Candidate Locations Research Articles

A location-or-routing problem with partial and decaying coverage

This paper studies a generalized location-or-routing problem by considering partial coverage of the users according to a distance-decaying coverage function. In this problem, there is a set of candidate locations where to open facilities, and a set of locations with given number of users that must be covered by the open facilities. Users may travel directly to an open facility if they are within the coverage range of it, or they may be transported to facilities by capacitated vehicles. A distance-decaying function for the facility coverage is considered and the vehicles are allowed to partially cover the users located at the same location. Two mixed integer programming models are presented that minimize the number of uncovered users subject to a restricted budget, and an adaptive large neighborhood search metaheuristic is developed as the solution methodology. Through several computational experiments, the efficiency of the proposed formulations and the solution algorithm are evaluated, and the ALNS algorithm is shown to perform well in terms of solution quality and computing time. Computational results indicate that considering the partial coverage of users reduces the number of uncovered ones as the vehicle capacity decreases, and this reduction is more significant under a distance-decaying facility coverage function. It is also observed that considering distance-decaying coverage increases both the number of uncovered users and the spent budget, especially with a continuous function such as an exponential decay function.

Optimal Tide Gauge Location for Tsunami Validation in The Lembeh Island, North Sulawesi

DOI:10.17014/ijog.9.3.315-327The tsunami early warning system in Bitung does not work optimally, because there is no buoy as a marine equipment for tsunami validation before reaching the coastal area. The lack of buoy can be replaced by placing a tide gauge on the east coast of Lembeh Island. To determine the optimal tide gauge location, the simple additive weighting (SAW) method was used with three criteria. Those three criteria are the potential of tsunami detection, sufficient evacuation time, and an appropriate site for tide gauge installation. Numerical tsunami modeling is used to calculate the first two criteria. The third criterion is a limiting factor, because the tide gauge can only be installed on the dock. Therefore, there were only five candidate locations on the east coast of Lembeh, namely Dorbolang, Pancuran, Posokan, Motto, and Lirang. The result, Posokan is the best location for tide gauge placement with a total score of 2.884. Based on the simulation, an additional tide gauge in Posokan can detect tsunami at the average of 11.4 minutes earlier than use only the tide gauge currently available at Bitung port. It means that people on the coast of Bitung have more evacuation time before the tsunami hits the coastal area

GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images.

Recognition of protein subcellular distribution patterns and identification of location biomarker proteins in cancer tissues are important for understanding protein functions and related diseases. Immunohistochemical (IHC) images enable visualizing the distribution of proteins at the tissue level, providing an important resource for the protein localization studies. In the past decades, several image-based protein subcellular location prediction methods have been developed, but the prediction accuracies still have much space to improve due to the complexity of protein patterns resulting from multi-label proteins and the variation of location patterns across cell types or states. Here, we propose a multi-label multi-instance model based on deep graph convolutional neural networks, GraphLoc, to recognize protein subcellular location patterns. GraphLoc builds a graph of multiple IHC images for one protein, learns protein-level representations by graph convolutions and predicts multi-label information by a dynamic threshold method. Our results show that GraphLoc is a promising model for image-based protein subcellular location prediction with model interpretability. Furthermore, we apply GraphLoc to the identification of candidate location biomarkers and potential members for protein networks. A large portion of the predicted results have supporting evidence from the existing literatures and the new candidates also provide guidance for further experimental screening. The dataset and code are available at: www.csbio.sjtu.edu.cn/bioinf/GraphLoc. Supplementary data are available at Bioinformatics online.

An Enhanced Information Sharing Roadside Unit Allocation Scheme for Vehicular Networks

Sharing up-to-date environment information collected by intelligent connected vehicles is critical in achieving travel comfort, convenience, and safety in vehicular networks. Individually collected information should be made available to other vehicular nodes, adjacent or distant, to achieve an informed and well-managed vehicular traffic. The coverage reach of sharing these road data can be maximized by allocating roadside units in strategic positions. In this work, we propose an Enhanced Information SHAring via Roadside Unit Allocation (EISHA–RSU) scheme that strategically determines where RSUs must be deployed from all spatial candidate locations. The urban area is irregularly partitioned into effective regions of movement (ERM) according to vehicular capacity with priority. For each ERM, EISHA–RSU greedily allocates the initial RSU to an effective position and optimally assigns the remaining RSUs to spatial locations that capture the maximum I2V/V2I information sharing based on the area’s average road speed. In effect, the proposed deployment scheme addresses both the issues of coverage and connectivity among vehicles and the infrastructure. We evaluate the proposed RSU allocation scheme by employing three urban empirical mobility datasets and compare its network starvation fairness, effectiveness, and efficiency performance measures with three other deployment benchmarks. Overall, EISHA–RSU reduces the number of required RSUs to cover a certain area, exhibits higher connectivity, and achieves maximum I2V/V2I information sharing among the evaluated schemes.

Quality of Service Measure for Bike Sharing Systems

Bike sharing systems (BSSs) are becoming an important part of urban mobility in many cities given that they are sustainable and environmentally friendly. BSS operators spend great efforts to ensure bike and dock availability at each station. Measuring the quality of service (QoS) of each station and/or the entire system is critical for efficient system operations. The traditionally-known QoS measure reported in the literature is based on the proportion of problematic stations, which are defined as those with no bikes or docks available to users. This measure neither exposes the spatial dependencies between stations nor does it discriminate between stations in the BSS. Hence, we propose a novel QoS measure, namely the Optimal Occupancy, in which: 1) the temporal variations in arrival and pick up rates at individual stations are considered; 2) the discriminative property of the Optimal Occupancy is demonstrated using Analysis of Variance (ANOVA) procedures; and 3) geo-statistics, which have not been used before, are applied to explore the spatial Optimal Occupancy variations and model variograms for spatial prediction. This study uses an anonymized bike trip dataset from 34 stations in downtown San Francisco to compare the traditionally-known QoS measure and the proposed Optimal Occupancy measure. Results reveal that the Optimal Occupancy is beneficial, outperforms the traditionally-known QoS measure, and produces a better prediction of the QoS at nearby locations. In addition, the Optimal Occupancy can be used to predict candidate locations for the introduction of new stations in an existing BSS.

Providing a simple and efficient algorithm for passive sound localization

The sound source localization (SSL) system is one of the main approaches for detecting targets. This kind of localization method is carried out in a passive way, without the emission of any signals, and cannot be detected easily. This paper investigates the two-dimensional SSL of stationary targets using only three receivers. Given that, flow-induced noise of the bluff-bodies in viscous fluids is one of the most important noise generation sources, firstly, this noise is simulated numerically. Due to the distance between the receivers in an array, the sound reaches the receiver with time delay. High accuracy SSL needs highly accurate time delays measurement. In the following, time delays are obtained by calculating the cross-correlation in a time domain of sound signals arriving at the receiver array, and next, the bearing angle of the sound source is estimated. Later, using a new, simple (easy installation), fast (real-time), and accurate pure geometric algorithm, the source candidate locations are obtained. Then, using an elimination algorithm, the ambiguity of the noise source location is removed, and source imaginary locations are removed. Finally, this process is validated by a physical algorithm. Numerical results verify the effectiveness of this geometric algorithm.

Territorial Resilience Through Visibility Analysis for Immediate Detection of Wildfires Integrating Fire Susceptibility, Geographical Features, and Optimization Methods

Climate change effects tend to reinforce the frequency and severity of wildfires worldwide, and early detection of wildfire events is considered of crucial importance. The primary aim of this study was the spatial optimization of fire resources (that is, watchtowers) considering the interplay of geographical features (that is, simulated burn probability to delimit fire vulnerability; topography effects; and accessibility to candidate watchtower locations) and geo-optimization techniques (exact programming methods) to find both an effective and financially feasible solution in terms of visibility coverage in Chalkidiki Prefecture of northern Greece. The integration of all geographical features through the Analytical Hierarchy Process indicated the most appropriate territory for the installment of watchtowers. Terrain analysis guaranteed the independence and proximity of location options (applying spatial systematic sampling to avoid first order redundancy) across the ridges. The conjunction of the above processes yielded 654 candidate watchtower positions in 151,890 ha of forests. The algorithm was designed to maximize the joint visible area and simultaneously minimize the number of candidate locations and overlapping effects (avoiding second order redundancy). The results indicate four differentiated location options in the study area: (1) 75 locations can cover 90% of the forests (maximum visible area); (2) 47 locations can cover 85% of the forests; (3) 31 locations can cover 80.2% of the forests; and (4) 16 locations can cover 70.6% of the forests. The last option is an efficient solution because it covers about 71% of the forests with just half the number of watchtowers that would be required for the third option with only about 10% additional forest coverage. However, the final choice of any location scheme is subject to agency priorities and their respective financial flexibility.

Optimizing Police Facility Locations Based on Cluster Analysis and the Maximal Covering Location Problem

Planning and defining places for the installation of police facilities are fundamental to improving the public security service in the urban space. Geographic Information Systems connecting the spatial distribution of police occurrences, budgetary restrictions, and the maximum distance covered are state-of-the-art innovations addressing the need for preventive and responsive police management. The present work proposes a management information system to support the analysis and suggestion of potential police facility locations. The management information system is modeled using k-means for cluster analysis and the definition of candidate locations, and the maximal covering location problem is used to optimize the predefined locations. The proposed system allows the analysis of alternative locations and their impacts on public security. The application in Brazil demonstrates that it is possible to obtain an additional 22% gain in the coverage area of occurrences and an additional reduction of 920 m in terms of the average distance covered when comparing the management information system’s suggested locations to the current configuration. Thus, our assessment provides an efficient tool for supporting decisions regarding the location of police facilities and helps improve the public security service.

Efficient kNN query for moving objects on time-dependent road networks

In this paper, we study the Time-Dependent k Nearest Neighbor (TD-kNN) query on moving objects that aims to return k objects arriving at the query location with the least traveling cost departing at a given time t. Although the kNN query on moving objects has been widely studied in the scenario of the static road network, the TD-kNN query tends to be more complicated and challenging because under the time-dependent road network, the cost of each edge is measured by a cost function rather than a fixed distance value. To tackle such difficulty, we adopt the framework of GLAD and develop an advanced index structure to support efficient fastest travel cost query on time-dependent road network. In particular, we propose the Time-Dependent H2H (TD-H2H) index, which pre-computes the aggregated weight functions between each node to some specific nodes in the decomposition tree derived from the road network. Additionally, we establish a grid index on moving objects for candidate object retrieval and location update. To further accelerate the TD-kNN query, two pruning strategies are proposed in our solution. Apart from that, we extend our framework to tackle the time-dependent approachable kNN (TD-AkNN) query on moving objects targeting for the application of taxi-hailing service, where the moving object might have been occupied. Extensive experiments with different parameter settings on real-world road network show that our solutions for both TD-kNN and TD-AkNN queries are superior to the competitors in orders of magnitude.

Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest recursive feature elimination algorithm.

Hazardous healthcare waste (HCW) management system is one of the most critical urban systems affected by the COVID-19 pandemic due to the increase in waste generation rate in hospitals and medical centers dealing with infected patients as well as the degree of hazardousness of generated waste due to exposure to the virus. In this regard, waste network flow would face severe problems without taking care of hazardous waste through disinfection facilities. For this purpose, this study aims to develop an advanced decision support system based on a multi-stage model that was combined with the random forest recursive feature elimination (RF-RFE) algorithm, the indifference threshold-based attribute ratio analysis (ITARA), and measurement of alternatives and ranking according to compromise solution (MARCOS) methods into a unique framework under the Fermatean fuzzy environment. In the first stage, the innovative Fermatean fuzzy RF-RFE algorithm extracts core criteria from a finite set of initial criteria. In the second stage, the novel Fermatean fuzzy ITARA determines the semi-objective importance of the core criteria. In the third stage, the new Fermatean fuzzy MARCOS method ranks alternatives. A real-life case study in Istanbul, Turkey, illustrates the applicability of the introduced methodology. Our empirical findings indicate that “Pendik” is the best among five candidate locations for sitting a new disinfection facility for hazardous HCW in Istanbul. The sensitivity and comparative analyses confirmed that our approach is highly robust and reliable. This approach could be used to tackle other critical multi-dimensional problems related to COVID-19 and support sustainability and circular economy.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10479-022-04822-0.

Life cycle evaluation of microalgae based biorefinery supply network: A sustainable multi-criteria approach

Due to enjoying such features as ability to produce high volumes of oil and reducing and eliminating environment carbon dioxide, microalgae is a promising solution for producing biofuels. In order to help the commercialization of this emerging industry, the present study offers a sustainable mixed integer linear programming model (MILP) for designing and planning microalgae biofuel production and distribution network. As objective functions, this model considers minimizing the total cost of the network and maximizing greenhouse gas emission saving simultaneously. In order to design microalgae biofuel sustainable production and distribution network, a two-stage approach is developed. In order to reduce computational complexity, at first place, data envelopment analysis approach (DEA) and Combinative Distance-Based Assessment (CODAS) approach as well as best worst method (BWM) are developed respectively for determining the location of suitable candidates for establishing microalgae production facilities and constructing biorefinery based on economic, social and environmental criteria. In the second stage, the environmental effects of microalgae biofuel supply chain network are measured using one of the newest and most effective methods that is based upon environmental life cycle assessment as well as OpenLCA software and then followed by formulizing a bi-objective linear programming model. For solving the offered model, augmented epsilon constraint method is used. Finally, a realistic case study conducted at national level concerning microalgae biofuel supply chain. The results indicate that based on the offered model by replacing fossil fuels with biofuel we can achieve greenhouse gases emission saving by 99 %.

On the distortion of single winner elections with aligned candidates

We study the problem of selecting a single element from a set of candidates on which a group of agents has some spatial preferences. The exact distances between agent and candidate locations are unknown but we know how agents rank the candidates from the closest to the farthest. Whether it is desirable or undesirable, the winning candidate should either minimize or maximize its aggregate distance to the agents. The goal is to understand the optimal distortion, which evaluates how good an algorithm that determines the winner based only on the agent rankings performs against the optimal solution. We give a characterization of the distortion in the case of latent Euclidean distances such that the candidates are aligned, but the agent locations are not constrained. This setting generalizes the well-studied setting where both agents and candidates are located on the real line. Our bounds on the distortion are expressed with a parameter which relates, for every agent, the distance to her best candidate to the distance to any other alternative.

A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States

AbstractMidwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.

An Improved Adaptive Large Neighborhood Search Algorithm for the Heterogeneous Customized Bus Service with Multiple Pickup and Delivery Candidate Locations

In order to tackle the congestion and environmental issues, customized bus services are proposed and deployed in metropolitan areas. As emerging public transportation services, customized bus services bring passengers more convenience and accessibility. Besides, conventional customized bus services generally organize homogeneous fleet and single location selection to passengers. In this paper, to enhance the mobility and flexibility of customized buses and increase companies’ profit, we propose a new form of customized bus service with heterogeneous fleets and multiple candidate locations. First, a mixed-integer programming model (MIP) is developed to describe the customized bus problem. Compared with the conventional model, the proposed MIP is involved in the case of one passenger with multiple candidate pickup or delivery locations and can be solved by GUROBI on small scale, quickly and efficiently. Second, an improved adaptive large neighborhood search algorithm ( ALNS i p ) is utilized to address the large-scale problem more efficiently. Time slack calculation method is then designed to optimize vehicle timetables, which provides stable and excellent performance for searching feasible solutions. In addition, we propose two inserting operators to deal with the problem with multiple candidate locations and analyse its influence on the results. Finally, we test the performance of the proposed model and algorithm on the numerical experiments. And they are verified the effectiveness and implication in a small-scale case on a simplified Sioux waterfall network and a large-scale problem in Beijing, China. The result shows that ALNS i p outperforms other algorithms in searching for more satisfying solutions with higher efficiency. However, the GUROBI solver can obtain the solution to small-scale problems within a shorter time than ALNS i p . Furthermore, it can be suggested that the heterogeneous fleets service with multiple candidate locations is helpful to facilitate collaboration among vehicles and optimize pickup and delivery routes in consequence.

Location of primary health care centers for demand coverage of complementary services

We address the problem of locating primary health care centers integrating the outpatient medical service as a capacitated service and a set of uncapacitated complementary services such as nutrition counseling, dental care, mental health, clinical analysis, and imaging service. The objective is to locate new facilities or upgrade existing ones while the demand coverage of the complementary services is maximized and the total travel distance for the outpatient service allocation is constrained. The cost of opening new facilities and upgrading the existing ones is restricted to a budget. The problem is modeled as a variation of the maximal covering location problem with additional side constraints. In addition, two auxiliary bi-objective integer programming models are introduced to help identify the trade-off between the total travel distance and different budgets. A case study based on the public health care system of the northern zone of the State of Mexico, Mexico, is presented, including an assessment of the trade-off between the total travel distance and the allowed budget. Optimal solutions were found using CPLEX for a set of instances composed of 1,086 demand nodes, 294 current facilities, and 117 candidate locations. The auxiliary bi-objective programming models were solved by an augmented ε-constraint method. The empirical work shows the usefulness of the proposed models.

Automated Selection and Localization of Mobile Cranes in Construction Planning

Accurate selection and location of mobile cranes is a critical issue on construction sites, being able to contribute to the improvement of the safety and efficiency of lifting operations. Considering the complexities and dynamics of construction sites, this study aimed to develop a useful approach for automated selection and localization of mobile cranes based on the simulation of crane operations. First, the information required for crane selection and localization is analyzed and extracted from BIM (building information modeling). Then, mainly considering the crane capacity, the initial crane type is selected with candidate location points. Based on the simulation of lifting operation at the candidate points, feasible location points and crane types are determined through three constraint checks (i.e., environment constraint, operation constraint, and safety constraint). Besides, two kinds of efficiency optimization, namely lifting time minimization and crane movement minimization, are presented to figure out the best location points from the feasible points. Finally, the proposed approach is validated using a case study. This research contributes to not only crane operation planning but also automatic construction simulation, thus supporting the implementation of intelligent construction in the future.

An Integrated Factor Analysis-Technique for Order Preference by Similarity to Ideal Solution for Location Decision in ASEAN Region: A Case Study of Thai Fabric Manufacturing Plant

In this paper, we propose an integrated model for selecting a suitable location for fabric manufacturing plants in the ASEAN region. In the first phase, Cambodia, Vietnam and Indonesia were determined as candidate locations for evaluation from the screening process. In this regard, key criteria influencing location decisions were derived using factor analysis of responses extracted from questionnaires. In the second phase, criterion weights were calculated using the rank of centroid (ROC) method. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was then used to prioritize three location alternatives, and sensitivity analysis was also employed to verify the stability of the method. Based on TOPSIS method, Vietnam was the preferred location, followed by Indonesia while Cambodia was not recommended. Sensitivity analysis also showed that the proposed model was valid. The findings from this study provided references for enterprises engaged in international location decision making. The results can help them better understand the decision-making process and identify key criteria that can influence location decisions internationally.

A Comparative Study of Using MCDM Methods Integrated with Entropy Weight Method for Evaluating Facility Location Problem

The location selection of facilities became a major interest for the organizations to establish their planned business for a long period of time. The choice of the best location among a set of candidate locations is a complex process. Although the multiple criteria decision making (MCDM) methods are applicable for location selection problems, different solutions can be obtained using different MCDM methods. Thus, a comparative study between four different MCDM methods was applied within numerical example to show the deviations in ranking of the alternatives that occurs when different methods are used. The weights of attributes are assigned using objective method namely Entropy weight method. The rank disagreements are expressed using spearman`s correlation coefficients.

Selecting the right location for sports facilities using analytical hierarchy process

PurposeThis study aims to adapt analytical hierarchy process (AHP) for choosing the optimal location for sport facilities. The location of a sports facility contributes significantly to its potential success or failure. Therefore, factors affecting such location-related decisions must be carefully studied and prioritized in a systematic fashion.Design/methodology/approachThis study develops a seven-step framework which may be used to decide on a location from among several alternatives. Through an extensive literature review, this study first determines the factors affecting sports facility location selection and then applies AHP steps by asking several sports facility owners and managers to assess importance of the criteria.FindingsThis study determined the sport facility location selection factors as “ease of access,” “facility features,” “financial issues,” “neighborhood” and “market,” and further divided each factor into its subfactors. To illustrate the framework of using AHP as a tool to select the right location for sport facilities, we chose three candidate locations and scored them according to the calculated weight scores of the criteria, identifying the strengths and weaknesses of each location.Practical implicationsThis study provides several managerial implications that may guide sport facility investors in choosing the right location.Social implicationsThis study presents a method to evaluate different factors for different actors of sport industry in a systematic way. Private investors can use the method for securing sufficient number of potential customers in a well-selected location. Government institutions and public policymakers can use the method, possibly with different sets of factors, to decide on the location of public sports facilities to maximize the number of visitors or to reach disadvantaged or underserved populations.Originality/valueThis framework of AHP method can help private and public investors and policymakers evaluate and make the optimal decision for choosing sports facility locations. This study contributes both to sport management theory and practice as well as to operation management literature. This study also refined the scattered factors in the literature of selecting a sport facility site in a more understandable and adaptable way.

Active control of road noise considering the vibro-acoustic transfer path of a passenger car

This paper presents an efficient approach to select the reference sensor position in active control of road noise by considering the vibro-acoustic transfer path of a passenger car. To ensure sufficient noise attenuation in active road noise control, the reference signals should be correlated with vehicle interior noise signals. To achieve this aim, the vibro-acoustic transfer path delivers promising options. An appropriate number of reference signals have generally been obtained by principal component analysis. Then reference sensor positions have been determined by evaluating a large number of candidate locations on a car from a coherence perspective. However, this task is time-consuming and inefficient in the actual vehicle development process. Furthermore, in practice, it is challenging to obtain a highly coherent reference signal for road noise with broadband noise characteristics without understanding the characteristics of the road noise transmitted to the interior of the vehicle. In this study, a structural transfer path analysis was conducted to derive the primary vibro-acoustic paths of structural road noise, and the optimum positions of the reference sensors were determined through the analysis of path contribution and vibro-acoustic transfer function. A practical active noise control system was implemented with the remote microphone technique based on a vehicle audio system to validate the noise attenuation performance. The experiments demonstrated an average attenuation of 3.4 dB at the driver and rear right-hand-side seat positions for up to 400 Hz frequencies. This approach is expected to increase the efficiency in developing active road noise control systems based on adaptive feedforward control systems.