Station Assignment Research Articles

Real-Time Congestion-Aware Charging Station Assignment Model for EVs

Real-Time Congestion-Aware Charging Station Assignment Model for EVs

The integrated on-demand bus routing problem: Combining on-demand buses with a high-frequency fixed line public transport network

In this work we analyze the performance of integrating a large-scale on-demand bus system with a high-frequency fixed line public transport network in an urban context. Given are a high-speed metro network, a set of real-time requests, a set of bus station locations and a fleet of fixed capacity minibuses. Requests have a set of possible departure/arrival bus stations within walking distance of the actual departure/arrival location and have to be served within a certain time window. The aim is to simultaneously (1) decide on the trip type for each passenger (only bus, only metro or mixed), (2) route the on-demand buses, (3) in the case of a bus-leg in the trip, assign each passenger to a departure and arrival bus station (bus station assignment), and (4) in the case of a metro-leg in the trip, decide the assigned transfer station(s) and used metro lines (transfer station assignment). We call this problem the integrated on-demand bus routing problem. After presenting a mathematical model, we propose a quick and scalable insertion-based heuristic to solve the problem.The results found by the heuristic are further used to compare the performance of an integrated system, to a system that only uses on-demand buses. It is concluded that the integrated system always performs better regarding the service rate or number of served requests. Depending on the speed and layout of the metro network, also the average user ride time per passenger improves by the integration.

Maximum Throughput First Access Point Selection Scheme for Multi-Rate Software-Defined Wireless Network

As the number of devices increases, many businesses deploy multiple access points (APs) in neighboring areas to provide Internet service. In such networks, a mobile station (MS) often has multiple APs within its coverage area, and deciding which AP to associate with becomes a significant issue that can influence network performance. Software Defined Network (SDN) has recently become a popular technology for making intelligent mobile station assignment decisions for APs. In a multi-rate Wi-Fi network, the actual bandwidth of high-rate links can be reduced to that of low-rate links. In this paper, we propose a Maximum Throughput First (MTF)AP selection scheme, which is a refinement of existing Mininet Wi-Fi mechanisms. MTF considers the parameters of actual throughput achieved by the access point and the number of associated stations as selection metrics when making the association decision. The simulation results show that MTF provides better performance, especially in multi-rate environments.

The electric on-demand bus routing problem with partial charging and nonlinear function

The electric vehicle routing problems (EVRPs) with recharging policy consider the limited range of electric vehicles and thus include intermediate visits to charging stations (CSs). In general, minimizing the resultant charging costs such as charging duration or charging amount is also part of the objective of the EVRP. Accordingly, the EVRP has received considerable attention over the past years. Nevertheless, this type of problem in the domain of passenger transportation, a VRP variant, has been rarely studied in the literature, especially with time windows, a realistic nonlinear charging function or a partial charging policy. Hence this research extends the existing work on the EVRP to the on-demand bus routing problem (ODBRP) which transports passengers with the bus station assignment (BSA). The resultant problem is the electric ODBRP (EODBRP). Specifically, each passenger can have more than one stations to board or alight, and they are assigned to the ones with the smallest increase in the total user ride time (URT). In the EODBRP, frequent intermediate visits to CSs are considered. Moreover, a nonlinear charging function is used and the partial charging strategy is applied. To solve the EODBRP, a greedy insertion method with a ‘charging first, routing second’ strategy is developed, followed by a large neighborhood search (LNS) which consists of local search (LS) operators to further improve the solution quality. Experimental data are generated by a realistic instance generator based on a real city map, and the corresponding results show that the proposed heuristic algorithm performs well in solving the EODBRP. Finally, sensitivity analyses with divergent parameters such as the temporal distributions of passengers and bus ranges may provide practical guidance.

Prepositioning can improve the performance of a dynamic stochastic on-demand public bus system

This paper presents the first application of prepositioning in the context of the dynamic stochastic on-demand bus routing problem (DODBRP). The DODBRP is a large-scale dial-a-ride problem that involves bus station assignment and aims to minimize the total user ride time (URT) by simultaneously assigning passengers to alternative stations and determining optimal bus routes.In the DODBRP, transportation requests are introduced dynamically, and buses are dispatched to stations with known requests. This paper investigates the concept of prepositioning, which involves sending buses not only to currently known requests but also to requests that are likely to appear in the future, based on a given probability.To solve this dynamic and stochastic ODBRP, the paper proposes a heuristic algorithm based on variable neighborhood search (VNS). The algorithm considers multiple scenarios to represent different realizations of the stochastic requests.Experimental results demonstrate the superiority of the prepositioning approach over the DODBRP across various levels of forecast accuracy, lengths of time bucket, and probabilities of realization. Furthermore, the paper shows that removing empty stations as a recourse action can further enhance solution quality. Additionally, in situations with low prediction accuracy, increasing the number of scenarios can lead to improved solutions. Finally, a combination of prepositioning, empty station removal, and the insertion of dynamic requests proves to be effective.Overall, the findings of this paper provide valuable insights into the application of prepositioning in the dynamic stochastic on-demand bus routing problem, highlighting its potential for addressing real-world transportation challenges.

An efficient multi-agent approach to order picking and robot scheduling in a robotic mobile fulfillment system

This paper presents a Multi-Agent System (MAS) for optimizing the scheduling and routing of mobile robots and human pickers in a Robotic Mobile Fulfillment System (RMFS). The RMFS is a system designed for e-commerce warehousing where autonomous mobile robots are used to fetch inventory pods, also referred to as racks, from the storage area and transport them to the appropriate picking station where human pickers pick the required number of goods. The system requires the solution of several hard decision problems like: the order-to-picking station assignment and sequencing, the pod selection, and the multi-robot task allocation. The proposed solution approach employs decentralized scheduling mechanisms, i.e. auctions and dispatching rules, to communicate and distribute picking and retrieval tasks among the agents. Various dispatching rules are identified and analyzed over a wide set of problem instances of the RMFS with varying numbers of mobile robots, picking stations, and order sizes. The proposed MAS framework shows promising results and requires only a fraction of the computation time compared to a centralized scheduling algorithm. The MAS also includes both unidirectional and bidirectional lanes. Although additional complexity in collision avoidance is introduced when using bidirectional lanes, it allows for better system performance.

Customised bus route design with passenger-to-station assignment optimisation

As an emerging and innovative public transportation mode, the customised bus (CB) has drawn widespread attention. Existing studies of the CB mainly focus on issues regarding the bus station location, route design, timetabling, and fare setting, but rarely consider the passenger to bus station assignment problem which can significantly influence passengers’ benefit and the buses’ operating scheme. Thus, this study focuses on the customised bus routing problem with passenger-to-station assignment (CBRP-PSA) aiming to simultaneously minimise passengers’ CB service access cost and the bus route cost. A time-discretized multi-commodity network flow model is developed to jointly determine the CB routes, timetables, and passenger-to-station assignment by allowing split loads and mixed loads. Through the dualization, the developed model is decomposed into two solvable sub-problems, and a Lagrangian-based heuristic solution algorithm is proposed. The model and algorithm are implemented in illustrative, medium-scale, and large-scale transportation networks to demonstrate their effectiveness under different scenarios.

Dynamic Channel Assignment for Downlink and Uplink Decoupling in Wireless Networks

The demand for wireless communication capacity continues to increase with the extensive usage of smartphones, tablets, and devices that are related to the Internet of Things (IoT). However, devices and base stations are diversified, and base stations of various sizes are mixed. In existing cellular networks, the transmission powers of the base stations are different. If the downlink received power from the base station that belongs to the device is maximum, the uplink received power from the device at the base station is not always the maximum. This study maximizes the power that is received from the device through downlink-uplink decoupling (DUDe). DUDe can improve the spectral efficiency by selecting the downlink base station and the uplink base station independently in a network with base stations with different transmission powers. This study focuses on two technologies, DUDe and the dynamic channel assignment (DCA). It proposes an association algorithm to solve the dynamic combinational optimization problem for uplink and downlink cellular networks separately using DUDe. When a user device arrives, it first connects to the base station that has the maximum capacity at that time. Subsequently, by using the base station assignment at that time as an individual, the proposed method performs a more optimal base station assignment with DCA by using a genetic algorithm. The computer simulations demonstrate that the proposed method can achieve up to a 140 % higher spectral efficiency than the existing DUDe in the fixed channel assignment (FCA).

Collaborative EV Routing and Charging Scheduling With Power Distribution and Traffic Networks Interaction

The increasing of electric vehicles (EVs) alleviates the faced environmental problems but brings challenges to the optimal operation of transportation network (TN) and distribution network (DN). However, the most of existing research works consider EV charging station assignment and navigation services in the TN separately from charging station power scheduling services in the DN. To overcome this research gap, this paper proposes a collaborative optimal routing and scheduling (CORS) method, providing optimal route to charging stations and designing optimized charging scheduling schemes for each EV. In the order of reporting, whenever an EV reports its charging demand, a CORS optimization model is built and solved so that a specific charging scheme is designed for that EV. Then, the TN and DN status is updated to guide the subsequent EVs operating. The proposed CORS integrates the real-time state of the TN and DN, and effects positive benefits in helping EVs to avoid traffic congestion, improving the utilization level of charging facilities and enhancing charging economy. The combined distributed biased min consensus algorithm and generalized benders decomposition algorithm are adopted to solve the complex nonlinear optimization problem. Through comparing with the existing methods, better effectiveness is verified by simulation results.

GTOC 11: Results found at Beijing Institute of Technology and China Academy of Space Technology

This paper presents the crucial method for the results found at Beijing Institute of Technology and China Academy of Space Technology in the 11th Global Trajectory Optimization Competition (GTOC-11). To build 12 solar-power stations placed in the “Dyson Ring” orbit around the Sun, GTOC-11 focuses on the flyby trajectory design, low-thrust trajectory optimization, and target assignment problem. The mission with time and state constraints causes many difficulties in the design and optimization, which is performed in the following four steps. The first step is to design the orbital elements of the “Dyson Ring” by the grid search method. An approximate model is utilized to evaluate the minimum transfer time for each candidate at first, and the accurate solutions are obtained by the indirect method. The second step involves the Mother Ship trajectories design. A large quantity of earth-to-asteroid and asteroid-to-asteroid subsequences are generated by parallel beam search. The complete trajectories of Mother Ship are designed by connecting the subsequences using beam search. The third step involves the station assignment problem, which is required to design the asteroid transfer device activation epoch and the target station assignment. Based on the results above, the last step involves the rendezvous trajectories optimization to obtain the accurate thrust direction profile. Finally, the submitted and post-competition results are reported.

A Novel Link-Network Assignment to Improve the Performance of Mobility Management Protocols in Future Mobile Networks

5G is expected to support new services and applications that will change the user experience and will drive to a new business landscape. Moreover, most of the services will require optimum connectivity and seamless mobility in heterogeneous networks. To cope with these challenges, network mobility management and network densification are envisioned to be key factors in the emerging 5G architectures. By deploying a large number of cells, 5G architectures can provide users with high throughput and flexible access services, an improvement of the network scalability and optimized network coverage. However, with this densification, seamless mobility support can lead to significant increasing in signaling overhead due to frequent handovers. In this context, network operators need to efficiently plan the deployment of the base stations taking into account the mobility management of the users, and the service degradation that this mobility process could cause. This article aims to optimize the assignment of the base stations to the access routers in the mobile network to improve the network performance. The results obtained show that our proposed Link-Network Assignment algorithm based on clustering techniques achieve significant gains in terms of signaling and data forwarding costs. These simulation results demonstrate that the proposed algorithm can successfully reduce signaling cost and packet delivery cost by up to 56% and 5%, respectively, on average, compared with baseline algorithms.

Influence factors on the passenger evacuation capacity of cruise ships: Modeling and simulation of full-scale evacuation incorporating information dissemination

The passenger evacuation capacity (PEC) of a cruise ship is a pivotal guarantee for quickly and safely evacuating all personnel on a damaged ship during an emergency. A general framework of the agent-based evacuation model is proposed in this study to simulate the entire process of cruise ship evacuation. The total evacuation time, duration of Level of Service (LOS) lower than E, effective flow rate of the escape route, density of the muster station, and usage rate of the lifeboat/life raft are used as the five evaluation indicators to quantify the PEC of cruise ships, thus providing a reference for the optimization analysis of evacuation procedures. With this framework, the PECs under different evacuation strategies of a cruise ship were evaluated in the context of ship heeling to capsizing for the Yangtze Gold 1. Evaluation results show that the increase of heeling angle when the evacuation order is issued does not affect the perception time of the first 90% of evacuees, but significantly increases the total evacuation time after more than 20°. Moreover, the degree of regional congestion is affected by several factors, including the assignment of muster stations, evacuation in batches, the place of obtaining life jackets, the heeling angle, the difference of escape route flows, and other factors. The results of this study provide PECs under different strategies as references for specific accident scenarios and evacuation targets.

The static on‐demand bus routing problem: large neighborhood search for a dial‐a‐ride problem with bus station assignment

AbstractWe introduce a novel optimization problem to support the planning and routing of on‐demand buses in an urban context. We call this problem the on‐demand bus routing problem (ODBRP). Given are a fleet of buses with fixed capacity, a set of bus stations and travel times between them, and a set of transportation requests. Each transportation request consists of a set of potential departure and a set of potential arrival bus stations, as well as a time window, that is, an earliest departure time and a latest arrival time. The aim of the ODBRP is to (1) assign each passenger to a departure and arrival bus station and (2) develop a set of bus routes to fulfill each request in time while minimizing the total travel time of all users. We present the static version of the ODBRP, as well as a straightforward large neighborhood search heuristic to solve it. The performance of the heuristic is established by comparing it to an off‐the‐shelve heuristic solver (LocalSolver). We also use our heuristic to solve (slightly modified instances of) the well‐known dial‐a‐ride problem. The results found by the heuristic for the on‐demand bus system are compared to those of a simulated traditional public bus system with fixed lines and timetables. A thorough analysis of the comparison demonstrates that total user ride times can be significantly lower in an on‐demand public bus system and shows that an on‐demand bus system works best with a large number of small buses.

Examining the Baggage Sorting Station Assignment Problem at the Airport in Terms of Buffer Time

Havaalanı performanslarının sıralanmasındaki anahtar göstergelerden biri yanlış işlenen bagaj sayısıdır. Geciken, hasar gören veya çalınan bagaj gibi hatalı işlenen bagajlar para ve itibar kaybına ve yolcu memnuniyetsizliğine neden olmaktadır. Verimli bir bagaj işleme sistemi havaalanı performansını artıracak, yolcu şikayetlerini azaltacaktır. Bagaj işlemlerinden biri olan bagaj sıralama istasyonu atama problemi, çeşitli kısıtların göz önünde bulundurularak bagajların uçağa ulaştırılması işlemidir. Bagaj sıralama istasyonu atama probleminin amacı tüm uçuşları, kısıtları göz önünde bulundurarak sıralama istasyonlarına atamaktır. Çalışmada, bagaj sıralama istasyonu atama problemi üzerine çalışılmış ve tampon süreyle ilgili analizler yapılarak önerilerde bulunulmuştur. Ortaya konulan çözüm yaklaşımı gerçek verilerle problem üzerinde test edilmiştir. Elde edilen bulgular, bagaj atama probleminde tampon süre üzerinde etkili sonuçlar ortaya koymaktadır.

Two-Stage Multi-Period Coordinated Load Restoration Strategy for Distribution Network Based on Intelligent Route Recommendation of Electric Vehicles

To cope with the frequent blackouts in recent years and improve the resilience of the distribution network, a two-stage multi-period coordinated load restoration strategy for the distribution network based on intelligent route recommendation of electric vehicles (EVs) is proposed. The first stage of the model aims at maximizing the weighted power supply time of load, minimizing the total network loss, optimizing the output of each power supply source at each time period, and determining the optimal charging station assignment scheme for schedulable EVs. The second stage is based on the optimal charging station assignment scheme for EV determined in the first stage, with the shortest total time for all EVs to reach the designated charging stations as the objective and determining the optimal travel route of each EV. The model dispatches the idle EVs during blackout as a flexible power supply resource, realizing the multi-period coordination output of multiple sources and recommending the routes for EVs to reach the designated charging stations to optimize the restoration effect of critical loads. The methods of piecewise linearization, second-order conic relaxation (SOCR) and the Dijkstra algorithm are applied to ensure the feasibility and accuracy of the model. Finally, by comparing the proposed strategy with two different single-stage strategies, the effect of these three strategies on the critical load’s restoration and the operation status of the distribution network is further analyzed, which verifies the effectiveness and superiority of the proposed strategy.

Optimizing bus line platform assignment across bus stations in Utrecht

Due to its increased popularity, public transport has grown considerably, which has resulted in more lines that are operated with higher frequencies. As a result, the current bus stations that are located in strategic places like city centers to serve as a hub are becoming too small. If there is no space to enlarge the station, then usually the best option is to create a second bus station close-by. This raises the problem of distributing the bus lines over the bus stations, such that good connections are offered to transfer passengers. We have considered this problem in the city of Utrecht, which is a middle-sized city in the center of the Netherlands. The central transit hub, which is located next to the central railway station, consists of several separate bus stations. The goal of the research is to minimize the total travel time for all passengers who want to transfer at the hub. Here we are not allowed to adjust the current timetable, and we have to take the capacity and vehicle limitations into account. To find out which journeys are made daily and by how many people we use data from a digital fare system. This results in passenger groups, and for each group we compute the relevant travel options given the current timetable. Thereto, the routes are split into an inbound itinerary, a transfer within the same bus station, and an outbound itinerary; the validity of a travel option depends on the assignment of the bus lines to the bus stations. We decompose the problem into first finding a distribution of the lines over the stations and then assigning them to a platform at the station of choice. In the first subproblem, we find the best set of transfers using Integer Linear Programming (ILP), resulting in a station assignment. In the second subproblem, for each station, we distribute the bus lines over the platforms. In this subproblem, there can be multiple lines assigned to a single platform, as long as there are never more vehicles at the platform simultaneously than physically fit. The goal is to maximize the comfort of the transfer passengers by assigning tight transfers to adjacent platforms; this problem is solved using ILP as well.

Robust estimation of spatially varying common-mode components in GPS time-series

We present a new method to estimate common-mode components (CMC) in global positioning system (GPS) position time-series. The method (‘CMC Imaging’) is fully automated, relies entirely on robust statistics, and exploits the recent proliferation of GPS stations by allowing stations with relatively short time-series to be considered as filter stations as well. The spatial extent of the CMC is purposely defined as local as possible and constrained by the proximity of nearby GPS stations. Our approach also avoids the need for subjective assignment of filter stations; every station is considered and those stations that deviate significantly from the local CMC are flagged and excluded as filter stations. We study thousands of GPS position time-series in the intraplate area of western Europe, and we show that CMC Imaging method is superior to other approaches in terms of noise reduction: we obtain an RMS reduction of 50%, 44% and 39% in the residual time-series in vertical, east, and north components, respectively. We show the importance of using filter stations that are as local as possible, because of systematic lateral variations in inter-station correlations and indeed in CMC, particularly in the vertical component. Those spatial variations are largest for continental stations, particularly those around the Baltic Sea, and could be due to atmospheric and nontidal ocean loading. CMC filtering has a large influence on reducing the temporal trend variability and approximately doubles the trend accuracy (by comparing variability in short-term trends with the long-term estimate).

Optimal fast charging station locations for electric ridesharing with vehicle-charging station assignment

Electrified shared mobility services need to handle charging infrastructure planning and manage their daily charging operations to minimize total charging operation time and cost. However, existing studies tend to address these problems separately. A new online vehicle-charging assignment model is proposed and integrated into the fast charging location problem for dynamic ridesharing services using electric vehicles. The latter is formulated as a bi-level optimization problem to minimize the fleet’s daily charging operation time. A surrogate-assisted optimization approach is proposed to solve the combinatorial optimization problem efficiently. The proposed model is tested on a realistic flexible bus service in Luxembourg. The results show that the proposed online charging policy can effectively reduce the charging delays of the fleet compared to the state-of-the-art methods. With 10 additional DC fast chargers installed, charging operation time can be reduced up to 27.8% when applying the online charging policy under the test scenarios.

Distributed Algorithm for Base Station Assignment in 4G/5G Machine-Type Communication Scenarios with Backhaul Limited Conditions.

A progressive paradigm shift from centralized to distributed network architectures has been consolidated since the 4G communication standard, calling for novel decision-making mechanisms with distributed control to operate at the network edge. This situation implies that each base station (BS) must manage resources independently to meet the quality of service (QoS) of existing human-type communication devices (HTC), as well as the emerging machine type communication (MTC) devices from the internet of things (IoT). In this paper, we address the BS assignment problem, whose aim is to determine the most appropriate serving BS to each mobile device. This problem is formulated as an optimization problem for maximizing the system throughput and imposing constraints on the air interface and backhaul resources. The assignment problem is challenging to solve, so we present a simple yet valid reformulation of the original problem while using dual decomposition theory. Subsequently, we propose a distributed price-based BS assignment algorithm that performs at each BS the assignment process, where a novel pricing update scheme is presented. The simulation results show that our proposed solution outperforms traditional maximum signal to interference plus noise ratio (Max-SINR) and minimum path-loss (Min-PL) approaches in terms of system throughput.

Online Station Assignment for Electric Vehicle Battery Swapping

This paper investigates the online station assignment for (commercial) electric vehicles (EVs) that request battery swapping from a central operator, i.e., in the absence of future information a battery swapping service station has to be assigned instantly to each EV upon its request. Based on EVs’ locations, the availability of fully-charged batteries at service stations in the system, as well as traffic conditions, the assignment aims to minimize cost to EVs and congestion at service stations. Inspired by a polynomial-time offline solution via a bipartite matching approach, we develop an efficient and implementable online station assignment algorithm that provably achieves the tight (optimal) competitive ratio under mild conditions. Monte Carlo experiments on a real transportation network by Baidu Maps show that our algorithm performs reasonably well on realistic inputs, even with a certain amount of estimation error in parameters.