Line Planning Research Articles

Predicting Cyclist Speed in Urban Contexts: A Neural Network Approach

Bicycle use has become more important today, but more information and planning models are needed to implement bike lanes that encourage cycling. This study aimed to develop a methodology to predict the speed a cyclist can reach in an urban environment and to provide information for planning cycling infrastructure. The methodology consisted of obtaining GPS data on longitude, latitude, elevation, and time from a smartphone of two groups of cyclists to calculate the speeds and slopes through a model based on a recurrent short-term memory (LSTM) type neural network. The model was trained on 70% of the dataset, with the remaining 30% used for validation and varying training epochs (100, 200, 300, and 600). The effectiveness of recurrent neural networks in predicting the speed of a cyclist in an urban environment is shown with determination coefficients from 0.77 to 0.96. Average cyclist speeds ranged from 6.1 to 20.62 km/h. This provides a new methodology that offers valuable information for various applications in urban transportation and bicycle line planning. A limitation can be the variability in GPS device accuracy, which could affect speed measurements and the generalizability of the findings.

A rule-driven method for disassembly sequence real-time planning of intelligent mixed-flow disassembly line

Intelligent mixed-flow disassembly serves an important role in enhancing the flexible operation capability of the disassembly line for multi-variety and small-batch used electromechanical products, and in forming the scale benefit of recycling. However, in mixed-flow disassembly lines, pre-planning the disassembly sequence for products is typically unfeasible and can lead to “over-disassembly” or “under-disassembly”, resulting in economic losses. This is attributed to significant variations in disassembly features, such as internal structural damage and subassembly recycling income, even within the same type of used electromechanical product. These differences contribute to considerable uncertainties in disassembly sequence planning. To this end, a method is proposed to leverage dynamic disassembly feature information for mining disassembly rules, efficiently driving disassembly sequences real-time planning, and realizing intelligent optimization of product disassembly profit on mixed-flow disassembly line. First, a multi-matrix collaboration based dynamic representing method of product disassembly feature information is proposed. It aims to eliminates the influence of uncertain information on the disassembly sequence planning by recording real-time disassembly feature information, such as disassembly operation time and subassembly recycling income. On this basis, a rule-driven disassembly sequences real-time planning model is built. This model realizes disassembly sequence real-time planning based on offline mined rules using the updated disassembly feature information in matrices. Finally, an improved gene expression programming-based disassembly rule offline mining algorithm is proposed, which incorporates a mutation probability mapping function to improve the efficiency and effectiveness of disassembly rule mining. A case study containing two products with differences in structure and function are used to confirm the effectiveness of the proposed method.

Deciphering Osteosarcopenia through the hallmarks of aging

Osteosarcopenia is a major driver of functional loss and a risk factor for falls, fractures, disability and mortality in older adults, urgently requiring the development of effective interventions to address it. The hallmarks of aging provide a theoretical and practical framework that allows for the structured organization of current knowledge and the planning of new development lines. This article comprehensively reviews the currently available literature on the role of the hallmarks of aging in the development of osteosarcopenia, thereby offering a panoramic view of the state of the art and knowledge gaps in this field.

Estimating electrical distribution network length and capital investment needs from real-world topologies and land cover data

Green technologies such as solar photovoltaic systems and electric vehicles play a fundamental role in the global decarbonization effort. To enable their diffusion, electricity distribution networks need to be upgraded, which is a complex and expensive endeavor. However, utilities face budgetary constraints and seek to reduce planning uncertainty. Here, we utilize 7,527 real-world grid topologies and land cover data to develop a model for estimating conductor and capital investment needs for electrifying a specific area. Our work yields three main contributions: First, we demonstrate the important role of land cover data in power line planning. We show that, for medium and large networks, distinct methodologies are needed due to the significant impact of land cover, particularly buildings and roads. Second, we introduce a parsimonious model of power line length and identify the number of consumption points as the primary determinant of network investment costs. Third, we present a cost assessment model tailored for regulators and investors, offering valuable insights for network planning, policymaking, due diligence, and research. Our work highlights the importance of combining land cover data and operations research algorithms in distribution network planning and provides policymakers with a tool to ensure cost-efficient network expansion.

Automatic Optimal Design Method for Minimum Total Resistance Hull Based on Enhanced FFD Method

Hull optimization plays a crucial role in enhancing ship performance and efficiency. This study aimed to improve ship performance, particularly by reducing total resistance, through automated optimization methods. To this end, an integrated, fully automated optimization program was developed based on Python, incorporating Enhanced Free-Form Deformation (FFD) technology, scripted CFD numerical evaluation, and the Particle Swarm Optimization (PSO) algorithm. This program allowed precise control of hull form, improving efficiency while reducing costs. The KCS hull, known for its excellent resistance performance, was chosen as the optimization target, with the goal of minimizing total resistance by adjusting the bulbous bow line plan. The research results indicated that the optimized scheme exhibited lower resistance characteristics compared to the original design while satisfying design constraints. This study not only provides a new optimization strategy for ship design but also lays a foundation for future hull optimization research.

Strategic maintenance planning in the digital era: a hybrid approach merging Reliability-Centered Maintenance with digitalization opportunities

AbstractIn the age of digital transformation, maintenance operations are crucial for leveraging the potential of Industry 4.0 and 5.0. Yet, this domain remains significantly under-optimized in terms of strategic maintenance planning and enhancing asset performance. The advent of smart technologies offers a myriad of innovative avenues; however, harnessing these effectively requires systematic planning that incorporates these new, various and quite diversified, smart practices. Thus, this paper proposes a new methodological approach to maintenance planning, based on the Reliability-Centered Maintenance method, aimed at providing an operative tool for organizations to foster the evolution of their maintenance plans towards the paradigm of digitalization. This novel method enables the identification of hidden opportunities of improvement not identifiable through the use of the traditional approach through the proposal of an Opportunity Index, to use together with the Criticality Index in asset selection, and a Digitalization Score to use during Failure Mode, Effects, and Criticality Analysis. The proposed method is applied to transform the maintenance planning of a production line, thus identifying the opportunities of the approach and testing its feasibility.

Aggregated planning to solve multi-product multi-period disassembly line balancing problem by considering multi-manned stations: A generic optimization model and solution algorithms

Disassembly lines are crucial in the recovery process of end-of-life (EOL) products, facilitating the systematic extraction of parts to meet predetermined objectives. While previous studies have made significant advancements, they have not addressed the disassembly line planning and balancing problem by focusing on the real-life significance of combining long-term decision-making and short-term operational missions. This study fills this gap by combining multi-product, multi-period, and multi-manned disassembly line concepts into a novel integrated approach. Initially, a generic optimization model is formulated, incorporating the concept of multi-manned stations. Subsequently, genetic algorithm (GA)-based solution approaches are developed to address the problem’s complexity. Three tactical-level policies, including economic disassembly quantity (EDQ), just-in-time (JIT), and random (R) based lot size policies, are explored within the algorithms. Additionally, simulated annealing-based local search algorithms and two crossover operators, CR1 and CR2, are incorporated to enhance solution quality. Through computational analysis, six algorithms are evaluated, with the GACR1-JIT algorithm demonstrating significant cost reductions compared to alternatives. The findings underscore the growing importance of JIT-based lot size policies, particularly with an increasing number of periods. This research bridges theoretical and practical considerations by highlighting the strategic importance of combining long-term disassembly line planning and short-term lot sizing decisions to improve system performance.

Integrated solutions for line planning and seafloor surface fitting in marine surveying

Integrated solutions for line planning and seafloor surface fitting in marine surveying

Line optimization of multi-beam sonar sounding technology in ocean surveying and mapping

Abstract In this paper, the problem of line optimization existing in the practical application of multi-beam sonar-sounding technology is studied. Firstly, by analyzing the working principle of multi-beam sonar, the multi-beam coverage width model and the overlap rate model between adjacent strips are established considering the slope of seabed terrain. Then, a more general parameterized multi-beam coverage width formula with an adjustable azimuth angle of the line is derived under three-dimensional space coordinates. Then, the minimum total line length is defined as the objective function, and the constraint conditions of overlap rate and complete coverage are added to establish the mathematical model of line planning. Then, an iterative algorithm is designed to quickly determine the line spacing, and an improved particle swarm optimization algorithm, including a trigonometric function change learning factor, is used to search for the optimal line layout. Finally, the simulation results show that the optimized scheme can reduce the total line length by 2% compared with the unoptimized line layout, which verifies the effectiveness of the established model and algorithm. This study provides a theoretical basis for navigation planning and shipboard system optimization of multi-beam sonar survey. Subsequent work taking into account the influence of complex sea conditions, efforts were made to expand the model’s applicability.

Research on multi-beam survey line system based on dynamic programming algorithm

This paper establishes a calculation model for the coverage width and overlap rate of a multi-beam survey line system [1], and optimizes the multi-beam bathymetric survey plan for marine areas. Through the dynamic programming algorithm, a reasonable survey line plan is designed based on the actual marine area depth. The research content includes: Model Establishment: Construct a calculation model for coverage width and overlap rate, determining the coverage width and overlap rate at each position. Three-Dimensional Space Model: Using the survey vessel as the origin and combining marine area information, solve for the coverage width at different angles. Shortest Survey Line Plan: Using the dynamic programming algorithm, determine the shortest survey line plan, with the dynamic transfer equation determined by the overlap rate index. The total length of the survey line in this plan is calculated to be 114,824 meters. Specific Survey Line Plan Design: Using sea depth data and cubic spline interpolation to complete the discrete point plane, construct approximate width and slope. Under the condition of relaxing the overlap rate to 5%-30%, the shortest survey line length is obtained as 398,059 meters, with a missing survey area ratio of 0%, and the total length of parts with an overlap rate exceeding 20% is 25,759 meters, accounting for 6.4%. This study provides theoretical basis and calculation methods for optimizing multi-beam bathymetric survey plans.

A Cost-Effective Approach for the Integrated Optimization of Line Planning and Timetabling in an Urban Rail Transit Line

A Cost-Effective Approach for the Integrated Optimization of Line Planning and Timetabling in an Urban Rail Transit Line

Integrated optimization of train line planning and timetabling: a new method of changing train operation zone and direct-service of cross-line ODs

ABSTRACT Cross-line operation increases accessibility to the railway network. However, its long-distance operation is quite difficult due to the transport resource limitations. To provide good transportation plans for operators and more direct services for cross-line passengers, we propose a new method of changing operation zones of cross-line trains, which can be summarized as the integrated train line planning and timetabling problem. A model is established to minimize deviation of same-line trains from ideal timetables and maximize cross-line origin-destination direct services. A Lagrangian relaxation approach and an improved priority-rule heuristic algorithm are developed to solve large-scale problems and obtain feasible solutions. Reality-based instances are conducted on the Beijing-Shanghai high-speed railway and its connecting lines to verify the performance of the model and solution approach. The results show the benefit of changing train operation zones, and the model and approach can help to generate a good balance between operating profit and passenger demand.

Systems Engineering Application for Better Design and Analysis of an Assembly Process

AbstractThe use of systems engineering has proven effective in managing complexity and improving system design. Model based system engineering utilizing the Systems Modeling Language (SysML) especially helps in multidisciplinary environments where engineering data needs to be transformed and integrated between environments. Manufacturing is another discipline same as systems engineering is on active digitalization transformation. In the paper, we propose method and solution to apply Model based system engineering (MBSE) for improvement of process planning of assembly lines leveraging model based approach. SysML modeling and execution enable automation of analysis activities as trade‐off, where the behavior of various assembly scenarios of an assembly line can be captured using SysML behavioral diagrams and compared based on various evaluation criteria. However, relying solely on descriptive SysML system models without integrating the virtual representation of the assembly line is insufficient to verify all aspects of system behavior, such as ergonomics and collision avoidance. The main objective of this work is to present a concept for transforming SysML assembly scenarios into the process and resources models of the computer‐aided design and manufacturing (CAD/CAM) environment. This yields a holistic view that serves as a foundation for further production‐related simulations and analyses, enhancing efficiency, ergonomic design, factory layout, and material flow, ensuring effective assembly workstation design optimized at systems engineering level.

Simulation and analysis of optimal energy-saving mode in microfield of underground rail transit

Underground rail transit has become one of the most popular transportation modes because of its advantages such as fast running speed, large passenger flow and punctuality. This mode of transportation can alleviate urban traffic congestion to a certain extent, so underground rail transit has been vigorously developed, and the number of rail lines has increased exponentially. However, with the continuous development of underground rail transit, the energy consumption of train operation is also increasing, resulting in a waste of energy. To solve this problem, this paper proposes to improve the energy-saving technology of train operation by using train autopilot control strategy. Firstly, a train operation optimization model considering train position and speed limit is established by using automatic train driving strategy, and the nonlinear problem of the optimization model is solved by genetic algorithm. The micro-field theory is introduced into the control strategy of automatic train driving, and the energy-saving model of underground rail transit is established. The energy consumption is optimized from three aspects: train energy-saving technology, line planning and overall operation planning of rail transit. The simulation results show that the average impact rate and energy consumption of trains under the proposed energy-saving model are significantly reduced compared with the other two groups of trains, and the average impact rate and energy consumption are significantly reduced compared with the other two groups of trains. The running time and energy consumption of the energy-saving model are lower than those of the experimental group. To sum up, the energy-saving model of underground rail transit proposed in this paper not only reduces the operating energy consumption, but also improves the passenger comfort, which can provide low-cost energy-saving technology for underground transportation field, and has positive significance for urban low-carbon development.

Collaborative Optimisation of High-Speed Rail Express Line Planning and Freight Flow Allocation Considering Multiple Transportation Modes and Products

Collaborative Optimisation of High-Speed Rail Express Line Planning and Freight Flow Allocation Considering Multiple Transportation Modes and Products

Active Distribution Network Expansion Planning Based on Wasserstein Distance and Dual Relaxation

In the future, a high proportion of distributed generations (DG) will be integrated into the distribution network. The existing active distribution network (ADN) planning methods have not fully considered multiple uncertainties, differentiated regulation modes or the cost of multiple types of interconnection switches. Meanwhile, it is difficult to solve large-scale problems at small granularity. Therefore, an expansion planning method of ADN considering the selection of multiple types of interconnection switches is proposed. Firstly, a probability distribution ambiguity set of DG output and electrical-load consumption based on the Wasserstein distance is established for dealing with the issue of source-load uncertainty. Secondly, a distributionally robust optimization model for collaborative planning of distribution network lines and multiple types of switches based on the previously mentioned ambiguity set is established. Then, the original model is transformed into a mixed integer second-order cone programming (SOCP) model by using the convex relaxation method, the Lagrangian duality method and the McCormick relaxation method. Finally, the effectiveness of the proposed method is systematically verified using the example of Portugal 54. The results indicate that the proposed method raises the annual net profit by nearly 5% compared with the traditional planning scheme and improves the reliability and low-carbon nature of the planning scheme.

Research on survey line arrangement of a multibeam bathymetric system in ocean mapping

Submarine topography measurement technology is an important technology for exploring marine resources. Multi-beam bathymetry systems have high efficiency and important application value in underwater resource surveys, marine delimitation, and other fields. They are currently one of the most widely used measurement technologies. This study aims to optimize multi-beam bathymetry technology and apply it to seabed topography measurement, achieving a transition from point measurement to line measurement and minimizing measurement paths. A mathematical model was established for the coverage width and overlap rate between adjacent bands of multi-beam bathymetry at a given water depth based on planar geometric modeling and three-dimensional geometric modeling. The model was applied to a rectangular sea area with a length of 2 nautical miles (3704 meters) from north to south and a width of 4 nautical miles (7408 meters) from east to west. The optimal survey line planning was obtained as follows: all survey lines are in the north-south direction, with a total of 34 survey lines and a total length of 68 nautical miles. This model has good solution results for optimizing the layout of survey lines under different seabed conditions and can adjust important parameters of the system according to different conditions. The model has broad practical significance and can provide a reference for ocean-sounding work and other fields with high accuracy.

Smart line planning method for power transmission based on D3QN‐PER algorithm

AbstractThe planning of power transmission line projects encompasses vast and complex geographical terrains. To address the complexity of transmission line planning and achieve lower line costs, this study proposes a novel intelligent line planning method. For the first time, it combines the Dueling Double Deep Q Network (D3QN) with the prioritized experience replay (PER) mechanism. First, correlate the reward function with metrics such as line length, number of corner points, and geographical environmental data, which are pertinent to the construction costs of power transmission line. Second, the D3QN algorithm is formulated by integrating Double DQN and Dueling DQN. The network's input information is divided into two components during training, aligning with the characteristics of power transmission line planning projects. Finally, the convergence efficiency of the algorithm is improved by using the PER mechanism for the problem of cost difference due to the different number of corner points in the planning path. In order to test the feasibility of the algorithm, we conducted experiments using real maps. Compared with the traditional ant colony optimization (ACO) algorithm, the D3QN‐PER deep reinforcement learning algorithm reduces the line length by more than 4% and the number of corner points by more than 60%.

Power distribution system resilience to extreme dust storms: probabilistic impact assessment and hardening planning

ABSTRACT Extreme dust storms (EDSs) are high-impact low-probability natural disasters, and their occurrence in humid climates can damage the power distribution systems (PDSs) as a critical infrastructure. In this paper, proposed a bi-level stochastic framework for simultaneously hardening substations and distribution lines. In the first level, total capital cost is addressed for PDS hardening under the financial constraints, while in the second level, the expected operating costs are minimized in the case of an EDS under the operating constraints. In the proposed model, the location of remote-controled switches (RCSs) is determined based on the PDS hardening planning results, and the decisions at each level depend on the planning results of the other level. The simulation results at different budget levels show that simultaneous hardening planning of distribution lines and substations considering network reconfiguration can not only reduce expected operating costs, but also can reducing total capital cost to PDS resilience enhancement.

Blockchain design for optimal joint production and maintenance over multiple periods for oil-filling production lines

Joint maintenance and production planning result in enhanced system efficiency and a significant reduction in expensive costs. Therefore, this research proposed an integrated blockchain design of two optimization models for the joint scheduling and sequencing of production orders and maintenance jobs over multiple periods with probabilistic and stochastic parameters for the oil-filling process. In the scheduling model, the objective function was to minimize total production and maintenance costs. Further, the sequencing model aimed to minimize the total overtime cost and the sum start times of jobs and orders. The proposed models were implemented on five production lines of five-liter lube oil bottles. Results showed that the developed optimization models effectively achieved concurrent production and maintenance planning at minimal total cost. In practice, this results in effective resource utilization, and saving costly production and maintenance costs. In conclusion, the developed blockchain design is valuable in developing, managing, and controlling optimal joint production and maintenance planning of oil-filling production lines.