Maximal Covering Location Problem Research Articles

Capacity Expansion Optimization of Determined Charging Stations Based on Accessibility Analysis and Improved Discrete Choice Model

Reasonable capacity expansion optimization of determined charging stations is important for the popularization of electric vehicles (EVs). In this article, Gaussian two-step floating catchment area method and temporal clustering are adopted to study the unbalanced spatial and temporal distribution of charging station accessibility, as well as to determine candidate sites for capacity expansion optimization. Then, a two-level optimization model for capacity expansion of determined charging stations is proposed to reduce the computational complexity caused by the non-linearity and non-convexity of choice probability formulas when the discrete choice model is applied to statistically describe and analyze discrete behaviors such as users’ EV purchase preferences and charging station heterogeneity. In the proposed method, the lower-level model is described as a maximal coverage location problem by expressing the error term of the utility function as a linear combination of the random vectors from IID normal distribution and IID Gumbel distribution, which effectively simplifies the computing process. Finally, the two-level model can be transformed into an integer linear programming problem to optimize the capacity of determined charging stations. Experimental results show that the purchase rate of EVs is significantly improved, and the accessibility of charging stations during rush hours is more balanced than before.

Baggage dissociation for sustainable air travel: Design study of ground baggage distribution networks

Dissociation of passenger travel from baggage delivery has been proposed as one of the radical innovations in future air travel. This concept is still relatively new and largely unexplored, so there are many issues that need to be resolved. For instance, a complete end-to-end baggage dissociation will require the ground distribution networks to deliver passenger luggage to and from the departing and arriving airports. This paper proposes to design such networks as the existing parcel delivery networks. In particular, baggage sorting centers (BSCs) can serve as local hubs for creasing a scalable, multi-level topology of the delivery network in order to manage baggage flows in a given geographical area around the selected airports. Assuming the population density as a proxy for estimating the baggage delivery service demands, the optimum locations of BSCs are determined by formulating and solving the standard p-median and the maximal covering location problems. The numerical results were obtained for Greater London, and also for the whole UK assuming all its major civilian airports. The Greater London area could be served by 36 BSCs to achieve a full service coverage. The 90% service coverage of the whole UK can be achieved by about the same number of BSCs, provided that the coverage distance is increased. In practice, the actual number of required BSCs crucially depends on the operational and capital costs, and the maximum processing capacity of each BSC. These findings have direct implications on the long-term planning and innovations in future air transport.

Electric Fences for Dockless Bike-Sharing Systems: An Electric Fence-Planning Framework for a Dockless Bike-Sharing System Based on a Land Parcel Subdivision and Regional Coverage Maximization

Dockless bike sharing (DBS) is convenient for bike users because it can solve their last-mile problems. However, dockless bikes that are disorderly parked in public spaces can disturb normal traffic. In planning electric fences for DBS systems, the maximum coverage location problem (MCLP) identifies locations suitable for electric fences. However, the MCLP model ignores the regional boundaries of bike use and causes substantial errors in coverage assessments. In this article, we present a novel framework for planning electric fences, accounting for both trip distribution and area shapes. We first introduce a dynamic land parcel subdivision algorithm, which can reasonably divide the city into fine-scale regions concerning the distributions of spatial parking demands. Then, we formulate the electric fence-planning problem as a regional coverage maximization problem. We develop an accelerated maximum coverage model with complementary coverage (MCMCC) to locate electric fence areas, which can improve solution accuracy and reliability. The DBS data of Shenzhen, China, are used to validate the framework. The study findings indicate that MCMCC produces more robust and accurate results than the MCLP model because the planning solutions are not affected by changes in the shapes and sizes of the regions. The framework can also automatically and accurately determine electric fence capacities and locations.

공공데이터 기반 공영주차장 최적입지 선정을 위한 최대 커버지역 문제(MCLP) 해결 기법

The illegal parking problem is the biggest problem around the world. Seoul and many metorpolitan cities are making great efforts to solve the problem by introducing policies to expand parking supply and curb parking demand. However, in urban areas of metropolitan cities, land prices are high and no site is available for parking lots, a vast budget is spent to make additional parking lots. Therefore, the creation of extra parking lots is extremely limited. Also, it is not easy to obtain parking lots because urban areas have a large floating population, an there is a variation in the floating population over time. In this paper, we present a method for selecting the optimal location for public parking lots through the Maximum Coverage Location Problem (MCLP). It allows policymakers to distribute parking demand in a metropolitan city and reduce parking concentration. Moreover, congestion on nearby roads can be alleviated by reducing queues for parking and wandering time to find a parking lot and reducing social costs to prevent illegal parking.

An agent-based modeling approach for public charging demand estimation and charging station location optimization at urban scale

As the market penetration of electric vehicles (EVs) increases, the surge of charging demand could potentially overload the power grid and disrupt infrastructure planning. Hence, an efficient deployment strategy of electrical vehicle supply equipment (EVSE) is much needed. This study attempts to address the EVSE problem from a microscopic perspective by formulating the problem in two steps: public charging demand simulation and charging station location optimization. Specifically, we apply agent-based modeling approach to produce high-resolution daily driving profiles within an urban-scale context using MATSim. Subsequently, we perform EV assignment based on socioeconomic attributes to determine EV adopters. Energy consumption model and public charging rule are specified for generating synthetic public charging demand and such demand is validated against real-world public charging records to guarantee the robustness of simulation results. In the second step, we apply a location approach – capacitated maximal coverage location problem (CMCLP) model – to reallocate existing charging stations with the objective of maximizing the coverage of total charging demands generated from the previous step under the budget and load capacity constraints. The entire framework is capable of modeling the spatiotemporal distribution of public charging demand in a bottom-up fashion, and provide practical support for future public EVSE installation.

Improving educational equity by maximizing service coverage in rural Changyuan, China: An evaluation-optimization-validation framework based on spatial accessibility to schools

Ensuring inclusive and equitable quality education is a primary goal in the United Nations' agenda for sustainable development. Under this global initiative, China has been striving to achieve equitable education in rural areas through the planning and development of rural schools. To this end, the paper focuses on the rural school planning issue by proposing an evaluation-optimization-validation framework in a case study in Changyuan, a county-level city in China. Specifically, by focusing on the nine-year compulsory education institutions, including elementary schools, junior high schools, and nursery schools, the paper first identifies the low-school-accessibility areas by a modified Huff three-step floating catchment area (MH3SFCA) method. Then, the paper optimizes the school locations using the maximum covering location problem by the complementary coverage (MCLP-CC) model by improving the equity of school access. Lastly, a comparative analysis before and after the optimization reveals that except for elementary schools, the spatial equality of the school-specific accessibility and that of the overall accessibility have both been improved to a significant degree. The proposed framework provides a new scientific procedure for schools’ location planning, and the implementation results can provide insights into future planning initiatives to promote educational equity in rural China and beyond.

A hybrid population-based algorithm for solving the fuzzy capacitated maximal covering location problem

In this paper, we study the fuzzy capacitated maximal covering location problem, where a hybrid population-based algorithm is proposed to solve it. An instance of the problem is represented by a set of customers in a given network with their distances and such that the coverage degree of facilities and the distance between customers are fuzzy. Its goal is to open (select) a subset of facilities to be located on customers such that the coverage of customers should be maximized. Two versions of a hybrid population-based algorithm are designed for tackling the aforementioned problem: (i) a modified grey wolf optimizer combined with exploration and exploitation strategies, and (ii) a modified grey wolf optimizer combined with a series of local searches and an upper bound delimiting wolf movements. Each version of the method enhances the quality of the solutions belonging to the current population by adding a series of local searches. Further, the exploration strategy is based upon the drop/rebuild operator for jumping from the current region to unvisited ones. Finally, the behavior of the proposed method is evaluated on a set of benchmark instances of the literature, where its provided results are compared to those reached by more recent methods of the literature and the state-of-the-art Cplex solver. Encouraging results have been obtained.

A flexible, leak crew focused localization model using a maximum coverage search area algorithm

Buried watermains are deteriorating and pipe failure is increasing in many cities. In response, advanced leak location models have been developed to help identify where a leak is occurring – which allows utilities to react quickly to pipe bursts and reduce the impact of the leak. This paper develops a new leak location model that is designed to identify optimal search areas for leak crews using a random forest classification model and the maximum coverage location problem algorithm. The model, when compared with other machine learning and clustering localization predictions, reduces the search space by over 35%, allowing utilities to confirm leak location and mitigate its impact more efficiently. The new model is also highly customizable, able to adjust the number of search areas and search size quickly and easily to meet leak crews’ requirements.

Maximum covering location problem to select facility location for operation timbang in the City of Iloilo, Philippines

Operation Timbang (OPT) Plus is one of the Philippines’ programs that focuses on nutrition by conducting an annual assessment for 0-59 months old children in barangays to identify the malnutrition data in the area. The barangay is the smallest administrative entity in the Philippines. OPT is a plan of action that estimates the number of malnutrition individuals and identifies those who will get prioritized programs in the community. The Iloilo City Health Office conducted the program in seven districts in the Philippines. The office planned to establish a community centre and playground facility based on the priority/demand areas. Maximum Covering Location Problem (MCLP) is used for this study to determine the optimal location that covers the area. A Mathematical Programming Language (AMPL) is used to apply mathematical programming to the MCLP. The results can be used to identify the optimal facility and the maximum coverage of the demand points. The experiment showed that the facility located in Mandurriao District is the optimal facility location. For Underweight/Severely Underweight children, a maximum total of 646 are covered, and for the Overweight/Obese, 1,041 are covered for the chosen facility. In addition, the findings of the sensitivity analysis indicate that the building of the three facilities in the case study can offer 100 percent of the required coverage area.

Optimizing Electric Motorcycle-Charging Station Locations for Easy Accessibility and Public Benefit: A Case Study in Surakarta

Many benefits follow from the use of Electric Vehicles (EVs) to replace fossil fuel-based vehicles (FVs), i.e., improved transportation energy efficiency, reduced carbon and noise emissions, and the mitigation of tailpipe emissions. However, replacing conventional FVs with EVs requires the establishment of a suitable charging infrastructure representing a commonplace detail that blends into the landscape and is available in various locations. This research focuses on the infrastructure of Electric Motorcycles (EM), constituting a relatively dense network of charging stations (CS), which is an essential factor in accelerating the commercialization of EM in Indonesia. In this case study, we propose a Charging Infrastructure Optimization approach for placing charging stations to meet the demand posed by motorcycles. This study uses motorcycle user data as the initiation data for electric motorcycle users. The selection of charging station development points uses the calculation methods of the centrality index and scalogram, which describe the density of community activities. After the charging station’s construction point is obtained, the point is validated with the optimization model that has been designed with respect to the Maximal Covering Location Problem. We also analyze the benefits and costs of constructing this charging station to determine its feasibility.

Using quantum computing to solve the maximal covering location problem

In this article, we present the process and results of using quantum computing (QC) to solve the maximal covering location problem proposed by Church and ReVelle. With this contribution, we seek to lay the foundations for other urban and regional scientists to begin to consider quantum technologies. We obtained promising results, but it is clear that there is a need for more capable devices with more qubits and less susceptibility to electronic noise to solve instances that currently cannot be optimally solved by traditional solvers. We foresee that QC will be of common use in urban and regional science and its applications in the years to come.

Assessment of shelter location-allocation for multi-hazard emergency evacuation

Intense rainstorms often trigger multiple disasters in mountain regions, such as floods and landslides. In disaster planning, the local administration allocates nearby schools or open fields as emergency evacuation shelters. However, access to these shelters is often cut off for certain population clusters during disaster impact on routes. We develop a framework for selecting emergency evacuation shelter locations for multi-disaster impact planning (floods and landslides). The framework consists of two parts. Firstly, we develop susceptibility maps of individual hazards using Random Forest algorithm and Google earth engine. Secondly, we assess the shelter's location-allocation by implementing two models in GIS: P-median and maximal covering location problem. Our framework treats existing schools as evacuation shelters and individual households as demand points in an emergency. The P-median method finds the shelter locations by minimizing maximum distances between the households. The maximal covering location problem method evaluates the coverage of households by the facilities of the evacuation shelters within an impedance cutoff. We tested our work in a mountainous village in the Western Ghat region, India, by recreating the 2005 rainstorm disaster that caused more than 190 fatalities and damaged 400 households. The result shows that existing shelters are insufficient to provide services to all households within 30 min and 60 min. This methodology helps develop simultaneous-hazard impact plans by local administration units in mountain regions to ensure emergency facilities' safe operation.

Multi-type maximal covering location problems: Hybridizing discrete and continuous problems

This paper introduces a general modeling framework for a multi-type maximal covering location problem in which the position of facilities in different normed spaces are simultaneously decided to maximize the demand generated by a set of points. From the need of intertwining location decisions in discrete and in continuous sets, a general hybridized problem is considered in which some types of facilities are to be located in finite sets and the others in continuous normed spaces. A natural non-linear model is proposed for which an integer linear programming reformulation is derived. A branch-and-cut algorithm is developed for better tackling the problem. The study proceeds considering the particular case in which the continuous facilities are to be located in the Euclidean plane. In this case, by taking advantage from some geometrical properties it is possible to propose an alternative integer linear programming model. The results of an extensive battery of computational experiments performed to assess the methodological contribution of this work is reported. Apart from a synthetic dataset, the different approaches are tested on a real geographical and demographic data with up to 920 demand nodes.

Submodular maximization of concave utility functions composed with a set-union operator with applications to maximal covering location problems

We study a family of discrete optimization problems asking for the maximization of the expected value of a concave, strictly increasing, and differentiable function composed with a set-union operator. The expected value is computed with respect to a set of coefficients taking values from a discrete set of scenarios. The function models the utility function of the decision maker, while the set-union operator models a covering relationship between two ground sets, a set of items and a set of metaitems. This problem generalizes the problem introduced by Ahmed S, Atamtürk A (Mathematical programming 128(1-2):149–169, 2011), and it can be modeled as a mixed integer nonlinear program involving binary decision variables associated with the items and metaitems. Its goal is to find a subset of metaitems that maximizes the total utility corresponding to the items it covers. It has applications to, among others, maximal covering location, and influence maximization problems. In the paper, we propose a double-hypograph decomposition which allows for projecting out the variables associated with the items by separately exploiting the structural properties of the utility function and of the set-union operator. Thanks to it, the utility function is linearized via an exact outer-approximation technique, whereas the set-union operator is linearized in two ways: either (i) via a reformulation based on submodular cuts, or (ii) via a Benders decomposition. We analyze from a theoretical perspective the strength of the inequalities of the resulting reformulations, and embed them into two branch-and-cut algorithms. We also show how to extend our reformulations to the case where the utility function is not necessarily increasing. We then experimentally compare our algorithms inter se, to a standard reformulation based on submodular cuts, to a state-of-the-art global-optimization solver, and to the greedy algorithm for the maximization of a submodular function. The results reveal that, on our testbed, the method based on combining an outer approximation with Benders cuts significantly outperforms the other ones.

Location Optimization of COVID-19 Vaccination Sites: Case in Hillsborough County, Florida.

The equitable allocation of COVID-19 vaccines is a critical challenge worldwide, given that the pandemic has been disproportionally affecting economically disadvantaged racial and ethnic groups. In the United States, the ongoing implementation efforts at different administrative levels and districts, to some extent, are standing in conflict with commitments to mitigate inequities. In this study, we developed a spatial optimization model to choose the best locations for vaccination sites. The model is a modified two-step maximal covering location problem (MCLP). It aims at maximizing the number of residents who can conveniently access the sites and mitigating inequity issues by prioritizing disadvantaged population groups who live in geographic areas identified through the CDC’s Social Vulnerability Index (SVI). We conducted our study using the case of Hillsborough County, Florida. We found that by reserving up to 30% of total vaccines for highly vulnerable communities, our model can optimize location choices for vaccination sites to provide effective coverage for residents at large while prioritizing disadvantaged groups of people. A series of sensitivity analyses have been performed to evaluate the impact of parameters such as site capacity and distance threshold. The model has the potential to guide the future allocation of critical medical resources in the U.S. and other countries.

Optimized Solutions for Deploying a Militarized 4G/LTE Network With Maximum Coverage and Minimum Interference

This work proposes to solve the maximal covering location problem of the Mobile Operations Coordination Center (CCOp Mv), which aims to support the operational command of the Brazilian Army. This problem consists of selecting, in a limited region and with poor communication infrastructure to the ground troops’s operating area, the positions of vehicles equipped with Base Transceiver Station (BTS), the amount needed, and theirs transmission power to be set that maximizes the coverage area and reduce the interference due to the overlap of signals. For this reason, analytical modeling based on the mixed-integer linear problem was proposed that guided two optimization solutions: (i) E-ALLOCATOR – Exact ALLOCATiOn seRvice; and (ii) M-ALLOCATOR – Metaheuristic ALLOCATiOn seRvice. The solutions were evaluated in a scenario that employs CCOp Mv to support a rescue operation based on the tragedy in January 2019 in Brumadinho-MG and compared with a heuristic. The performance evaluation results show evidence of efficiencies in terms of quality and resource savings of the proposed solutions. Furthermore, E-ALLOCATOR has been proven to be suitable for a low workload on the network. At the same time, M-ALLOCATOR is suitable for scenarios with a high workload providing almost optimal solutions within the adequate computational time for all problem instances.

OPTIMASI ALOKASI SISTEM PENGOLAHAN SAMPAH ANORGANIK DENGAN METODE CAPACITATED MAXIMUM COVERING LOCATION PROBLEM

Province X in Indonesia has a significant problem with trash that has been challenging to address. The Environment Agency therefore desires to construct an inorganic Trash Management System. The Environment Agency has four potential locations with various capacities. The purpose of this research is to locate a trash treatment system's best possible site and allocate each TPS that is less than 30 km and more over 30 km from the chosen system, respectively, to ensure that the overall distance traveled is as little as possible. There are four possible locations, and it required to select three of them. This study use the LINGO 18.0 software to solve the Capacitated Maximum Covering Location Problem (CMCLP) approach. Distance parameters used by CMCLP are split into two stages. The first stage is to determine the location of the trash treatment system to be built and the allocation of trash and the amount of trash from each TPS using Mix Integer Programming. B is not chosen out of the options, which are A, B, C, and D. System A will receive 1,407,520 tons of trash totaling 24 TPS. System C receives trash from 147 TPS weighing 1,294,495 pounds. System D will receive trash weighing 819,142 tons from 88 TPS. The allocation of trash from TPS that are more than 30 km away takes place in the second stage. Three TPS are assigned to System A, and seven TPS are assigned to System D.

A multicriteria GIS-based decision-making approach for locating electric vehicle charging stations

Electric vehicles (EVs) will be the next technological leap for urban mobility, however the market penetration rate depends on several factors, including the major hurdle of limited EV charging stations. Equity is also an important factor – ensuring EV charging stations are widespread and available for all segments of the population. This paper outlines an innovative methodology to systematically determine the locations of EV charging stations while considering equity and efficiency to maximize accessibility and usage. The methodology has two levels. First, solving what is known as the Set Covering Location Problem (SCLP) by determining a threshold so that the distance (or travel time) between the consumer and the EV charging station will be less than or equal to a given value. This is a policy-based decision and provides a framework to ensure EV charging stations are ubiquitous and equitable. Second is solving the Maximum Covering Location Problem (MCLP) by considering a series of evaluation criteria to satisfy the demand of the early adopters. Following evaluation of the SCLP and MCLP, selected locations are aggregated by partitions to develop larger scale hubs. These hubs will not only include EV charging stations but will also function as connection points that integrate different modes of transportation.

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.

Mathematical Models and Nonlinear Optimization in Continuous Maximum Coverage Location Problem

This paper considers the maximum coverage location problem (MCLP) in a continuous formulation. It is assumed that the coverage domain and the family of geometric objects of arbitrary shape are specified. It is necessary to find such a location of geometric objects to cover the greatest possible amount of the domain. A mathematical model of MCLP is proposed in the form of an unconstrained nonlinear optimization problem. Python computational geometry packages were used to calculate the area of partial coverage domain. Many experiments were carried out which made it possible to describe the statistical dependence of the area calculation time of coverage domain on the number of covering objects. To obtain a local solution, the BFGS method with first-order differences was used. An approach to the numerical estimation of the objective function gradient is proposed, which significantly reduces computational costs, which is confirmed experimentally. The proposed approach is shown to solve the maximum coverage problem of a rectangular area by a family of ellipses.