Location Optimization Research Articles

Intermodal container terminal location and capacity design with decentralized flow estimation

This paper studies an intermodal container terminal (IMT) location and design problem, where the IMT operator wants to locate a set of open-access IMTs and design their capacity levels to maximize its profit. Following the IMT operator’s decisions, network users, responsible for container transportation, will independently choose their routes and may procure intermodal services from the IMT operator. Since only limited information is available before the existence of the network, we employ the entropy maximization principle as a least-biased approach to estimate the flow distribution resulting from the network users’ route choices. This enables the IMT operator to predict profit and evaluate the quality of its network design decisions. We formulate the problem as a mixed-integer bilevel nonlinear program, automatically embedding a decentralized flow estimation scheme into the optimization of IMT location and capacity design. By exploring the rationale behind the entropy maximization principle, our problem can also be interpreted as a leader-follower game, in which the IMT operator (as the leader) aims to maximize its profit and the network users (as the follower) maximize their welfare. Due to the bilevel structure and the nonlinear entropy function, the problem is extremely changeling. To support its application in real-world contexts, we propose both exact and approximation algorithms. Finally, we conduct a real-world case study on Sydney Greater Metropolitan Area and draw managerial implications.

The effects of digital economy on manufacturing resource allocation efficiency across Chinese cities: a spatial economic perspective

ABSTRACT The development of digital economy provides significant impacts on spatial distribution of industries. Using microdata from Chinese manufacturing enterprises and adopting a spatial economic perspective, this study reveals that the digital economy exerts non-linear impacts on resource allocation. As the level of urban digital economy improves, the efficiency of manufacturing resource allocation shows an inverted U-shaped trend, and high hierarchy cities exhibit higher critical values. The study finds a positive spatial correlation in manufacturing resource allocation efficiency across cities; however, the growth of digital economy in neighbouring cities may lead to backwash effects and impede local improvements in allocation efficiency. The mechanism analysis indicates that the digital economy primarily affects spatial resource allocation through urban agglomeration economies and diseconomies. Consequently, economic agents and policy makers in cities should consider the dual effects of ‘location optimization’ and ‘transforming the economy from substantial to fictitious’ during digital transformation processes to select optimal development paths and improve allocation efficiency.

A novel trajectory planning method for mobile robotic grinding wind turbine blade

As the core component of wind turbine, wind turbine blade requires two grinding processes in the production. However, mobile robotic automation grinding of wind turbine blades is considered to be a challenging task due to the high aspect ratio and compound surface of the wind turbine blade. The trajectories generated by most robotic grinding trajectory planning algorithms are often found to be inferior in grinding large compound surface workpieces, as they are typically designed for robotic machining with a fixed base. In this paper, a novel iso-planar algorithm based on oriented bounding box (OBB) of the workpiece is developed to plan the grinding trajectories by taking into consideration the characteristics of blade. The constant chord length algorithm employing Taylor quadratic expansion is then developed to discretize trajectory into grinding points. Considering the characteristics of compound surface, a post-processing strategy is proposed to eliminate redundant grinding points and generate consistent tool orientations on compound surface. Based on these three steps, a workstation location optimization model for improving robot manipulability is introduced to determine a series of workstation locations. Furthermore, the grinding and movement synchronization strategy based on mobile platform trajectory interpolation is proposed to enhance the efficiency of machining large workpieces. The simulation and experiments demonstrate the effectiveness of the proposed trajectory planning method for mobile robotic grinding wind turbine blade, the rationality of the optimization model and the feasibility of grinding and movement synchronization strategy.

Dynamic Optimization of Exclusive Bus Lane Location Considering Reliability: A Case Study of Beijing

For metropolises like Beijing, heavy congestions cause transit passengers’ unreliable travel time, including in vehicle time and waiting time. Comparing with other managerial measures, designing a lane for bus use only is an effective method to improve travel reliability, for it can eliminate the influence on bus-driving conditions. This paper proposes a reliable and practical method to determine exclusive bus lanes (EBL). A reliability-based optimization model is established, in which the tradeoff among bus and private car passengers’ travel time, reliability, and EBL construction cost are considered. Based on the actual network, a user equilibrium demand assignment model is applied to estimate the dynamic bus flow distribution. Since the model is nonlinear, a two-step method is proposed where tangent lines are introduced to constitute an envelope curve to linearize the model. This work conducts the statistical modeling and fitting analysis with actual bus trajectory data, collected on EBL in Beijing during peak hours. Passenger travel time distributions are fitted to estimate the statistical passenger travel time; Lognormal distribution and Gaussian distribution are the best fit. The optimization results indicate that the passenger travel time reliability can be improved by 5.5% by the optimized EBL location scheme. This study will provide a theoretical basis and methodological support for improving the service level of the public transportation system in large cities through the scientific planning of exclusive bus lanes.

An event tree-based distance transform algorithm for simultaneously determining mountain railway alignments and station locations

The concurrent optimization of railway alignments and station locations (RA&SL) is a complex task, especially for the main railway lines in mountainous regions. The intricate coupling constraints between RA and SL are very challenging for the conventional RA&SL search methods, which consider limited solution types to generate an optimized RA&SL solution. To solve this problem, an event tree-based distance transform (ET-DT) algorithm is proposed. The study area is first divided into grid cells, followed by a preprocessing step to identify infeasible areas for station locations. Next, an event tree (ET) is constructed to represent all the possible solution types during the RA&SL design process. Afterward, in the ET-DT method, a total of eight types of RA&SL search operators are integrated to generate diverse RA&SL alternatives. Ultimately, the proposed method is applied to a realistic case with significantly-undulating terrain. Results show that ET-DT generates 2.8 times more solutions than a contemporary DT-based method (PreDT) and further improves solution quality through an 8.7% reduction in construction cost compared to the best manually-designed alignment, while PreDT only achieves a 5.7% reduction.

Metaheuristics-based multistage iterative optimization framework for decomposed high-speed rail systems planning problem

High-speed rail (HSR) systems planning and decision-making is a large-scale, multistage, intricate problem, where each stage represents a complex sub-problem. The solution approach to the multistage problem needs information exchange across the stages. A non-optimal planning and decision at any stage could yield a sub-optimal HSR system. This study proposes a novel multistage iterative HSR station location and alignment optimization framework (HSLAOF) to solve the main problem by decomposing it into the identification of (a) HSR potential regions and corridor, (b) station location, (c) shortest sequence/corridor of stations, and (d) collision-free alignment (horizontal and vertical) sub-problems. It integrates these sub-problems, which are solved separately using customized artificial intelligence- and motion planning-based metaheuristics such as particle swarm optimization for station location, ant colony optimization for shortest sequence of stations, path planner method for horizontal alignment and exploration, and exploitation-based ant colony optimization for vertical profile. In HSLAOF, the information from the upper and lower stages of the optimization process are exchanged and combined for a holistic optimized HSR system. Application of HSLAOF in the Mumbai-Ahmedabad HSR project yielded a solution that has 14.24 % lower total system cost, no environmental impact, 5.82 % better socio-economic impact, 21.14 % lesser noise and vibration impacted population, and 2.79 % higher station location utility than the one proposed by the experts. For predefined station location, the HSLAOF yielded alignment with 13.90 % lower total system cost and 24.00 % less noise and vibration impacted population than the one developed by experts. It required about 150 min to obtain a solution for each iteration.

6-DOFs Robot Placement Based on the Multi-Criteria Procedure for Industrial Applications

Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.

Strain Gauge Location Optimization for Operational Load Monitoring of an Aircraft Wing Using an Improved Correlation Measure

Operational loads of an aircraft are the prerequisite for assessing its safety or fatigue life. Traditionally, numerous strain gauge sensors are installed to monitor the operational loads, which inevitably increase the weight and system complexity of the aircraft. Therefore, in order to decrease the maintenance costs and data redundancy, the number and location of strain sensors should be optimized for accurate and reliable operational load monitoring. In this paper, a novel two-stage strain gauge location optimization method is proposed to reduce the number of strain gauges while maintaining the operational load monitoring accuracy, which is validated by a numerical case study of an aircraft wing. In the first stage, the traditional Pearson correlation measure is harnessed to initially eliminate numerous correlated strain gauge monitoring points, reducing 996 original strain gauge measurement points to 13 for the aircraft wing box. In the second stage, an improved correlation measure method is proposed to further reduce the 13 strain gauge points to 2, which can evaluate the correlation degree of several variables and simultaneously determine the optimal strain monitoring locations for the two load actuators in this study. The relative errors between the predicted loads and the actual loads for both load actuators are less than 4% when only two optimized monitoring points are adopted. In addition, a comparison study with LASSO regression and principal component regression methods is conducted. The results demonstrate that the proposed method has the characteristics of less monitoring points and higher load prediction precision.

CFD Analysis to Optimize Air Quality in Aeroponic Tower Garden

Aeroponic tower gardens represent an innovative approach to urban agriculture, utilizing vertical structures to cultivate plants without soil. Research highlights the efficiency of this method in optimizing resource usage, such as water and space, compared to traditional farming. Studies emphasize increased crop yields, faster growth rates, and reduced environmental impact. Additionally, aeroponic systems enable precise nutrient delivery to plants, fostering optimal growth conditions. While the technology shows promise, further investigations are needed to address potential challenges and optimize its application in diverse agricultural settings. The study presents the simulation of air temperature and humidity distribution in a closed chamber of the vertical farm for optimization purposes. The environmental conditions inside the chamber are optimized for plant growth, to maintain the temperature at a range of 18-24 °C temperature and the relative humidity at a range of 75-85 %. The paper also discusses the challenges, limitations, and future recommendations for improving the system. Simulation of the system, on the other hand, is crucial not only for prototype fabrication but also for the optimization of sensor locations. The simulation of the tower garden system was performed using ANSYS-FLUENT to portray how the airflow inside the tower garden's enclosed environment was distributed. The system parameters such as humidity and temperature were set to meet the plants' growth requirements, and the system was treated as the steady state with an adiabatic boundary. Based on the simulation results, the best design recommended is to use two humidity and temperature sensors, one at the top and the other at the core of the prototype. Hence, a complete mapping of temperature and humidity distribution that is critical for plant growth would be occurred.

Pertinence of finite element method for impulse localization in urban environments

Although urban combat is nothing new, recent conflicts have shed light on Western armies' difficulties when operating in such environments. Thus, new surveillance and hazard detection challenges in complex environments are addressed. A new decision aid tool for acoustic sensor positioning has been developed to improve reconnaissance performance. The software's sensor location optimization kernel is based on a genetic algorithm developed by our partners from FKIE. It resolves successive localization problems using pre-computed Times Of Arrival (TOA) between an impulsive acoustic source and various surveillance microphones. The present study discusses the pertinence of the Time-Domain Finite-Element Method (TD-FEM) to simulate wave propagation within an urban area to obtain TOA and resolve localization problems. The approach is compared to experimental measurements in an urban area, and the pros and cons of the TD-FEM for this configuration are discussed.

Optimization of Division and Reconfiguration Locations of the Medium-Voltage Power Grid Based on Forecasting the Level of Load and Generation from Renewable Energy Sources

The article addresses challenges in optimizing the operation of medium voltage networks, emphasizing optimizing network division points and selecting the best network configuration for minimizing power and energy losses. It critically reviews recent research on the issue of network configuration optimization. The optimization of the medium voltage power grid reconfiguration process was carried out using known optimization tools. The novelty lies in the inclusion of a probabilistic approach in the decision-making process in forecasting loads and generation from renewable energy sources (RES). Optimization studies utilizing heuristic optimization methods were completed, and an algorithm was developed for forecasting load and power generated from RES based on historical data and current weather data obtained from weather API. The solution proposed in the article allows multiple applications, including optimizing network division points’ locations (which decreases financial costs of modernizing network infrastructure) as well as improving the reconfiguration process, resulting in lower power losses while maintaining voltage requirements.

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations

Abstract Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.

Multi-RIS Deployment Location Optimization for Coverage Enhancement in Outdoor Wireless Communication Networks

Multi-RIS Deployment Location Optimization for Coverage Enhancement in Outdoor Wireless Communication Networks

Research on Factors Influencing the Production of Deep Shale Gas Wells in Weiyuan Block : A Case Study of X Well on Platform A

Abstract The practice of Weiyuan deep shale gas development shows that there are many factors that affect the gas production, resulting in significant differences in gas production between wells in the same region and on the same platform. In response to the phenomenon of low gas production, low wellhead pressure, and high flowback rate in Well X on Platform A, using data from drilling, fracturing, and gas production profile testing, this study examines the impact of geological and engineering factors on gas well production through gray correlation analysis and geological engineering integrated numerical simulation methods. The results show that: ① The production of adjacent wells reduces the formation energy, resulting in a large pressure drop rate after the pump is stopped in the corresponding fracturing section of the well, poor fracturing effect and low gas production contribution; ② The order of the influence of geological factors on the gas production rate of each section is: gas content > Si content > Al content > GR value> Ca content;③The segment with natural fractures and faults contributes significantly to gas production, but its long-term effectiveness needs further evaluation. This study will provide useful references for target location optimization, fracturing design optimization, and overall development deployment of deep shale gas horizontal wells in Weiyuan.

Optimizing Retail Recommendation via Similarity Measures and Machine Learning Approach

Finding a suitable retail business with potential success in a specific location can be challenging for retailers. The process is often lengthy and inconsistent due to the subjective nature of expert opinions. Previous research has demonstrated several techniques that consider numerous influential attributes for location optimization problems. However, while many studies rely on a business's core data for analytical purposes, accessing this information is often a significant constraint. This study aims to address the challenge of extracting valuable location features to enhance the profitability of chosen businesses despite the inaccessibility of core business data. The proposed methodology involves three main steps. First, an analytical dataset must be created by utilizing geographic and demographic information. Second, we conduct similarity measures by applying Manhattan distance to the entire analytical dataset, using an ideal business outlet that contains the footfall information. Through this process, we can identify businesses that share similar characteristics with popular outlets. Finally, several supervised machine learning models are trained to employ the extracted meta-features. Experimental results show that the XGBoost classifier performs best with an 87% accuracy score, outperforming the baseline models. The proposed methodology in this research presents a robust framework that demonstrated remarkable efficiency in achieving the stated objectives and improving the performance of retail business recommendations within a given location. Future work could consider a broader range of features that could potentially enhance model performance by applying ensemble learning.

Robust cooperative hub location optimization considering demand uncertainty and hub disruptions

Amidst the rise of economic globalization and increased commodity trading, the logistics industry is experiencing rapid growth, encountering intricate transportation demands and fierce market competition. Simultaneously, it faces challenges related to infrastructure development and resource allocation efficiency. To enhance the adaptability and robustness of transportation networks when facing uncertain demands and potential hub disruptions, this paper proposes a two-stage robust optimization model for cooperative hub location problem. The model utilizes a hybrid algorithm that effectively combines the global search capability of genetic algorithms with the step-by-step optimization efficiency of Benders decomposition. Case study analyses demonstrate that, irrespective of the uncertainty environment, the cooperative model maintains lower total costs. Particularly in the case of large demand fluctuations, its cost advantages over non-cooperative models become notably prominent, showcasing remarkable performance in meeting service demands and enhancing resource utilization. Additionally, in cooperative networks, hub disruptions have a more significant impact on hub location decisions, with penalty and supplementary costs further exacerbating this influence. These research findings offer crucial insights for management practices: in uncertain market environments, adopting cooperative and robust planning strategies is pivotal for mitigating operational risks. When making decisions on cooperative hub location selection, carriers should comprehensively consider the interaction between uncertainty and economic benefits to achieve an optimal balance between risk and cost, ensuring the sustained economic feasibility of cooperative ventures.

Simulation-Based Optimization for Vertiport Location Selection: A Surrogate Model With Machine Learning Method

We present Vertiport-informed Surrogate-Based Optimization with Machine Learning Surrogates (VinS), a novel framework for solving the vertiport location problem for urban air mobility operations. The primary focus of this work is on the optimization of vertiport locations to facilitate efficient urban air transportation. Our framework helps choose not only the optimal vertiport locations but also the optimal number of vertiports. We develop a simulation model to analyze the impacts of various vertiport location configurations on the efficiency of the transportation network. To expedite the simulation process, a surrogate model is trained using machine learning algorithms, effectively reducing the computational time for evaluating a given vertiport location configuration. With the machine learning surrogate models, we apply a genetic algorithm to find the optimal set of vertiport locations. An empirical study was performed in the San Francisco Bay Area, from which we found that given the optimal set of vertiport locations, we further reduced the total travel time in the entire transportation system in the Bay Area compared with the sampled sets by 0.05% (400 h) on average. We release our framework at: https://github.com/Xuan-1998/LPSim .

Sensor placement for optimal aerodynamic data fusion

Aircraft design is recently evolving towards a digital twin representation that involves many heterogeneous data sources. The aerodynamic development of aircraft usually incorporates data from computational fluid dynamics simulations, wind tunnel testing, and flight tests. All of these data sources have their advantages and disadvantages, which can optimally be combined using data fusion methods. However, the quality of the data fusion result strongly depends on the experimental design, i.e. the placement of discrete sensors. Therefore, an optimized sensor placement is essential for data fusion applications, as the number of sensors is limited. This work presents a sensor placement strategy for the widely used Gappy proper orthogonal decomposition data fusion methodology. The sensor placement relies on a Bayesian formulation of the data fusion, allowing accurate error estimates. Based on the Bayesian posterior, a utility function characterizes the quality of the fused result by quantifying the expected information gain for the proper orthogonal decomposition coefficients. As the optimization of the sensor locations involves a complex combinatorial problem, we introduce an efficient genetic algorithm for this task. The method is demonstrated on a two-dimensional airfoil and the NASA Common Research Model with synthetic measurement errors. For both test cases, an optimal sensor placement results in smaller reconstruction errors than a conventional layout. The Bayesian approach leads, in most cases, to more accurate reconstructions and is more versatile than other well-established sensor placement methods. The proposed genetic algorithm finds better optima with significantly fewer function evaluations than the widely used greedy algorithms.

Advanced Queueing and Location-Allocation Strategies for Sustainable Food Supply Chain

Background: This study presents an integrated multi-product, multi-period queuing location-allocation model for a sustainable, three-level food supply chain involving farmlands, facilities, and markets. The model employs M/M/C/K queuing systems to optimize the transportation of goods, enhancing efficiency and sustainability. A mixed-integer nonlinear programming (MINLP) approach is used to identify optimal facility locations while maximizing profitability, minimizing driver waiting times, and reducing environmental impact. Methods: The grasshopper optimization algorithm (GOA), a meta-heuristic algorithm inspired by the behavior of grasshopper swarms, is utilized to solve the model on a large scale. Numerical experiments demonstrate the effectiveness of the proposed model, particularly in solving large-scale problems where traditional methods like GAMS fall short. Results: The results indicate that the proposed model, utilizing the grasshopper optimization algorithm (GOA), effectively addresses complex and large-scale food supply chain problems. Compared to GAMS, GOA achieved similar outcomes with minimal differences in key metrics such as profitability (with a gap ranging from 0.097% to 1.11%), environmental impact (0.172% to 1.83%), and waiting time (less than 0.027%). In large-scale scenarios, GOA significantly reduced processing times, ranging from 20.45 to 64.78 s. The optimization of processing facility locations within the supply chain, based on this model, led to improved balance between cost (up to $74.2 million), environmental impact (122,112 hazardous units), and waiting time (down to 11.75 h). Sensitivity analysis further demonstrated that increases in truck arrival rates and product value had a significant impact on improving supply chain performance.

Research on origin-based cold storage location and routing optimization of fresh agricultural products based on hybrid whale algorithm

With the focus on the insufficient origin-based cold storage in China, this study investigates the location and routing problem (LRP) of origin-based cold storage for fresh agricultural products. This study considers the loss of fresh agricultural products in different environments during transportation and presents a cold storage LRP model. To address this issue, a hybrid whale algorithm with heuristic rules is designed, and the effectiveness of the algorithm is verified by standard instances. Finally, taking Chenggu County as a practical case, the influence of cold storage capacity and farmers’ demand for refrigeration are analysed. Experimental results show that the proposed algorithm has a good effect in solving medium-scale LRP. As the storage capacity increases, the total cost of the system can be increased by 0.086%. As farmers’ demand for refrigeration increases, the total cost of the system can be increased by 34.034%. Farmers’ demand has a greater impact on the system’s total costs than the cold storage capacity. When optimizing the cold storage layout, changes in fresh agricultural product output in the next few years can be roughly predicted, and the most economical optimization scheme can be obtained.