Location Optimization Research Articles

A cost-and-effect simulation model for compact city approaches: A case study in Japan

Japan's compact-city policy addresses rapid population decline and aging to ensure sustainable development. However, its implementation requires significant time, financial resources, and complex stakeholder collaborations. Faced with these challenges, we asked: How can the cost-and-effect of planning be simply and automatically recognized by stakeholders? To address this, a simulation model was developed for evaluating urban planning strategies within the compact-city framework. Our model integrates pseudo-people-flow data and includes three fundamental modules and five sub-models to simulate and predict urban metrics, including population, administrative costs, visitors, and traffic volume from 2015 to 2040. Four scenarios-baseline, location optimization, new road and new station installation-were analyzed. Findings suggest that 1) new station project could mitigate population decline, 2) both location optimization and new station projects improve public services efficiency and sustainability, 3) urban vitality could be enhanced by new station construction, generating a new urban core, and 4) compact city policy may also reduce and concentrate traffic volume, shortening car trips. The model's efficiency and uncertainty were examined, and user experiments highlighted the potential of digital tools to democratize urban planning, making it accessible for non-expert stakeholders and fostering informed participation. This research advances the use of technology in participatory urban planning.

Downlink Transmissions of UAV-RIS-Assisted Cell-Free Massive MIMO Systems: Location and Trajectory Optimization.

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF-mMIMO) system with a reconfigurable intelligent surface (RIS) carried by an unmanned aerial vehicle (UAV), called the UAV-RIS. Compared with the RIS located on the ground, the UAV-RIS has a wider coverage that can reflect all signals from access points (APs) and user equipment (UE). By correlating the UAV location with the Rician K-factor, we derive a closed-form approximation of the UE achievable downlink rate. Based on this, we obtain the optimal UAV location and RIS phase shift that can maximize the UE sum rate through an alternating optimization method. Simulation results have verified the accuracy of the derived approximation and shown that the UE sum rate can be significantly improved with the obtained optimal UAV location and RIS phase shift. Moreover, we find that with a uniform UE distribution, the UAV-RIS should fly to the center of the system, while with an uneven UE distribution, the UAV-RIS should fly above the area where UEs are gathered. In addition, we also design the best trajectory for the UAV-RIS to fly from its initial location to the optimal destination while maintaining the maximum UE sum rate per time slot during the flight.

When and where to step: Terrain-aware real-time footstep location and timing optimization for bipedal robots

Online footstep planning is essential for bipedal walking robots, allowing them to walk in the presence of disturbances and sensory noise. Most of the literature on the topic has focused on optimizing the footstep placement while keeping the step timing constant. In this work, we introduce a footstep planner capable of optimizing footstep placement and step time online. The proposed planner, consisting of an Interior Point Optimizer (IPOPT) and an optimizer based on Augmented Lagrangian (AL) method with analytical gradient descent, solves the full dynamics of the Linear Inverted Pendulum (LIP) model in real time to optimize for footstep location as well as step timing at the rate of 200 Hz. We show that such asynchronous real-time optimization with the AL method (ARTO-AL) provides the required robustness and speed for successful online footstep planning. Furthermore, ARTO-AL can be extended to plan footsteps in 3D, allowing terrain-aware footstep planning on uneven terrains. Compared to an algorithm with no footstep time adaptation, our proposed ARTO-AL demonstrates increased stability in simulated walking experiments as it can resist pushes on flat ground and on a 10° ramp up to 120 N and 100 N respectively. Videos22https://youtu.be/2pwDTEJzmjI. and open-source code33https://github.com/WangKeAlchemist/ARTO-AL/tree/master. are released.

The role of vehicular considerations in tsunami shelter location optimization

Using vehicles during tsunami evacuation might exacerbate road congestion, potentially increasing casualties. However, since the 2011 Tohoku Earthquake Tsunami, vehicular evacuation during tsunamis has partially been permitted, especially to assist elderly and disabled individuals with mobility challenges. The present study aimed to elucidate the impact of vehicular use on reducing mortality rate by introducing a new tsunami evacuation shelter in a given coastal area. To do so, the authors applied an agent-based tsunami evacuation simulation model that can account for behaviors of both pedestrians and vehicular evacuees to three Japanese coastal cities (Mihama Town, Shingu City, and Minabe Town) considering future Nankai–Tonankai Earthquake Tsunami scenarios. The authors first determined mortality rates under scenarios both with and without vehicular evacuation using current shelter placements. Subsequently, mortality rates with the introduction of a new tsunami evacuation shelter were examined for both scenarios. The findings showed that effective shelter locations for minimizing mortality rates varied depending on whether vehicular evacuation was considered. It was also found that the effectiveness of vehicular use during evacuation and the strategic placement of new tsunami evacuation shelters are highly dependent on local factors such as population distribution, existing shelter locations, and road layouts. These findings underscore the necessity for area-specific tsunami evacuation simulations to construct robust evacuation strategies.

Solving Optimal Electric Vehicle Charger Deployment Problem

Electric vehicles (EVs) have already been acknowledged to be the most viable solution to the climate change that the entire globe has long been combating. Along the same line, it is a salient subject to expand the availability of EV charging infrastructure, which quintessentially necessitates the optimization of the charger’s locations. This paper proposes to formulate the optimal EV charger location problem into a facility location problem (FLP). As an effort to find an efficient method to solve the well-known nonpolynomial deterministic (NP) hard problem, we present a comparative quantification among several representative solving techniques. This paper features two comprehensive case studies representing regions with an average and a high density of EVs. As such, this paper shows that the proposed framework can lead to successful location optimization with adequate refinement of solving techniques.

Study on location optimization of Floodlight Trucks

Study on location optimization of Floodlight Trucks

Incorporating decentralized facilities into the food waste treatment infrastructure in Megacity: A locational optimization in Beijing

Food waste management has emerged as a pivotal issue with the acceleration of urbanization. Inappropriate treatment of food waste causes environmental contamination, resource squandering, and land occupation. This research explores optimal strategies in Beijing with an experimental case study on the bioconversion technology using black soldier fly larvae (BSFL), comparing centralized and decentralized approaches. We studied waste sorting behaviors influenced by community characteristics, employing an Agent-Based Model (ABM) and a mixed integer non-linear programming (MINLP). Results indicate that 29 % of Beijing's food waste could be suitably managed through decentralized methods with BSFL, particularly in outlying areas and densely populated suburbs. Decentralized treatment exhibits the potential to boost waste separation participation in medium-sized, close- knit communities. However, this approach encounters hurdles related to technological implementation, public perception, and urban planning. This study underscores the need for community-specific strategies and a concerted effort from communities, government, and the private sector in advancing sustainable urban food waste management.

A novel mathematical model for simultaneous optimization of desalination plant location and water distribution network; A case study

In recent decades, water scarcity has turned into a serious problem spanning many countries, now even capable of causing or inflaming ethnic and national conflicts. While our planet has very limited freshwater resources, it has huge amounts of saltwater in seas and oceans. There is a very limited number of ways that can make saltwater drinkable, the most important of them is desalination. This study aimed to provide a method for the simultaneous optimization of desalination plant location and its water distribution network based on mathematical modeling. For this purpose, the authors formulated a non-linear mathematical model with the objective of minimizing the costs of water production and transmission. A genetic algorithm was also developed for solving the proposed nonlinear model. The method was used in a case study of Sistan and Baluchestan, which is one of Iran's most water stressed provinces. The proposed genetic algorithm managed to provide an acceptable solution for this problem in 3.74 s. The best solution was found to be constructing a desalination facility with a capacity of 394,052 cubic meters per day in a single location, that is, the city of Chabahar. The water transmission lines needed for transporting water to other parts of the province and their capacities were also determined.

Spatial Optimization for Energy Island Location and Cable Routing Considering Offshore Zoning

The energy hub concept presents a compelling opportunity to channel electricity from offshore wind farms to the grid, facilitating cross-border energy trading and conversion. However, one of the main challenges lies in the strategic identification of a suitable energy island location and the establishment of an interconnected cable routing path. Part of the complexity arises from the need to navigate offshore zoning regulations and accommodate various spatial uses within the offshore area. This paper exhibits a case study to explore the spatial optimization for a potential offshore energy hub in the North Sea. It aims at finding out an optimal spatial configuration of the offshore energy hub, such that the island location and the associated cable routing do not infringe upon keep-out zones while strategically positioning the hub closer to high-capacity wind farms. To achieve this goal, detailed geographical data incorporating offshore zoning information is leveraged for spatial optimization. The Dijkstra’s algorithm is then applied to identify the optimal island location and cable routing path. The effectiveness of this spatial optimization methodology is demonstrated through a case study involving offshore wind farm sites in the North Sea. Given the real geographical data, simulation results underscore the efficacy of the Dijkstra’s algorithm in determining the optimal energy hub layout. In particular, an optimal spatial design is achieved, based on cable lengths, asset capacities, offshore zoning and island/platform location for a potential offshore energy hub in the North Sea while ensuring compliance with keep-out zoning regulations.

Electric Vehicles Charging Services Perspectives and Challenges

Abstract The year 2023 marked another breakthrough in electromobility. Customers accept electric powertrain; sales of battery electric vehicles and plug-in hybrid vehicles are booming and public charging infrastructure follows the similar pace. However, the leading European independent charging stations operators, despite the significant public funding utilized for financing their expansion activities are still experiencing losses, as a result of their operation. Generally, current research work in area of electric vehicle charging is rather focused on specific and rather technical aspects of charging infrastructure operations, like optimization of charger locations, connection to energy networks and operational excellence. Very few authors are focusing on core business essentials of charging infrastructure operation, in particular on sustainability of their operations. Within this paper author conducted financial analyses of selected charging infrastructure operator in Europe, compared the conclusions with its peers. Furthermore, this paper has an ambition to perform synthesis (after thorough review and analysis of relevant information sources) of different aspects of e-mobility ecosystem to conclude innovative recommendations. The main result of this paper is the recommendation for the charging infrastructure operators to adjust their strategies and respective business models in terms to make a move towards slow charging world, by building and operating Vehicle to Grid equipped infrastructure as a supplement to fast chargers’ network. Sooner charging infrastructure operators will develop their competences in Vehicle to Grid world the better, mainly for two reasons. Firstly, it is very probable that Vehicle to Grid market will jump on fast track very soon. Not only from technology point of view, but also from services provided/expected from customers. Secondly, in Vehicle to Grid connected ecosystem lays needed tens or even hundreds of millions EUR opportunities which will be waiting for Electric Vehicle´s owners and any subjects which can facilitate and instrument interconnection between energy network’s needs – auxiliary services, storage of electricity, integration of renewable energy sources and traction batteries of Electric Vehicles.

Optimizing virtual power plant allocation for enhanced resilience in smart microgrids under severe fault conditions using the hunting prey optimization algorithm

The rapid expansion of renewable energy sources (RESs), such as photovoltaic (PV), wind turbine (WT), micro turbine (MT), and fuel cell (FC), has presented both opportunities and challenges to power systems, particularly in terms of environmental sustainability and economic feasibility. While RESs offer benefits like reduced environmental pollution, decreased power losses, and enhanced power quality, their intermittent nature and uncertainties pose challenges, resulting in variable generation and instability in distribution systems. To address these challenges, the concept of aggregating distributed energy resources (DERs), battery energy storage system (BESS), electric vehicles (EVs), and controllable loads into a virtual power plant (VPP) managed by an energy management system (EMS) has emerged. This study aims to determine the optimal location and size of VPPs within radial distribution system (RDS) while considering network resilience to severe weather events. The problem is formulated as an optimization task with dual objectives: minimizing the operating cost of VPPs and reducing energy not supplied (ENS) during natural disasters such as floods and earthquakes. To address this optimization problem, a novel meta-heuristic optimization algorithm called the hunting prey optimization algorithm (HPOA) is applied. HPOA serves various functions within the RDS, specifically targeting the optimization of VPP location, VPP resource management, and the objective functions. A case study is conducted, incorporating RESs, BESS, and EVs, and compared against existing algorithms such as BESA and SMA using a standard IEEE 85-bus RDS. Simulation results conducted in MATLAB demonstrate that the proposed HPOA algorithm effectively determines the optimal size and location of VPPs, leading to improved economic, operational, and resilience indices in the network.

Optimizing shared bike systems for economic gain: Integrating land use and retail

This study explores the optimization of shared-bike station locations, emphasizing the cycling role in low-carbon mobility and local economic revitalization. A unique aspect of this research is the incorporation of the retail activity index in determining station locations, land-use mix, transit proximity, and population and employment densities. A novel weighting method, combining the multi-criteria decision method “Criteria Importance Through Inter-criteria Correlation (CRITIC)” with Ensemble-based predictive algorithms (Random Forest, XGBoost, Gradient Boosting), was developed to evaluate these factors. Using a geographic information system-based spatial model, this study conducted a suitability analysis by applying these weights to assess and propose bike station sites. The findings from the Seoul case indicate a balanced land-use mix, a low number of transactions linked to cyclists, and a significant gap in multimodal transport integration. The weighting results revealed a high priority for transit proximity and retail activity in station placement. Suitability scores across districts revealed significant variability, suggesting the need for greater integration of cycling policies into urban planning. High-scoring districts, such as Gangnam-gu and Seocho-gu, offer insights for improvements in less suitable areas. This study advocates expanded cycling planning, focusing on enhancing access to public transit and retail areas.

Quantum entanglement and self-attention neural networks: An investigation into passengers and stops characteristics for optimal bus stop localization

Urban transportation significantly contributes to global carbon emissions, thus emphasizing the importance of green and efficient alternatives like public transportation. Enhancing the appeal of public transport through the strategic placement of bus stops can mitigate carbon emissions concurrently. This research proposes a Quantum Entangled Self-Attention Neural Networks (QESANN) model that considers both the passengers and stops characteristics to optimize bus stop locations. The QESANN model integrates the complexity of the relationship between passengers and bus stops characteristics, thereby fostering a more holistic approach to bus stop placement. It uses a Conditional Random Field (CRF) to initialize and process the attributes of both commuters and bus stops through quantum entanglement. The Vehicle Specific Power (VSP) model is employed to gage the variation in bus carbon emissions pre and post bus stop location optimization. Simulation tests, conducted via the Anylogic software and the IBM Quantum platform, demonstrated promising results. Over a yearly travel cycle for 1,000 individuals, the number of bus commuters rose from 322 to 651, concurrently reducing urban traffic carbon emissions by 430,243.70 kg annually. These findings affirm the effectiveness of the proposed QESANN model in enhancing the appeal of bus stops, reducing carbon emissions from urban transportation, and fostering a more sustainable urban environment.

Reactive bipedal balance: Coordinating compliance and stepping through virtual model imitation for enhanced stability

Compliance and step location adjustments are both important for robots to maintain balance when encountering external disturbances. Compared with controlling separately, unifying compliance with step location adjustments and integrating them into real-time pattern planning gets obvious advantages. However, the controllers’ models differ significantly from each other and from the dynamics of real position-controlled robots, compromising the stabilizing performance. In this paper, we propose a Virtual Model Imitation (VMI) strategy that unifies the controllers’ models and aligns them with actual robot dynamics. Based on this, a reactive pattern planning method is proposed which simultaneously coordinates compliance and step location adjusting strategies. The approach involves emulating the passive dynamic motion of a Linear Inverted Pendulum Model (LIPM) to enable the robot to respond to external disturbances with compliant motions automatically. Additionally, Zero Moment Point Intervened (ZI) estimation is employed to precisely determine the Center of Mass (CoM) state. Real-time optimization of step location is then conducted to stabilize the estimated CoM, actively responding to external disturbances. Importantly, the decision to adjust step location is quantitatively defined. To enhance efficiency, explicit solutions for both ZI Estimation and Step Location Optimization are provided to reduce computational costs. To validate our approach, we conducted simulations and experiments using the biped robot BHR-T, subjecting it to unexpected external pushes and obstacles during walking.

Road network, landing location, and routing optimization for forest smallholders landscapes

AbstractWe present two mixed integer linear programming (MILP) formulations for a well‐known integrated network, timber landing location, and routing problem that arises in forest management. The models seek to jointly optimize the construction and maintenance schedule of forest road networks with landing site selection and transportation routing for timber production. This problem is, in general, difficult to solve as it contains the so‐called fixed charge network flow problem, which is known to be NP‐hard. One of the proposed MILP formulations considers 3‐index continuous variables to represent timber flows on road segments in each period. The presence of Big‐M constraints leads to weak linear relaxation bounds. Disaggregating flow variables, according to timber origin, results in a novel 4‐index formulation with very tight linear relaxation bounds. Nevertheless, the number of variables increases prohibitively. This research makes use of spatial constraints common to Smallholding Forested Landscapes to develop a solution approach that reduces the number of flow variables in the new 4‐index model. Results from a real‐world case study located in Northwest Portugal show that, with the 4‐index formulation, the proposed solution approach makes it possible to obtain optimal solutions in a short computational time.

The implications of using maternity care deserts to measure progress in access to obstetric care: a mixed-integer optimization analysis

BackgroundLack of access to risk-appropriate maternity services, particularly for rural residents, is thought to be a leading contributor to disparities in maternal morbidity and mortality. There are several existing measures of access to obstetric care in the literature and popular media. In this study, we explored how current measures of obstetric access inform the number and location of additional obstetric care facilities required to improve access.MethodsWe formulated two facility location optimization models to determine the number of new facilities required to minimize the number of reproductive-aged women who lack access to obstetric care. We define regions with a lack of access as either maternity care deserts, designated by the March of Dimes to be counties with no obstetric care facility or obstetric providers, or regions further than 50 miles from critical care obstetric (CCO) services. We gathered information on hospitals with obstetric services from Georgia Department of Public Health public reports and estimated the female reproductive-age population by census block group using the American Community Survey.ResultsOut of the 1,910,308 reproductive-aged women who live in Georgia, 104,158 (5.5%) live in maternity care deserts, 150,563 (7.9%) reproductive-aged women live further than 50 miles from CCO services, and 38,202 (2.0%) live in both maternity care desert and further than 50 miles from CCO services. Our optimization analysis suggests that at least 56 new obstetric care facilities (a 67% increase) would be required to eliminate maternity care deserts in Georgia. However, the expansion of 8 facilities would ensure all women in Georgia live within 50 miles of CCO services.ConclusionsCurrent measures of access to obstetric care may not be sufficient for evaluating access and planning action toward improvements. In a state like Georgia with a large number of small counties, eliminating maternity care deserts would require a prohibitively large number of new obstetric care facilities. This work suggests that additional measures and tools are needed to estimate the number and type of obstetric care facilities that best match practical resources to meet obstetric care needs.

Location optimization of emergency medical services: Considering joint service coverage of ambulances and emergency centers

Emergency Medical Services (EMS) play an essential role in saving lives and improving health outcomes by offering immediate medical care to individuals who experience sudden illnesses or injuries. A complete EMS journey consists of two related trips: one from an EMS station to a scene (Trip 1), and the other from a scene to a definitive care location (Trip 2), where the service is coordinately provided by two types of facilities: EMS stations/ambulances and emergency centers (e.g., trauma centers or stroke centers) that are often affiliated with general hospitals. Current work on EMS location optimization considers only one trip (Trip 1 or Trip 2) which ignores the coordination between EMS stations and emergency centers, or the overall trip alone that overlooks the response time requirement. This paper proposed a spatial optimization model, the maximal coverage location problem based on joint coverage (MCLP-JC), for siting EMS stations and emergency centers simultaneously with a consideration of the two related trips. An empirical study of stroke center planning in Wuhan, China, is implemented to compare the proposed approach with the maximal coverage location problem based on overall coverage (MCLP-OC). The results demonstrate that the MCLP-JC can ensure more people being able to receive the first care from an ambulance within the response time requirement, which is critical to subsequent treatment at emergency centers and the odds of survival. The findings from the two scenarios regarding service relocation and expansion offer insights for future health facility planning.

Effect of Cross-Well Natural Fractures and Fracture Network on Production History Match and Well Location Optimization in an Ultra-Deep Gas Reservoir

Understanding subsurface natural fracture systems is crucial to characterize well production dynamics and long-term productivity potential. In addition, the placement of future wells can benefit from in-depth fracture network connectivity investigations, vastly improving new wells’ profitability and life cycles if they are placed in dense, well-connected natural fracture zones. In this study, a novel natural fracture calibration workflow is proposed. This workflow starts with the extraction of sector geology and a natural fracture model from the pre-built full-field model. Then, a cross wellbore discrete fracture network (CW-DFN) is created using a novel CW-DFN generation tool, based on image log data. An innovative fracture network identification tool is developed to detect the interconnected regional fracture network (IcFN) with CW-DFN. The non-intrusive embedded discrete fracture model (EDFM) is utilized to numerically incorporate the complex IcFN and CW-DFN in a reservoir simulation, and it is history-matched by tuning their conductivities. This workflow is applied to a single vertical well within a natural fracture carbonate reservoir in Northwest China. The study results show that the number of CW-DFNs is 11, and the number of IcFNs is 72. The non-intersected natural fractures only account for 5.5% of the production, and thus can be removed to improve simulation efficiency. The history-matching absolute average relative deviation (AARD) is 15.16%. The calibrated effective fracture permeability is 280 millidarcy, with an aperture of 0.001 m, equating to a conductivity of 0.28 millidarcy-meter. The 30-year gas production forecast is estimated to be 1.66 billion cubic meters based on a history-matched model. Finally, if the well is drilled to the east of the sector, 30-year production declines to 1.33 billion cubic meters (a reduction of 20%). However, if the well is drilled to the west of the sector, 30-year production increases to 2 billion cubic meters (an improvement of 20.5%).

Enhancing Logistics Optimization

With the expansion of the logistics network, enterprise logistics distribution faces increasing challenges, including high transportation costs, low distribution efficiency, and unstable distribution networks. To address these issues, this study focuses on optimizing enterprise logistics distribution using a double-layer (DL) model. In this paper, we propose a DL model for optimizing enterprise logistics distribution. The DL model is designed to find the optimal solution using the particle swarm optimization (PSO) algorithm. By leveraging location data from the region, the DL model evaluates and compares alternative distribution centers to determine the most efficient distribution strategy. The results demonstrate that the DL site selection model developed in this study effectively addresses the tasks of logistics center location and distribution optimization among alternative distribution centers. Comparison tests reveal that the distribution path proposed by the DL model is more accessible and cost-effective compared to alternative approaches.

Data-driven traffic sensor location and path flow estimation using Wasserstein metric

This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality.