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The traditional methods of transporting goods and people in urban areas using vehicles powered by internal combustion engines are major contributors to pollution. As a result, an increasing number of logistics companies are transitioning to electric vehicles (EVs) for daily operations, replacing traditional engines. This shift opens research avenues regarding the integration of EVs into delivery workflows and how this can contribute to greener cities. This study tackles the EV routing problem, focusing on balancing battery constraints and optimizing routes. We formulated the problem as a pickup and delivery with time windows, incorporating electric energy consumption constraints, and utilized consensus mechanisms in an agent-based simulation context. Our evaluation used 15 scenarios, capturing variations in vehicle configurations, order generation rates, and battery and freight capacities. We compared two order allocation strategies: "Closest Allocation" and "Negotiation" consensus-based allocation. The results confirmed that the consensus-based strategy outperformed the "Closest Allocation" in metrics such as remaining orders, orders not handled in time, total distance traveled, total recharging cost, and total number of recharges. These findings have significant implications for urban planners, logistic companies, and policymakers, demonstrating that an agent-based simulation context for electric vehicles using consensus-based strategies can enhance delivery efficiency and promote sustainability. [ABSTRACT FROM AUTHOR]

This paper explores the significant impact of e-commerce and investigates manufacturers' decision-making processes regarding the opening of e-shops and the adoption of online and offline coordination strategies to enhance profits in an e-commerce environment. A novel three-channel model with asymmetric retailers is proposed, and three scenarios are analyzed: the initial dual-channel scenario, the multi-channel scenario with e-platform, and the "Order online, Pickup offline" coordinated scenario. By comparing optimal profits across these scenarios, decision-making inequalities for manufacturers are derived. Subsequently, numerical experiments validate that manufacturers, aiming to maximize profits, opt to establish an e-commerce channel and coordinate with offline retailers. Additionally, the study emphasizes the importance of e-platforms in setting commission rates to drive increased profits. The findings offer practical implications for manufacturers to engage in interest-balanced coordination, enabling them to adapt to evolving retail dynamics in the advancing e-commerce landscape from both theoretical and practical perspectives. [ABSTRACT FROM AUTHOR]

The traditional methods of transporting goods and people in urban areas using vehicles powered by internal combustion engines are major contributors to pollution. As a result, an increasing number of logistics companies are transitioning to electric vehicles (EVs) for daily operations, replacing traditional engines. This shift opens research avenues regarding the integration of EVs into delivery workflows and how this can contribute to greener cities. This study tackles the EV routing problem, focusing on balancing battery constraints and optimizing routes. We formulated the problem as a pickup and delivery with time windows, incorporating electric energy consumption constraints, and utilized consensus mechanisms in an agent-based simulation context. Our evaluation used 15 scenarios, capturing variations in vehicle configurations, order generation rates, and battery and freight capacities. We compared two order allocation strategies: “Closest Allocation” and “Negotiation” consensus-based allocation. The results confirmed that the consensus-based strategy outperformed the “Closest Allocation” in metrics such as remaining orders, orders not handled in time, total distance traveled, total recharging cost, and total number of recharges. These findings have significant implications for urban planners, logistic companies, and policymakers, demonstrating that an agent-based simulation context for electric vehicles using consensus-based strategies can enhance delivery efficiency and promote sustainability.

In the new "gig" economy, a user plays the role of a consumer as well as a service provider. As a service provider, drivers travelling from a source to a destination may opportunistically pickup and drop-off packages along the way if that does not add significantly to their trip distance or time. This gives rise to a new business offering called Package Delivery as a Service (PDaaS) that brokers package pickups and deliveries at one end and connects them to drivers on the other end, thus creating an ecosystem of supply and demand. The dramatic cost savings of such a service model come from the fact that the driver is already en-route to their destination and the package delivery adds a small overhead to an already pre-planned trip. From a technical perspective, this problem introduces new technical challenges that are uncommon in the literature. The driver may want to optimise for distance or time. Furthermore, new packages arrive for delivery all the time and are assigned to various drivers continuously. This means that the algorithm has to work in an environment that is dynamic, thereby precluding most standard road network precomputation efforts. Furthermore, the number of packages that are available for delivery could be in the hundreds of thousands, which has to be quickly pruned down for the algorithm to scale. The paper proposes a variation called dual Dijkstra's that combines a forward and a backward scan in order to find delivery options that satisfy the constraints specified by the driver. The new dual heuristic improves the standard single Dijkstra's approach by narrowing down the search space, thus resulting in significant speed-ups over the standard algorithms. Furthermore, a combination of dual Dijkstra's with a heuristic landmark approach results in a dramatic speed-up compared to the baseline algorithms. Experimental results show that the proposed approach can offer drivers a choice of packages to deliver under specified constraints of time or distance, and providing sub-second response time despite the complexity of the problem involved. As the number of packages in the system increases, the matchmaking process becomes easier resulting in faster response times. The scalability of the PDaaS infrastructure is demonstrated using extensive experimental results. [ABSTRACT FROM AUTHOR]

A set of scientific instruments capable of high temporal resolution measurements onboard the Mars Atmosphere and Volatile Evolution (MAVEN) spacecraft made it possible to study the structure and properties of the dayside Martian magnetosphere. The Mars plasma envelope on the dayside of the planet was discovered based on observations on the first orbiters of Mars (Vaisberg et al., 1976; Gringauz et al., 1976). The high temporal and mass resolution of MAVEN instruments made it possible to study the structure and processes in the plasma of the dayside magnetosphere, the plasma layer that exists between the solar wind heated by the shock wave and the ionosphere (Vaisberg et al., 2017). It is shown that, under different external conditions, two different types of plasma layers are formed between the ionosphere and magnetosheath on the dayside of the planet: (1) mixture of heated ionospheric ions and picked-up exospheric ions and (2) a layer of ionospheric ions accelerated by the solar wind motional electric field called "plume" (Dong et al., 2015). The first type of the Martian daytime magnetosphere is formed as a result of the interaction of oxygen exospheric ions captured and accelerated by the solar wind with the upper part of the Martian ionosphere (Vaisberg and Shuvalov, 2021). The second type of the Martian dayside magnetosphere is formed by the acceleration of an ion beam from the outer ionosphere, which then passes into a more energetic plume beam in the magnetosheath. This article discusses typical plasma populations, their properties and conditions that lead to the formation of the daytime magnetosphere from the material of the upper ionosphere. [ABSTRACT FROM AUTHOR]

Low operation efficiency and poor working performance are the main reasons that restrict the application of pneumatic date pickers. In this study, it is proposed to use positive pressure air flow blowing and negative pressure air flow suction to pick jujube fruit and to use inertia air flow to remove impurities and design a positive and negative collaborative air flow type date fruit harvester (P-N JH). The second-order regression model of test factors and response indexes was established by using the central composite design method, and the operating performance of P-N JH was evaluated and comprehensively optimized, and the optimal combination of operating parameters was obtained: positive pressure wind speed, negative pressure wind speed, and travel speed were 16.8 m·s−1, 34.0 m·s−1, and 1.5 m·s−1, respectively. In addition, the verification test results show that the pickup rate, impurity rate, damage rate, and operating efficiency are 97.21%, 2.15%, 1.08%, and 2170 kg/h, respectively. The operating performance of P-N JH not only meets the requirements of jujube picking but also significantly improves the operating efficiency compared with the traditional air-suction jujube harvester. This study can provide theoretical and technical reference for the harvesting of air-suctioned dates; it also provides a new way of thinking for jujube fruit picking. [ABSTRACT FROM AUTHOR]

The online pickup and delivery problem is motivated by the takeaway order delivery on crowdsourcing delivery platform, which is a newly emerged online to offline business model based on sharing economy. Considering the features of crowdsourcing delivery, an online pickup and delivery problem with constrained capacity is proposed, whose objective is to route a delivery man with constrained capacity to serve requests released over time so as to minimize the total latency. We consider online point-to-point requests with single pickup location where each request has to be picked up at the single pickup location and delivered to its destination, and each request become available at its release time, which is not known in advance. The lower bound of this problem for various capacities is proved. Two online algorithms WR and WI are presented, the competitive ratios on a half line and on general metric space are proved respectively. Further, a computational study is conducted to compare the performance of these two online algorithms on random instances of general metric space. The result shows algorithm WR performs better than WI in random cases but not in the worst case. [ABSTRACT FROM AUTHOR]

This paper proposes a control method for the multi-agent pickup and delivery problem (MAPD problem) by extending the priority inheritance with backtracking (PIBT) method to make it applicable to more general environments. PIBT is an effective algorithm that introduces a priority to each agent, and at each timestep, the agents, in descending order of priority, decide their next neighboring locations in the next timestep through communications only with the local agents. Unfortunately, PIBT is only applicable to environments that are modeled as a bi-connected area, and if it contains dead-ends, such as tree-shaped paths, PIBT may cause deadlocks. However, in the real-world environment, there are many dead-end paths to locations such as the shelves where materials are stored as well as loading/unloading locations to transportation trucks. Our proposed method enables MAPD tasks to be performed in environments with some tree-shaped paths without deadlock while preserving the PIBT feature; it does this by allowing the agents to have temporary priorities and restricting agents' movements in the trees. First, we demonstrate that agents can always reach their delivery location without deadlock. Our experiments indicate that the proposed method is very efficient, even in environments where PIBT is not applicable, by comparing them with those obtained using the well-known token passing method as a baseline. [ABSTRACT FROM AUTHOR]

In order to increase customer satisfaction and maintain customer loyalty, logistics service providers must pay attention to the quality of service provided, one of which is effecive warehouse management, especially in scheduling the arrival and departure of products transporting vehicles. Therefore, this study discusses warehouse management in form of delivery and pickup scheduling at PT XYZ’s cross-docking warehouse. This study aims to obtain effective delivery and pickup scheduling and minimize operational costs. The Cross-docking Distribution Problem is an np-hard problem, so the Particle Swarm Optimization algorithm is used, which is a metaheuristic method in finding solutions. Based on the result, it was found that effective delivery and pickup scheduling was able to save inventory cost by 3.12% and reduce the percentage of delays from 73% to 0%. The scheduling process using Particle Swarm Optimization requires an average computation time of 26.2 seconds.

There is a need to innovate current solutions in the field of fleet management and control systems in connection with the continuous growth of retail sales via the Internet and the associated delivery services. This is also supported by the development of the Logistics 4.0 concept, which refers to the massive digital transformation and deployment of cyber-physical systems based on artificial intelligence. For example, thanks to this delivery companies can reduce their operating costs, maintain competitiveness and margin size. The aim of this article is to present the basic principles on which the newly designed functionality of route and travel times optimization is based. This functionality is intended as part of intelligent transportation systems supporting delivery and utility services. The contribution of this article refers mainly to the field of practice. The article provides professionals with insight into the stated issue. Exemplary calculations of financial savings achievable by implementing such a solution in practice are also given.

Focusing on ring-shaped ion velocity distributions with a finite width formed in magnetic reconnection in the presence of a guide magnetic field, intriguing properties such as the formation mechanism, a significant change in the shape, and necessary conditions for the change are investigated by means of theory and simulations. The width of a ring velocity distribution predominantly originates from velocity variations of seed particles for the pickup-like process. A function exactly representing a ring with a width is analytically formulated, assuming a steady supply of seed particles satisfying a Maxwellian velocity distribution and a mixing of gyration phases. The formulated function indicates that when the ring width is larger than a criterion, the local minimum of the ring’s center is changed into the maximum, and the shape is transformed into a mountain shape. Such a mountain-like distribution is defined as “a pseudo-Maxwellian distribution,” because it is almost indistinguishable in shape from a genuine Maxwellian distribution. Actually, particle simulations demonstrate that mountain-shaped ion velocity distributions are formed during magnetic reconnection with a guide magnetic field, and it is nearly concluded that they are pseudo-Maxwellian distributions. Moreover, two types of evidence for pseudo-Maxwellian distributions are shown by simulations. One is to analyze the dependence of the distribution shape on the guide magnetic field, which is explored by the particle simulation. In cases of slightly different values of the guide field, vague shapes of rings with a width are observed as ion velocity distributions. The other is to observe velocity distributions under a hypothetical condition of an artificial zero temperature in the upstream by utilizing a test particle simulation. In the test particle simulation, ring-shaped distributions with a width are clearly seen, because the velocity variations in the upstream are reduced. From the two types of evidence, it is definitely confirmed that the mountain-shaped distributions found in the particle simulations are pseudo-Maxwellian distribution. These results imply that pseudo-Maxwellian distributions would be created for various cases of guide field magnetic reconnection.

By establishing two solar‐wind convection electric field‐associated coordinate systems, the magnetic and plasma properties in the lunar wake are examined. In the plane perpendicular to the interplanetary magnetic field, all the properties exhibit asymmetric distributions related to the electric field, and they are even periodically modulated every two lunar radii but only along the flank which the electric field points to. Evidences show that the solar‐wind ions, reflected over the dayside lunar magnetic anomalies, have produced these asymmetries and periodical modulations. These ions, undergoing both the electric drift and large‐radius gyration, bypass the Moon body and periodically penetrate into the wake flank, during which they exchange momentum with the refilling solar‐wind plasma, thereby resulting in the oscillations of the plasma and the periodical modulations of the wake. Plain Language Summary: The lunar wake, produced by the solar‐wind plasma absorption on the dayside lunar surface, has been expected to be symmetric about the plane of the solar‐wind velocity and the interplanetary magnetic field. The solar‐wind convection electric field, which is perpendicular to that plane, has been found to be able to make asymmetric magnetic perturbations but only ahead of the lunar terminator, its influences to the magnetic field in the downstream wake, however, have not been noticed, yet. In this study, the reflected solar‐wind ions from the dayside lunar surface, biased and accelerated by the solar‐wind electric field, are found to be able to bypass the Moon, periodically penetrate into the wake flank which the electric field points to, and modulate the refilling plasma in the direction perpendicular to the magnetic field. The periodical modulations, thereby, produce an asymmetric and meandering wake as observed. Key Points: The central lunar wake meanders in phase with the gyromotions of the reflected ions in the solar‐windThe reflected solar‐wind ions periodically modulate the wake refilling plasma, thereby producing the meandering wakeThe magnetic field and the plasma properties in the wake are all modulated along with the meandering structure [ABSTRACT FROM AUTHOR]

This paper studies a novel sustainable vessel routing problem modeling considering the multi-compartment, split pickup and split delivery, and time windows concepts. In the presented problem, oil tankers transport crude oil from supply ports to demand ports around the globe. The objective is to find ship routes, as well as port arrival and departure times, in a way that minimizes transportation costs. As a second objective, we considered the sustainability aspect by minimizing the vessel energy efficiency operational indicator. Multiple products are transported by a heterogeneous fleet of tankers. Small realistic test instances are solved with the exact method. [ABSTRACT FROM AUTHOR]

In order to solve the problem of poor working performance due to incidental picking-up loss of a spring-tooth drum pickup, the coupling analysis approach of Automated Dynamic Analysis of Mechanical Systems (ADAMS) and Discrete Element Method (DEM) was proposed to reveal its motion characteristics. The coupled ADAMS-DEM method was used to construct the pickup simulation model with rice straw as the object. The validation tests were carried out in the field with different operating forward speeds and straw coverages. The results showed that when the baler worked with a rake, the straw loss rate tended to rise with the increase of the operating speed and the straw coverage. Tests results indicated periodic peaks and valleys of the forces between the rollers and the track chute, which could increase the wear of the track chute. In addition, the movement and variation of the straw and the distribution of different straw lengths after picking up were analyzed. The research results can provide a reference for the design and optimization of a pickup in the later period. [ABSTRACT FROM AUTHOR]

In oil seismic exploration, automatic picking of first arrivals is very important but difficult for seismic data with low signal-to-noise ratios. In the paper, we proposed a new method based on mutual information in information theory. Mutual information between signals and noises is zero theoretically; thus, random noises have fewer effects on first arrivals pickup with the criterion of mutual information. The paper presents a detailed description of the method based on mutual information. Tests and verification with simulation data and field measurements are carried out. When compared with other well-known methods, the first break picking accuracy and efficiency of the proposed algorithm are verified. [ABSTRACT FROM AUTHOR]

In this paper, we present a device that is capable of wireless synchronization to the MRI pulse sequence time frame with sub-microsecond precision. This is achieved by detecting radio frequency pulses in the parent pulse sequence using a small resonant circuit. The device incorporates a 3-axis pickup coil, constructed using conventional printed circuit board (PCB) manufacturing techniques, to measure the rate of change of the gradient waveforms with respect to time. Using Maxwell’s equations, assuming negligible rates of change of curl and divergence, a model of the expected gradient derivative (slew) vector field is presented. A 3-axis Hall effect magnetometer allows for the measurement of the direction of the static magnetic field in the device co-ordinate frame. By combining the magnetometer measurement with the pickup coil voltages and slew vector field model, the orientation and position can be determined to within a precision of 0.1 degrees and 0.1 mm, respectively, using a pulse series lasting $880~\mu \textsf {s}$. The gradient pulses are designed to be sinusoidal, enabling the detection of a phase shift between the time frame of the pickup coil digitization circuit and the gradient amplifiers. The signal processing is performed by a low power micro-controller on the device and the results are transmitted out of the scanner bore using a low latency 2.4 GHz radio link. The device identified an unexpected 40 kHz oscillation relating to the pulse width modulation frequency of the gradient amplifiers that is predominantly in the direction of the static magnetic field. The proposed wireless radio frequency triggered acquisition device enables users to probe the scanner gradient slew vector field with minimal hardware set-up and shows promise for the future developments in the prospective motion correction. [ABSTRACT FROM AUTHOR]

With the "last mile" of the delivery process being the most expensive phase, autonomous package delivery systems are gaining traction as they aim for faster and cheaper delivery of goods to city, urban and rural destinations. This interest is further fueled by the emergence of e-commerce, where many applications can benefit from autonomous package delivery solutions. However, the environment stochasticity, variability and task complexity for autonomous operation make it difficult to deploy such systems in real-world applications without the incorporation of advanced machine learning and optimization algorithms. Moving away from designing a "one size fits all" agent to solve the outdoor package delivery problem and considering ad-hoc teams of agents trained within a data-driven framework could provide the answer. In this work, we propose a delivery scheduling algorithm for heterogeneous multi-agent systems using the pickup and delivery problem (PDP) formulation. Specifically, a 3-index mixed integer program-based PDP that allows coalition formation (PDP-CF) among agents is derived to allow multi-agent PDP schedules. We propose a quantum genetic algorithm to solve for the schedule since it is can better handle the large computational complexity of PDP-CF. Multiple PDP scenario simulations show the merits of the proposed approach. [ABSTRACT FROM AUTHOR]

In respond to the problem of insufficient capacity in peak hours and idle capacity in off-peak hours of O2O delivery, the logistics industry has been challenged to minimize the total costs of both B2C and O2O delivery. Considering the high coincidence in geographical space of B2C and O2O orders, an innovative joint distribution service is proposed. This paper designs a modified C-W algorithm based on the merging of node-inserted paths to achieve the optimization of pickup and delivery problem with time windows (PDPTW). A numerical analysis is conducted to compare the independent distribution mode with the innovative joint distribution mode. The results highlight that the innovative joint distribution mode performs better than the independent distribution mode in both different order sizes and different lengths of O2O time windows, which can improve the delivery efficiency and lower the total costs. [ABSTRACT FROM AUTHOR]