical intersections, node similarity, road segment correlations, and characteristics of RSU information transmission. Then, a roadway accessibility algorithm is developed that accounts for channel fading. Considering the robustness of wire network deployment, an improved traveling salesman problem (TSP) problem is proposed that includes candidate locations and constructs a model for optimal RSU deployment that maximizes consolidated revenue. Finally, using the Sioux Falls network as an example, the RSU deployment strategy is evaluated for the overall network and the road network after being subdivided. The results indicate that subdividing the road network improves the efficiency of the optimization solution, the information coverage of critical intersections increases by 1.8 times. The deployment optimization scheme of RSUs is directly influenced by various parameters such as bandwidth capacity and cost coefficient. When deploying RSUs in road network subareas, variations in total demand have minimal impact on RSU deployment, ensuring a stable deployment scheme.

DNA sequences alignment method using sparse index on pan-genome graph.

The graph of sequences represents the genetic variations of pan-genome concisely and space-efficiently than multiple linear reference genome. In order to accelerate aligning reads to the graph, an index of graph-based reference genomes is used to obtain candidate locations. However, the potential combinatorial explosion of nodes on the sequence graph leads to increasing the index space and maximum memory usage of alignment process considerably, especially for large-scale datasets. For this, existing methods typically attempt to prune complex regions, or extend the length of seeds, which sacrifices the recall of alignment algorithm despite reducing space usage slightly. We present the Sparse-index of Graph (SIG) and alignment algorithm SIG-Aligner, capable of indexing and aligning at the lower memory cost. SIG builds the non-overlapping minimizers index inside nodes of sequence graph and SIG-Aligner filters out most of the false positive matches by the method based on the pigeonhole principle. Compared to Giraffe, the results of computational experiments show that SIG achieves a significant reduction in index memory space ranging from 50% to 75% for the human pan-genome graphs, while still preserving superior or comparable accuracy of alignment and the faster alignment time.

A mathematical programming approach to export pathway planning of distributed hydrogen production in offshore wind farm

Hydrogen production is a vital development trend to improve the economics and penetration rate of offshore wind farms (OWFs), and distributed hydrogen production (DHP) is a preferred solution for deep and far sea OWFs without expensive submarine cables and offshore substations. The export pathway of OWF-DHP consists of collection pipelines, transmission pipelines, and offshore compressor stations. In this paper, mathematical programming is introduced to explore the export pathway planning problem of OWF-DHP. Firstly, the area of OWF is discretized into multiple grids with the center of the grids being the candidate locations of the offshore compressor station. Then, a binary integer quadratic programming problem is established to optimize both the offshore compressor station location and pipeline construction with different topologies. Further, the mathematical model is solved by the branch and cut algorithm integrated with the proposed dimension reduction methods. Finally, the effectiveness of the proposed export pathway planning approach is verified by the actual data of Baltic Eagle OWF in Germany, which would support the construction of the OWF-DHP project.

A Model-Free Approach for Solving Choice-Based Competitive Facility Location Problems Using Simulation and Submodularity

This paper considers facility location problems in which a firm entering a market seeks to open facilities on a subset of candidate locations so as to maximize its expected market share, assuming that customers choose the available alternative that maximizes a random utility function. We introduce a deterministic equivalent reformulation of this stochastic problem as a maximum covering location problem with an exponential number of demand points, each of which is covered by a different set of candidate locations. Estimating the prevalence of these preference profiles through simulation generalizes a sample average approximation method from the literature and results in a maximum covering location problem of manageable size. To solve it, we develop a partial Benders reformulation in which the contribution to the objective of the least influential preference profiles is aggregated and bounded by submodular cuts. This set of profiles is selected by a knee detection method that seeks to identify the best tradeoff between the fraction of the demand that is retained in the master problem and the size of the model. We develop a theoretical analysis of our approach and show that the solution quality it provides for the original stochastic problem, its computational performance, and the automatic profile-retention strategy it exploits are directly connected to the entropy of the preference profiles in the population. Computational experiments on existing and new benchmark sets indicate that our approach dominates the classical sample average approximation method on large instances of the competitive facility location problem, can outperform the best heuristic method from the literature under the multinomial logit model, and achieves state-of-the-art results under the mixed multinomial logit model. We characterize a broader class of problems, which includes assortment optimization, to which the solving methodology and the analyses developed in this paper can be extended. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This research was supported by Fonds de Recherche du Québec-Nature et Technologies and Institut de Valorisation des Données through scholarships to R. Legault. E. Frejinger was partially supported by the Canada Research Chair program [Grant 950-232244]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/ijoc.2023.0280 .

Damage Identification at Off-Grid Locations on Plate-like Structures using Frequency-Coupled Group Lasso

Localizing elastic wave scatterers is the working principle behind a family of model-based approaches to detect and localize damage in plate-like structures. These approaches take advantage of a physical model of propagation and scattering, which enables an efficient data-driven strategy to decompose the effect of damage on the structure. This strategy consists in measuring on the healthy structure the responses to excitations at predefined candidate damage locations, and then in building from these a dictionary that maps damage at candidate locations to changes in responses. At inspection time, inverse methods are used to identify damage, based on this dictionary and the responses measured on the structure under test. One such inverse method is Frequency-Coupled Group Lasso, which exploits the inherent sparsity of damage to reduce the number of required measurements. This paper examines the performance of the above-described approach in the “off-grid” case, that is, when the damage is not located at one of the predefined candidate locations. This is an important extension because real-world damage can occur at a large or infinite number of locations, but the number of candidate locations is limited by measurement and/or computing cost. Off-grid cases are simulated by removing from the dictionary the entries corresponding to the actual damage locations and then running Frequency-Coupled Group Lasso with this incomplete dictionary. The results are then compared to the on-grid case, when the actual damage locations are included in the dictionary. Damage identification is demonstrated on a 600mm by 600mm composite plate with up to 6 damage locations, using a single accelerometer and 7 hammer excitations. Off-grid performance is evaluated for 4 different accelerometer locations, for SNRs ranging from 30 to 10dB, and compared to on-grid performance. The proposed damage identification approach is shown to remain robust in the off-grid case, further proving its real-world applicability.

Gut-to-brain regulation of Drosophila aging through neuropeptide F, insulin and juvenile hormone.

Dietary restriction slows aging in many animals, while in some cases the sensory signals from diet alone are sufficient to retard or accelerate lifespan. The digestive tract is a candidate location to sense nutrients, where neuropeptides secreted by enteroendocrine cells (EEC) produce systemic signals in response to food. Here we measure how Drosophila neuropeptide F (NPF) is secreted into adult circulation by enteroendocrine cells and find that specific enteroendocrine cells differentially respond to dietary sugar and yeast. Lifespan is increased when gut NPF is genetically depleted, and this manipulation is sufficient to blunt the longevity benefit conferred by dietary restriction. Depletion of NPF receptors at insulin producing neurons of the brain also increases lifespan, consistent with observations where loss of gut NPF decreases neuronal insulin secretion. The longevity conferred by repressing gut NPF and brain NPF receptors is reversed by treating adults with a juvenile hormone (JH) analog. JH is produced by the adult corpora allata, and inhibition of the insulin receptor at this tissue decreases JH titer and extends lifespan, while this longevity is restored to wild type by treating adults with a JH analog. Overall, enteroendocrine cells of the gut modulate Drosophila aging through interorgan communication mediated by a gut-brain-corpora allata axis, and insulin produced in the brain impacts lifespan through its control of JH titer. These data suggest that we should consider how human incretins and their analogs, which are used to treat obesity and diabetes, may impact aging.

AMICO-COSMOS galaxy cluster and group catalogue up to z = 2: Sample properties and X-ray counterparts

Aims. We present the results of a new galaxy cluster search in the COSMOS field obtained using the Adaptive Matched Identifier of Clustered Objects (AMICO). Our aim was to produce a new cluster and group catalogue up to z = 2 by performing an innovative application of AMICO with respect to previous successful applications to wide-field surveys in terms of depth (down to r < 26.7), small area covered (∼1.69 deg2 of unmasked effective area), and redshift extent. This sample and the comparative analysis we performed with the X-rays, allowed for the calibration of mass-proxy scaling relations up to z = 2 and down to less than 1013 M⊙, and constitutes the basis for the refinement of the cluster model for future applications of AMICO, such as the analysis of upcoming Euclid data. Methods. The AMICO algorithm is based on an optimal linear matched filter and detects clusters in photometric galaxy catalogues using galaxy location, photometric redshift and, in the simplest case, one galaxy property. We chose to use a single magnitude as the galaxy property, avoiding the explicit use of galaxy colour for the selection of clusters. We used three different magnitudes by performing three independent runs in the r-, Y-, and H-bands using both COSMOS2020 and COSMOS2015 galaxy catalogues. We created a composite visibility mask and cluster models for the signal to detect, and we estimated the noise directly from the data. Results. We performed a matching of the catalogues resulting from the three runs and merged them to produce a final catalogue that contains 1269 and 666 candidate clusters and groups with S/N > 3.0 and > 3.5, respectively. A total of 490 candidates are detected in all three runs. Most of the detections unmatched between runs have S/N < 3.5, which can be set as a threshold for selecting a more robust sample. We assigned X-ray properties to our detections by matching the catalogue with a public X-ray selected group sample and by estimating, for unmatched detections, the X-ray properties at the location of AMICO candidates using Chandra+XMM-Newton data. There are in total 622 candidate clusters and groups with an X-ray flux estimate. This large sample of candidates with X-ray properties allowed the calibration of the scaling relations between two AMICO mass-proxies (richness and cluster amplitude) and X-ray mass and the study of their redshift dependence for the selection of the most stable photometric bands.

A stepwise fast leakage localization method applying the strategy of dynamic area narrowing down for large-scale water distribution network

ABSTRACT Urban water distribution networks (WDNs) are facing serious leakage problems. As cities expand, the leakage localization burden on large WDNs gradually increases. Although methods have been widely researched, there is a lack of studies and successful applications for large-scale WDNs. To deal with this, a stepwise fast leakage localization Method, SFLLM, utilizing the `dynamic area narrowing down (DAND)' strategy and coupled leakage features (CLF) is proposed. The SFLLM includes a fast-and-dynamic stage using the DAND strategy to reduce the potential leakage area and an accurate localization stage. Only partial representative candidate locations are required to simulate leakages by DAND, and meantime CLF is used to analyze the leakage similarities so that the localization accuracy and efficiency can be improved. SFLLM was tested on a benchmark WDN, saving more than 88% of simulation by DAND strategy and achieving localization in 11 seconds. The results also proved the enhanced performance of CLF in ensuring the stability of the accuracy against various types of uncertainties that may occur in real WDNs. Moreover, three real burst leaks in an actual large-scale WDN were localized within 205 m in about 22 minutes by SFLLM, showing the method's reliable applicability in guiding field leak exploration.

Offshoring Location Decision in Fuzzy Environment

Offshoring location selection is a crucial decision for firms in terms of competitiveness, flexibility, productivity, and profitability. Determining an efficient and appropriate location for offshoring has been a substantial multicriteria decision-making (MCDM) problem. Considering that the outcome of an MCDM method alone can be misleading, a novel hybrid approach is presented in this study. Thus, five MCDM methods are utilized to solve the problem, and the results of four MCDM methods are integrated to assure an optimal offshoring location. A Fuzzy-AHP (analytical hierarchy process) integrated with the technique for order preference by similarity to ideal solution (TOPSIS), additive ratio assessment (ARAS), elimination et choix traduisant la realité (ELECTRE), and weighted sum method (WSM) methodology is proposed for the appraisal and selection of the optimal offshoring location. In this context, fifteen alternative locations are determined based on the attractiveness of the locations in terms of offshoring. Fuzzy-AHP is implemented to analyze the problem's structure and find the weights of the quantitative and qualitative criteria. Consistency tests are implemented to assess the quality of inputs of an expert. Then, TOPSIS, WSM, ARAS, and ELECTRE are used to evaluate and rank the candidate locations and present a comparative analysis. By considering fifteen countries and using real data, offshoring location selection is conducted through the proposed methodology. Moreover, sensitivity analysis is made to diminish the subjectivity and assess the robustness of the techniques. The results demonstrated that giving more weights to the labor characteristics and proximity to market criteria might improve the quality of the best offshoring country index.

Analysis of potential lobster artificial reef sites in Gerupuk Bay waters of Central Lombok, West Nusa Tenggara

Abstract Lobster is one of the fisheries products which has become the most searched in international trade because of its high price. Therefore, the highest number of lobsters is from the natural fishing effort. The increasing market has an impact on decreasing habitat. Uncontrollable degradation impact requires an alternative to save the stability of lobster production in nature. This study aims to analyse the suitability of candidate locations for the placement of Lobster Artificial Reefs. The selection site uses the Stationary Visual Census method, while oceanography parameters use Water Quality Checker. Data collection was done in August and October 2021. The analyse suitability site was based on the bio-ecological parameters from some research stations. The research recommended all stations from 2014 to 2016 and FGD in July 2021. Recommendation site was found around Tiung Stone station. It is expected to be a protected area from predators, and it can be a feeding area to provide natural food during lobsters’ life cycle in nature. These results still need to ensure the effectiveness of the artificial habitat function for lobster breeding. Further analysis is required to see the effectiveness value of the location and lobster artificial reef before being implemented in other locations.

An optimization-based design methodology to manage the sustainable biomass-to-biodiesel supply chain under disruptions: A case study

The detrimental effects of excessive fossil fuel consumption have recently become a major global concern. Moreover, fuel supply chains are vulnerable to disruptive and operational risks. This study proposes a robust-stochastic approach to design a resilient sustainable biodiesel supply chain from Jatropha and waste cooking oil, while facilities and transportation links are vulnerable to multiple site-dependent disruptions. The main contributions are implementing preventive resiliency strategies to manage risks, integrating sustainability and resiliency, and selecting biodiesel conversion technology based on byproducts’ demands. Herein, candidate locations are identified to cultivate Jatropha using the best-worst method and geographical information system. Moreover, the supply chain network is designed to optimize economic performance, network non-resiliency, and social impacts. The Monte Carlo simulation approach, k-means clustering, augmented epsilon constraint, and Mulvey robust optimization are adopted to generate and reduce probable scenarios, manage multiple objectives, and handle uncertainty, respectively. The results showed that the non-resilient supply chain experiences a 20.11 % additional cost in disruption scenarios; however, even with a higher initial cost, adopting resiliency strategies reduces disruption cost effects by 9.46 %. Managerial insights have demonstrated that resilience and sustainability are positively correlated in biofuel supply chain management, and rising biodiesel demand enhances social welfare and economic performance.

Identifying a good business location using prescriptive analytics: Restaurant location recommendation based on spatial data mining

This study proposes a new prescriptive analytics method that aims to provide decision-makers with a systematic and objective approach to identify suitable locations, considering the spatial distribution of different types of restaurants. The method comprises of two main components: spatial co-location pattern mining and locationGCN, where locationGCN is based on graph convolutional network (GCN). The spatial co-location pattern mining is utilized to capture the spatial correlation of specific restaurant to determine the candidate location selection range. The locationGCN is designed to further screen out final suitable location ranges for the specific restaurant type. A case study using restaurant data from Xiamen Island collected from Dianping.com is conducted. The empirical results demonstrate that the algorithm achieves an accuracy of 74.88%, precision of 63.59%, and recall of 77.48%. Results indicate that the proposed approach can provide suitable location recommendations for specific types of restaurants based on existing restaurant distribution information.

A Methodology for Designing One-Way Station-Based Carsharing Services in a GIS Environment: A Case Study in Palermo

One-way carsharing is recognized as one of the most popular transportation services in urban areas, being an alternative option to private cars. Over the last decades, a vast amount of literature on the design of specific aspects of this service (fleet size, stations’ locations, fare, balancing operations) has formed. However, a holistic approach for designing carsharing services seems not to be developed. This paper proposes a new approach for designing one-way station-based carsharing services, presenting a five-step method, entirely developed in a GIS environment. The first three steps (suitability analysis, site selection analysis, and walkability analysis) allow finding the candidate locations for carsharing stations. After the assessment of the capacity of the potential stations, a location-allocation analysis allows for assessing the fleet size, the number of stations that maximize the coverage of carsharing demand, and their optimal locations. This paper presents a case study: a new one-way carsharing service was designed in Palermo (Italy) and compared to the existing carsharing service operating in the city. The results highlight that the current carsharing supply is undersized, having about 45% fewer stations and about half the cars compared to those resulting from the model, leaving some POIs unserved.

Evolution inspired binary flower pollination for the uncapacitated facility location problem

The present paper introduces a modified flower pollination algorithm (FPA) enhanced by evolutionary operators to solve the uncapacitated facility location problem (UFLP), which is one of the well-known location science problems. The aim in UFLP is to select some locations to open facilities among a certain number of candidate locations so as to minimize the total cost, which is the sum of facility opening costs and transportation costs. Since UFLP is a binary optimization problem, FPA, which is introduced to solve real-valued optimization problems, is redesigned to be able to conduct search in binary domains. This constitutes one of the contributions of the present study. In this context, some evolutionary operators such as crossover and mutation are adopted by the proposed FPA. Next, the mutation operator is further enhanced by making use of an adaptive procedure that introduces greater level of diversity at earlier iterations and encourages intensification toward the end of search. Thus, while premature convergence and local optima problems at earlier iterations are avoided, a more intensified search around the found promising regions is performed. Secondarily, as demonstrated in this study, by making use of the reported evolutionary procedures, FPA is able to run in binary spaces without employing any additional auxiliary procedures such as transfer functions. All available benchmarking instances are solved by the proposed approach. As demonstrated by the comprehensive experimental study that includes statistically verified results, the developed approach is found as a promising algorithm that can be extended to numerous binary optimization problems.

Searching for X-ray counterparts of unassociated Fermi-LAT sources and rotation-powered pulsars with SRG/eROSITA

Context. The latest source catalog of the Fermi-LAT telescope contains more than 7000 γ-ray sources at giga-electronvolt energies, with the two dominant source classes thought to be blazars and rotation-powered pulsars. Despite continuous follow-up efforts, around 2600 sources have no known multiwavelength association. Aims. Our target is the identification of possible (young and recycled) pulsar candidates in the sample of unassociated γ-ray sources via their characteristic X-ray and γ-ray emission. To achieve this, we cross-matched the Fermi-LAT catalog with the catalog of X-ray sources in the western Galactic hemisphere from the first four all-sky surveys of eROSITA on the Spektrum-Roentgen-Gamma (SRG) mission. We complement this by identifying X-ray counterparts of known pulsars detected at γ-ray and radio energies in the eROSITA data. Methods. We used a Bayesian cross-matching scheme to construct a probabilistic catalog of possible pulsar-type X-ray counterparts to Fermi-LAT sources. Our method combines the overlap of X-ray and γ-ray source positions with a probabilistic classification (into pulsar and blazar candidates) of each source based on its γ-ray properties and a prediction on the X-ray flux of pulsar- or blazar-type counterparts. Finally, an optical and infrared counterpart search was performed to exclude coronally emitting stars and active galactic nuclei from our catalog. Results. We provide a catalog of our prior γ-ray-based classifications of all 2600 unassociated sources in the Fermi-LAT catalog, with around equal numbers of pulsar and blazar candidates. Our final list of candidate X-ray counterparts to suspected new high-energy pulsars, cleaned for spurious detections and sources with obvious non-pulsar counterparts, contains around 900 X-ray sources, the vast majority of which lie in the 95% γ-ray error ellipse. We predict between 30 and 40 new pulsars among our top 200 candidates, with around equal numbers of young and recycled pulsars. This candidate list may serve as input to future follow-up campaigns, looking directly for pulsations or for the orbital modulation of possible binary companions, where it may allow for a drastic reduction in the number of candidate locations to search. We furthermore detect the X-ray counterparts of 15 known rotation-powered pulsars, which were not seen in X-rays before.