Minimum Travel Distance Research Articles

Comparative assessment of global warming potential of gasoline, battery, and hybrid vehicles in India

This study reports a comparative analysis of life cycle greenhouse gas (GHG, CO2-eq.) emissions of three fuel-type vehicles, viz. internal combustion engine (ICEVs), battery electric (BEVs), and hybrid electric vehicles (HEVs), from an Indian perspective. Vehicles in hatchback and compact sports utility vehicles (SUVs) categories were analyzed. GHG emissions in fuel life cycle (well-to-tank and tank-to-wheel) and vehicle life cycle were estimated using data from websites of OEMs and ministries of Government of India, and the GREET software. The vehicle life cycle constitutes a smaller fraction (15–20 %) of total life cycle GHG emissions for ICEVs and HEVs compared to BEVs (∼35 %). The vehicle cycle emissions of BEVs are higher than ICEV and HEV in both categories (52–63 % for hatchback and approx. 45 % for compact SUV). The fuel cycle emissions of BEVs are smaller than ICEV and HEV in both categories (29–34 % for hatchback and 32–40 % for compact SUV). The total life cycle GHG emissions of BEVs were lower than ICEV and HEV (15–17 % in hatchback and 17–25 % in compact SUV category) for the 2023 energy mix in India with a significant share of thermal route. With anticipated doubling of renewable energy sources in India by 2030, the total life cycle GHG emission of BEV will decrease by approximately 20 %. However, sensitivity analysis revealed that BEVs produce higher GHG emissions than ICEVs and HEVs until a minimum lifetime travel distance. Thermal power plant efficiency and battery lifespan also influence BEVs lifetime GHG emissions.

Grouping and scheduling multiple sports leagues: an integrated approach

This paper introduces the multi-league grouping and scheduling problem, which integrates the grouping of teams into leagues and the scheduling of each league. This involves two possibly conflicting objectives: minimizing travel distance and minimizing capacity violations of venues shared by teams. We formulate this problem as a bi-objective mixed-integer programming model. Given the NP-hardness of the grouping problem, the integrated problem is particularly challenging. Hence, we design a two-layer constructive heuristic to efficiently approximate the Pareto set, using simulated annealing on the outer layer and an integer programming model on the inner layer. We further develop a speed-up version where the inner layer is solved heuristically. We develop a series of large-scale problem instances, including one based on data from the Royal Belgian Football Association. In a computational study, we compare our algorithms with an epsilon-constraint method and evaluate their results using various multi-objective solution quality metrics.

Allocation of charging stations in Kota city using data analysis/machine learning

ABSTRACT This paper proposes the allocation of charging stations of optimal capacity at appropriate locations within Kota city in Rajasthan state of India. The location of charging stations has been identified by considering various parameters including population density, family income, distribution network performance, number of electric vehicles and transformer capacity. The proposed approach of allocation of charging stations has been modeled mathematically and also by machine learning algorithms such as data mining, clustering with all the possible scenarios and constraints. In identifying suitable places for positioning charging stations in the city by observing different parameters and aspects, i.e. geo-statistical approaches, minimum traveling time and travel distance for charging stations has been included in the proposed approach. The proposed approach has been tested in MATLAB/Simulink environment.

Parking recommendation with meta-heuristic algorithms

Due to the exponential growth of cars in urban areas, parking problems have become a significant concern. Addressing this issue requires efficient methods for locating available parking spaces, enhancing the overall experience for drivers. This paper introduces a parking lot recommendation model leveraging meta-heuristic algorithms to generate a list of potential parking locations based on the users travel destinations. The primary objectives of these algorithms include minimizing travel distance, reducing total parking fees, and selecting parking lots with ample available spaces. The proposed model incorporates bio-inspired algorithms, including simulated annealing, genetic algorithms, and their adaptive variants. Our evaluation compares the performance of these algorithms, highlighting the adaptive simulated annealings superior quality of solutions and robustness against local minima. However, it is important to note that this approach comes with a trade-off, requiring longer execution times. In summary, this research contributes a novel parking lot recommendation model that effectively addresses the challenges posed by urban parking. The performance evaluation underscores the efficacy of the adaptive simulated annealing approach, showcasing its potential for practical implementation despite its relatively longer execution time.

OPTIMIZING UNMANNED AERIAL VEHICLE BASED FOOD DELIVERY THROUGH VEHICLE ROUTING PROBLEM: A COMPARATIVE ANALYSIS OF THREE DELIVERY SYSTEMS.

In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.

Sustainable waste collection optimization using electric vehicles

Solid waste management is a crucial municipal service directly influencing urban livability. Optimizing refuse collection routes enhances service efficiency and reduces costs. The recent introduction of fully electric refuse trucks, boasting features like zero emissions, low noise, and lower operating costs compared to diesel trucks, holds promise. However, their limited autonomy presents challenges that need addressing before widespread deployment. This study proposes a realistic formulation for the refuse collection routing problem with electric trucks, introducing electromobility constraints never considered before in related works. The goal is to find optimal routes covering all collection points while minimizing travel distance within vehicle battery and capacity limits. An accurate energy consumption model is presented, accounting for route elevation, vehicle speed, waste collection operations, and regenerative braking. Genetic algorithms are used to find solutions to the problem, as well as the well-known Clarke and Wright heuristic for comparison purposes. The algorithms are tested on six diverse instances reflecting real-world scenarios in New York City (United States) and Puerto Real (Spain). Results indicate the genetic algorithm’s superiority, yielding solutions with reduced travel distance and consumption while requiring fewer routes compared to the state-of-the-art heuristic.

Over 300 km Dispersion of Wild Boar during Hot Summer, from Central Poland to Ukraine.

The movement of wild boars is a complex process influenced by both internal conditions and external factors. Despite their typically sedentary lifestyle, dispersion constitutes an integral element of the wild boar's behavior. This report documents the longest observed wild boar dispersal, involving a collared two-year-old male near Warsaw, Poland. The aim of this study was to present the characteristics of movement during the "nomadic phase", drawing comparisons with the "sedentary phase". The other aim was to evaluate the influence of meteorological factors on the minimum daily travel distance of the wild boar. We collected data from two-year-old males. The first exhibited long-distance dispersal and the second only demonstrated local movements. We calculated the minimum daily distance of both wild boars based on collar locations and calculated basic statistics of movement. We used a generalized linear model with a gamma distribution and log link function to assess the potential impact of weather conditions on the minimum daily distance of wild boars. We tested maximum daily temperature, average daily temperature, and the sum of daily precipitation. The wild boar during a "nomadic phase" covered a total of 922 km with a mean minimum daily movement of 6 km. The dispersion distance was 307 km. The highest value of the minimum daily distance reached 31.8 km/day. The second wild boar (near Warsaw) covered a mean minimum daily distance of 1.4 km; the highest value of the minimum daily distance was 3.9 km. Both wild boars exhibited no dependence of minimum daily distance on weather conditions. However, when intensive and non-intensive dispersion were analyzed separately, it was demonstrated that the maximum daily temperature positively influenced the minimum daily distance. We speculate that the wild boar was forced to search for water sources after dark on hot days, which induced a longer traveling distance in an unfamiliar environment. This study highlights the significant spatial capabilities of wild boar in the transmission of genes or pathogens. We speculate that extended daily distances during the initial "nomadic phase" might suggest a panicked escape from a perceived threat. It is plausible that the wild boar found improved shelter within tall cereal crops in July and August, which resulted in lower daily distances.

Модель региональной транспортной сети для построения рациональных мультимодальных маршрутов пассажирских перевозок

Purpose: Adaptation of methods of constructing models of transport networks for the development of rational multimodal routes of regional passenger transportation, as well as the creation of a software product for the practical implementation of the proposed algorithms. Methods: Mathematical modeling; graph theory for solving regional transportation problems with construction of two-level oriented weighted multigraphs; Analysis of existing algorithms for optimizing the search for graph solutions; Dijkstra’s algorithm. Results: The model of the regional passenger transport network has been implemented in the form of a calculation program developed by the authors. In this program, the schedule of the movement of all transport points under consideration is the initial data, and the result is the construction of rational multimodal routes. The calculation program is compiled from several modules that implement the following functionality in general: reading input data from files; the formation of an array of connections for the problems under consideration; search for a rational solution and output of the received data while saving it to the file. The optimization criterion when choosing a route can be the minimum travel time, distance, or cost of the trip, depending on the task and input data provided. The resulting array of data with the results has been further analyzed using the MS Excel table editor. This approach is selected due to sufficient flexibility when performing a variety of data analysis and the convenience of graphic presentation of the results. Practical significance: The development of rational multimodal routes, relevant primarily for cases of abrupt changes in transport relations, in which the extrapolation methods of existing passenger flow do not provide reliable forecast data. Justification of the adjustment of the schedules of regional passenger routes based on the results of the analysis of the demand and the load of their specific segments.

Simultaneous allocation and sequencing of orders for robotic mobile fulfillment system using reinforcement learning algorithm

Robotic Mobile Fulfillment Systems (RMFS) can benefit large e-commerce warehouse operations significantly. To fulfill the orders received, RMFS deploys mobile robots to carry shelves back and forth from the storage area to the picking station. Order allocation and sequencing for mobile robots is a complex yet critical task as it influences the distance traveled by mobile robots in fulfilling the orders, i.e., appropriately allocating and sequencing the orders. In this paper, a Simultaneous Allocation and Sequencing of Orders Reinforcement Learning (SASORL) algorithm is proposed to minimize the distance traveled by mobile robots. Unlike existing methods, the SASORL algorithm optimizes order allocation and sequencing concurrently, significantly reducing mobile robot travel distance. The proposed SASORL algorithm encompasses three sets, namely state, action, and reward/penalty. The state set comprises the orders fulfilled, whereas the action set contains the orders yet to be fulfilled. The distance traveled by the mobile robot as a result of the orders allocated and sequenced is taken as the penalty for the proposed SASORL algorithm. As the proposed SASORL algorithm simultaneously allocates and sequences the orders to the mobile robots, the action set depletes, the state set enlarges, and the penalty updates until the action set becomes null. Each episode restarts with the learned experience of the prior episodes, and after completing a few episodes, the SASORL algorithm is capable of generating an optimal order allocation and sequence that commits the minimum travel distance to the mobile robots. SASORL algorithm is superior to widely adopted soft computing techniques when orders are randomly distributed. This superiority is evidenced by a 26% reduction in the maximum distance traveled by all mobile robots, a 54% reduction in the standard deviation of the distance traveled by the mobile robots, and a marginal increase of 7% in the total distance traveled by all mobile robots. Moreover, the SASORL algorithm outperforms soft computing techniques by 44% in terms of computation time.

PKGS: A Privacy-Preserving Hitchhiking Task Assignment Scheme for Spatial Crowdsourcing

Privacy-preserving task assignment is vital to assign a task to appropriate workers and protect workers’ privacy or task privacy for spatial crowdsourcing (SC). Existing solutions usually require each worker to travel to the task location on purpose to perform this task, which fails to consider that workers have specific trajectories and carry out the task on their way in a hitchhiking manner. To this end, this paper proposes a privacy-preserving hitchhiking task assignment scheme for SC, named PKGS. Specifically, we formulate the privacy-preserving hitchhiking task assignment as a decision problem of the relationship between dot and line under privacy protection. In particular, we present a privacy-preserving travel distance calculation protocol and a privacy-preserving comparison protocol through the Paillier cryptosystem and the SC framework. Results of theoretical analysis and experimental evaluation show that PKGS can not only protect the location privacy of both each worker and the task simultaneously but also assign the task to the worker holding a minimum travel distance. In contrast to prior solutions, PKGS outperforms in the computation of travel distance and task assignment.

MDN-Enabled SO for Vehicle Proactive Guidance in Ride-Hailing Systems: Minimizing Travel Distance and Wait Time

MDN-Enabled SO for Vehicle Proactive Guidance in Ride-Hailing Systems: Minimizing Travel Distance and Wait Time

Multi Depot Vehicle Routing Problem and Geographical Information System Integration: Retail Stores Case Study

The improper delivery route planning of multi depot and delivery assignment model leads the logistic operation in high travel distance and high distribution cost. This paper aim to explain the integration of multi depot vehicle routing problem (MDVRP) and Geographical Information System (GIS) to obtain the optimum delivery route planning with minimum travel distance by using multi-objective mixed integer programming approach and visualize its delivery assignment with open-source public GIS integration. In this research, the coordinate location of the depots and the retail stores are obtained, process the raw data through mathematical programming, then integrate the delivery plan to GIS for vehicle routing visualization. To validate the proposed model, the primary data of a retail stores chain was used and the model was evaluated with sensitivity analysis, resulted optimum value of total travel distance in daily logistic operation.

골드심을 이용한 해수담수화 이동형 선박의 급수 모델 적용성 평가

Because of climate change, damage to coastal and island regions due to abnormal weather patterns such as drought and typhoons has become a more severe threat. In particular, there is a high risk of water shortages on islands that import water from the mainland by ship because of a lack of a water supply system and groundwater. In addition, ship-based water transport systems have not been well-planned; thus, they involve many simple round-trip operations between the mainland and individual islands, which raises costs. In this study, we attempted to reduce the operating costs of water supply shipments by analyzing water usage on islands and planning a new method of operation. To achieve this, the water supply system is modeled using the GoldSim program, widely employed for water resource and management analysis. The applicability of a shipboard seawater desalination plant was also evaluated. Three different scenario models were analyzed for the applying shipboard seawater desalination plant to island regions. When calculating the minimum travel distance to ensure a stable water supply, it was found that using a shipboard seawater desalination plant minimized the operating space. Because the traveling length of a ship is directly related to costs, the results of this analysis are expected to have economic benefits for water supply systems.

Optimization of Blood Bag Distribution Routes Using AMPL Software and Nearest Neighbor Algorithm (Case Study of the Indonesian Red Cross Jakarta)

The process of distributing blood bags by the Indonesian Red Cross (PMI) DKI Jakarta uses route selection preferences by ambulance drivers. Basically, this routing problem can impact other aspects such as additional costs, distribution time, fuel use, carbon emissions, and others, so this research needs to propose the best route to minimize travel distance. There are several Hospital Blood Banks (BDRS) in Jakarta that do not receive blood bags at the right time. As an organization authorized to provide blood bag supply, the PMI must distribute blood bag using the 7R concept (Right Time, Right Place, Right Quantity, Right Quality, Right Cost, Right Condition, and Right People). The PMI also has to consider that blood bags are classified as perishable items that need to require fast and precise handling. Therefore, it is necessary to optimize the blood distribution by minimizing travel distance. The optimization model used is the Vehicle Routing Problem (VRP) with AMPL software comparing with the Nearest Neighbor (NN) algorithm. After 4 hours of running the trial, this research has revealed that AMPL software gives the best combination of travel distance than the Nearest Neighbor algorithm, with the differences being approximately 11.52 km.

Mechatronic System Integrating an Electromagnetic Actuator with the Possibility of Moving the Working Microscopic Tool at Submicrometric Resolutions

Abstract The possibility to work by using small objects, under the microscopic view, it is a challenge. The force’s spectrum differs from that of the macroscopic scale, and many unexpected force vectors will appear. A sharp tip must be the ideal pressure vector on different microscopic small object to push, roll, and brake and so on. The positioning of this tip must be a very precise one, comparatively with the working area dimensions and we consider that 10 microns can be a reasonable measuring unit for these moves that are executed into a specific way in these kinds of cases. The mechatronic system designed to actuate the tip, becomes this paper subject. Indisputable we will talk about an actuator with one or more than one freedom degrees. Mechanical aspects are secondary, but necessary to understand the impact of electrical parameters on the minimum possible unit of movement. Electronic control refers to a switching and PWM signal, through a software that develops both the effective part of generating the control signal and the HMI interface through a touch-screen. The aim of this paper was to determine the minimum micrometric travel distance that can be obtained with a 12-bit PWM signal generated by a Raspberry mini-computer

A Mathematical Model for the Routing Optimization Problem with Time Window

Abstract This paper establishes a mathematical model for the routing optimization problem. Then, the model is solved by a genetic algorithm. Finally, to verify the proposed method, a case study is carried out. According to the results, the proposed model and algorithm can be used to solve the problem and obtain the optimal vehicle route with minimum time cost and travel distance.

California Joins Six States in Setting Standards for MH/SUD Appointment Wait Times

California Joins Six States in Setting Standards for MH/SUD Appointment Wait Times

Planning Method for Urban Subway Park-and-Ride Station and Parking Berth

For satisfying the growing demand of parking in urban area and relieving traffic congestion in downtown, under the condition of urban subway park-and-ride (P&R), the methods of site selection and scale calculation of parking lots are studied. Selecting Xi'an Rd. business circle in Dalian as study object, both survey and analysis of parking demand were conducted. Based on the principle of random equilibrium selection, the logit model of P&R demand was constructed. Then the forecasting models of parking demand both in downtown and peripheral zones were proposed respectively. Using the minimum travel distance of vehicles as the objective function, site selection optimization model of P&R station was constructed. The calculation methods of scale for parking lots in both downtown and P&R station were also processed. For the case of Xi'an Rd. business circle, not only the site of P&R station was planned, but the scale of parking berth was calculated. According to the plan and calculation, the total number of parking berth has increased from 3380 to 4011. Among the planned berths, 75.7 % are in the business circle and 24.3 % in P&R stations are outside downtown. Thus, the number of planned berths in the business circle has actually decreased by about 8 %. The research indicates that, for downtown with subway system, by reducing parking berths in downtown and increasing them in P&R stations outside the urban area, this planning method is more beneficial to relieve the problems of parking and traffic congestion in downtown than traditional method.

Flower Pollination Algorithm for Vehicle Routing Problem with Time Windows (VRPTW)

Vehicle Routing Problem with Time Windows (VRPTW) is a vehicle route deciding problem that is used in order to serve customer who involved more than one vehicle with a limited time, so as a minimum distance route is obtained without disobeying vehicle capacity cargo restriction and time range. Flower Pollination Algorithm (FPA) is an algorithm which inspires from nature and that is flower pollination process toward a plant. Within an FPA, there are two main steps to use, they are global flower pollination and local flower pollination. Those two steps are determined by using switch probability parameter. This program is made in Java language program to apply FPA in solving VRPTW which is implemented in three example cases, they are small-scale datum with 25 customers, medium-scale datum with 50 customers, and big-scale datum with 100 customers. According to the results, it can be concluded that the larger number of flowers and iterations can affect the number of total minimum travel distance become smaller. Furthermore, a better total minimum travel distances also will be obtained if the value of switch probability parameter is larger.

New intergeneric orchid hybrid found in Romania × Pseudorhiza nieschalkii (Senghas) P.F.Hunt nothosubsp. siculorum H.Kertész & N.Anghelescu, 2020.

We describe the first reported intergeneric, which naturally occurs between two subspecies belonging to different genera, Dactylorhiza fuchsii subsp. sooana (genus Dactylorhiza) and Pseudorchis albida subsp. tricuspis (genus Pseudorchis), as × Pseudorhiza nieschalkii (Senghas) P.F.Hunt nothosubsp. siculorum H.Kertész & N.Anghelescu, 2020. The hybrid was found and digitally photographed for the first time by Hajnalka Kertész in June, 2020, within Terra Siculorum, in one of the Natura 2000 protected areas, known as Harghita Mădăraș, ROSCI00090. Following detailed morphometric analysis using 67 characters and molecular karyological analyses, we identified this unique specimen as an intergeneric hybrid, new to science. The hybrid, an F1 generation plant, most likely representing a single intergeneric pollination event, is phenotypically intermediate between its parental species in most of the characters scored, but it significantly closely resembles Pseudorchis albida subsp. tricuspis parent. Since several individuals of the parental species occurred in near proximity, within 1–10 meters distance, we suggest that the production of this hybrid required a minimum travel distance of ca 1–10 meters, by the pollinators and frequent exchange of pollen between the parental species was very likely. The parental species and the hybrid, which display a considerable synchronicity in their flowering time, overlap in the pollinator community, sharing various species of Hymenopterans and Dipterans, very abundant in the heathland. This Terra Siculorum hybrid is thus best described as a rarely occurring intergeneric hybrid that shows strong Pseudorchis albida subsp. tricuspis parental dominance in inheritance patterns.