Service Routing Research Articles

ER-OCN: Toward efficient network routing in ocean city based on deep reinforcement learning

Smart ocean cities are crucial to future city development, and offshore networks are an essential part of smart ocean cities. Nowadays, communication in offshore networks is becoming increasingly important. However, the offshore networks are highly heterogeneous, with low reliability, narrow bandwidth, and dramatic dynamics. Offshore networks are more challenging to manage than traditional terrestrial networks, and the standard quality of service routing faces significant challenges. Currently, artificial intelligence provides a way to meet the requirements. This paper proposes ER-OCN (Efficient Routing in Ocean City Networks), a two-stage routing optimization scheme driven by artificial intelligence to cope with the complex offshore networks environment. The first stage will calculate the flow paths based on Lagrangian relaxation. Besides, the ocean environment is under dramatic dynamics, leading to unpredictable network topology or link bandwidth changes. Thus, the second stage deployed a deep reinforcement learning algorithm to monitor the environment in real-time and update the routing strategy when needed. We conducted experiments based on actual network topology and traffic data sets of offshore platforms’ network systems. The results prove that our scheme improves communication cost performance by 36.61% compared to traditional methods.

A Triple Bottom Line optimization model for assignment and routing of on-demand home services

‘On-demand home services’ is a fast-growing industry where online platforms match independent service professionals with customers seeking aid for household tasks. In this paper, we study the assignment and routing of service professionals for serving customers of an on-demand home services platform considering the Triple Bottom Line (TBL) criteria for ensuring sustainability in operations. We characterize this as the Home Services Assignment and Routing Problem with the Triple Bottom Line (HSARP-TBL) and implement a Mixed Integer Linear Programming (MILP) model for solving it. We assign service professionals to customers based on their desired time slots and also transport modes for each customer visit by a professional, considering either combinations of public transport or a personal vehicle for each professional’s tour. The objective is to minimize costs due to time window violations and uncovered customers, catering to the economic pillar of the TBL. We incorporate additional constraints related to the TBL by improving customer satisfaction based on the ratings of assigned professionals to customers, with and without subscription (economic), controlling emissions due to transportation of professionals (environmental) and ensuring equity in service allocation and net earnings between professionals (social). For tackling large instances we implement a Hybrid Genetic Search (HGS) algorithm adapting it to our problem setting. We demonstrate that the HGS outperforms the MILP model systematically for large instances in terms of solution value and computational time. Finally, we observe that for some instances, without worsening the primary economic objective, all the TBL indicators can be improved.

Application and comparison of remote sensing techniques for data-driven disaster debris quantification

ABSTRACT Effective disaster debris management requires reasonable predictions pre-hazard and estimates post-hazard of debris for a community to get back to normal sooner. However, there is a lack of data related to post-disaster waste quantities that could validate and improve debris predictions. This knowledge gap can be addressed by using remote sensing technology to quantify disaster debris promptly following a hazard. This study aimed to demonstrate and compare multiple remote sensing tools available for quantifying disaster debris using post-disaster data collected following Hurricane Ida. The tools used in this study are satellite imagery, emergency response airborne imagery, unmanned aerial vehicles (UAVs), and terrestrial laser scanning (TLS). We found that satellite imagery is useful for quantifying vegetative debris generation and transportation across vast areas, however it is often limited by spatial and temporal resolutions. Emergency response airborne imagery, collected within days following the hazard, is well-suited for quantifying transported vegetative debris and can assist debris clearance of emergency service routes across large areas, although it can be limited spatially and temporally to the interests of the acquiring agency. UAVs and TLS can provide precise volumes of debris but UAVs may be a better option due to their lower cost and computational demand. Guidance is provided for selecting a remote sensing tool based on the desired application and available resources, which can assist decision making for disaster waste managers.

The flex-route transit service routing plan considering heterogeneous requests and time windows

Flex-route transit (FRT) service has become increasingly popular in low-density suburban areas as a shared transportation mode. Improving the service quality of FRT heavily relies on effective routing plans. To end of this, this paper addresses the FRT routing plan problem considering heterogeneous requests and time windows (FRTRPP-HRTW), which is formulated as a mixed-integer programming (MIP) model. The MIP model concerns on the objective of minimizing the average system costs. Due to the NP-hard nature of the considering problem, a niche-based modified ant colony optimization (NM-ACO) algorithm is developed to handle it efficiently for large-scale cases. In NM-ACO, a niche-based pheromone update technology is designed to increase the population diversity. Finally, as a case study, a real-world FRT service is employed to validate the effectiveness of NM-ACO. Computational results demonstrate that the NM-ACO can obtain high-quality solutions with small gap in much shorter time compared with the mature solver (i.e. CPLEX). Furthermore, compared with the well-known MMAS algorithm and a state-of-art ACO-DI algorithm, the NM-ACO expresses higher precision and stronger robustness.

How to assess the needs of vulnerable population groups towards heat-sensitive routing?

Heat poses a significant risk to human health, particularly for vulnerable populations, such as pregnant women, older individuals, young children and people with pre-existing medical conditions. In view of this, we formulated a heat stress-avoidant routing approach in Heidelberg, Germany, to ensure mobility and support day-to-day activities in urban areas during heat events. Although the primary focus is on pedestrians, it is also applicable to cyclists. To obtain a nuanced understanding of the needs and demands of the wider population, especially vulnerable groups, and to address the challenge of reducing urban heat stress, we used an inter- and transdisciplinary approach. The needs of vulnerable groups, the public, and the city administration were identified through participatory methods and various tools, including interactive city walks. Solution approaches and adaptation measures to prevent heat stress were evaluated and integrated into the development of a heat-avoiding route service through a co-design process. The findings comprise the identification of perceived hotspots for heat (such as large public spaces in the city centre with low shading levels), the determination of commonly reported symptoms resulting from severe heat (e.g., fatigue or lack of concentration), and the assessment of heat adaptation measures that were rated positively, including remaining in the shade and delaying errands. Additionally, we analysed and distinguished between individual and community adaptation strategies. Overall, many respondents did not accurately perceive the risk of heat stress in hot weather, despite severe limitations. As a result, the heat-sensitive routing service (available in prototype form during publication) that has been created serves as a readily available and accessible source of assistance to combat the effects of heat stress in individuals' daily lives during periods of intense heat. The findings served as input for the calibration of heat stress-avoidant routing and supported the development of local heat protection plans without investing in large-scale adaptation of the built environment. It is crucial to involve the population, particularly vulnerable groups, in the development of the tool to ensure it meets their needs. This study employed a transdisciplinary multi-method approach, which considers practical framework conditions by involving the city administration.

Fairness-oriented multicast routing for distributed interactive applications

Facing the challenges of network services on the edge, such as routing considering Quality of Service, is a crucial issue for current networking research efforts. Multicast routing is an essential technique for delivering routing services at a high level of optimization from the perspective of operators and application providers when there are user groups. Furthermore, routing that considers latency constraints has obstacles when it comes to merging different conditions in the solution, particularly when there is a need for fairness in the users’ communication. This work addresses this fairness requirement in multicasting, showing an efficient solution to increase the balance in routing by choosing better options for fair group interaction.

Moving from classical towards machine learning stances for bus passengers’ alighting estimation: A comparison of state-of-the-art approaches in the city of Lisbon

Passenger alighting estimation is a critical task in public transport (PT) management, especially for entry-only Automatic Fare Collection (AFC) transport systems where passenger alighting are not recorded. Effective estimation methods are necessary for trip analysis and route planning, offering valuable insights into passengers’ mobility patterns and, subsequently, improving the quality of service. However, the stochastic nature of passenger behaviour challenges the degree of successful alighting estimates. A classic approach to infer the alighting stops of passengers is the use of trip-chaining principles. Since these principles are dispersed across the literature in the field, their comprehensive review is pivotal to establish the best practice for alighting estimation. Still, trip-chaining approaches are unable to infer the alighting of non-commuting passengers. This paper addresses these two research gaps by: i) providing a critical overview of the existing principles and methods for alighting estimation; ii) proposing an approach to improve alighting estimation that consistently integrates the most effective state-of-the-art principles on trip-chaining; and iii) further introducing a frequent pattern mining and density-based clustering solutions to support alighting estimation for non-commuting passengers. Considering the public bus transport in Lisbon city as the guiding case study, the achieved estimation rate by the proposed assembled model is 92%. Moreover, the density-based clustering solution is found to improve the estimation of 11pp against classic trip-chaining principles. Furthermore, the proposed model and acquired results yield actionable value to enhance PT operations and services, ultimately leading to improved bus routing and quality of service.

Decisions on ship route, refueling, and sailing speed considering ECA regulation and demand uncertainty

This article aims to maximize the shipping company’s profits by jointly optimizing shipping volume, refueling, ship leg option, and sailing speed with considering of stochastic shipping demand and Emission Control Area (ECA) regulation. Shipping demand is inherently stochastic and intermittent characteristic, causing formidable challenges in shipping service. A distributionally robust optimization method is adopted to handle the demand uncertainties. We build an ambiguity set of shipping demand and introduce a chance constraint with the shipping volume and shipping demand balance to guarantee the satisfactory rate of shipping demand. Moreover, the ECA regulation stimulates the diversification of transportation routes, which further increases transportation uncertainty. Thus, a nonlinear mixed integer programming model with nonconvex chance-constraint is developed. The optimal solution is obtained by transforming the original problem into an attractable second-order conic programming problem. An Asia service route is selected as a numerical experiment. The results show that the proposed model is effective, the optimal refueling strategy, leg option, speed, and shipping volume can be determined simultaneously. Meanwhile, it outperforms the state-of-the-art methods in requiring less demand information and producing fewer conservative results under demand uncertainties. Additionally, some managerial insights are revealed according to the case study.

Integrated Transit-Oriented Development (TOD) with suburban rail network design problem for maximizing profits

ABSTRACT Driven by the transit-oriented development (TOD) policy for rail stations, this study focuses on the network design of rapid suburban railways and the TOD zone design of corresponding suburban rail stations within metropolitan areas. A multi-level rail transit network was introduced, and the newly planned suburban rail network was considered as an expansion of the existing network to provide rapid services between urban and suburban areas. An integrated programming model was proposed to determine the physical and service routes of suburban rail network, the frequencies of each line, and the TOD plan along the selected rail stations. The integrated model is solved using the Adaptive Large Neighborhood Search (ALNS) algorithm. The effectiveness of the proposed model and methods is illustrated through numerical experiments, and several scenarios are designed for parameter analysis.

Research on Power Service Route Planning Scheme Based on SDN Architecture and Reinforcement Learning Algorithm

The power communication network carries various power services to ensure the safe operation of the power network, among which, the relay protection service is the most important service. Reasonable planning of the service route can improve the effectiveness and reliability of data transmission in the power communication network, thereby ensuring the reliable operation of the power grid. This paper constructs a route planning architecture for the power communication network based on a software-defined network. On this basis, parameters such as the power service and network-carrying service status are defined. With the goal of minimizing network risk variance and considering link bandwidth utilization and overload constraints of relay protection services, a service route allocation problem has been raised. To solve this problem, a power service route planning scheme based on a reinforcement learning algorithm is proposed. This algorithm uses the state–action–reward–state–action (SARSA) algorithm to complete service route planning. The simulation results show that using the route planning scheme proposed in this paper can avoid the overload of relay protection services, reduce network risk variance, and effectively balance network risk.

Service Supply Chain Model in an Old Town of Mardin: Service Routing

Service Supply Chains (SSCs) differ from the supply chain in manufacturing. In SSCs, customer orders trigger the SSCs. The supply chain process starts after customer demand. Most often customers are not in the same destinations and orders do not arrive one by one. Hence service providers should manage the ordering process effectively to serve in time. If the orders are widespread and there is a limited service provider the service route should plan carefully and precisely. In this study, service routing is constituted under three main scenarios for the most visited destinations of the old town city of Mardin, Turkey. 3D visual representations of specified regions visual representations were taken from Google Earth. Calculations between chosen touristic attractions and a total length made on Google Earth. The study is novel for being an ex-ante of the tourism supply chain by means of a digital map, in a very tough area of an old city. The scenario-based approach is used as a method of deciding the route for the service supply chain.

Geospatial Tools and Remote Sensing Strategies for Timely Humanitarian Response: A Case Study on Drought Monitoring in Eswatini

This article explores the escalating impact of natural disasters, particularly droughts, in the Southern African Development Community (SADC), with a specific focus on Eswatini. Over the last century, approximately 63 million people in SADC countries have been affected by droughts, leading to challenges in agriculture, livestock losses, and severe food and water shortages. Despite being the smallest SADC nation, the Kingdom of Eswatini faces disproportionate consequences due to its susceptibility to climate variability, particularly drought. The inadequacy and unreliability of rainfall have resulted in a drastic reduction in food production, with maize, a staple crop, experiencing a 70% decline. This adverse trend, spanning three decades, has heightened the vulnerability of farmers to climatic shocks, hindering sustainable agricultural development and impeding poverty alleviation efforts. To address the growing threat of drought in the kingdom, a comprehensive approach is imperative, involving coordinated plans and the development of swift humanitarian relief strategies. This study utilized remote sensing technologies to monitor drought and assess its repercussions, evaluating the impact on agricultural production. Additionally, geospatial tools, including Open Route Service (ORS) and Near Neighbor Analysis algorithms, were employed to optimize humanitarian supply chain logistics. Results from the analysis, including Vegetation Health Index (VHI) fluctuations and drought severity mapping, reveal that 1990 was the year the kingdom was most severely hit by drought. This study also found that smallholder farmers practicing rainfed agriculture in vulnerable regions, such as the lower Middleveld and western Lowveld, suffered the severe socioeconomic consequences of agricultural drought, including income loss, food insecurity, and migration patterns. Through this integrated approach, decision makers can engage in targeted interventions, focusing on farming areas needing irrigation infrastructures or populated areas requiring a coordinated humanitarian response amidst climate variability.

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

Antimagic labeling in butterfly and benes networks

This paper focus on the dynamic and rapidly evolving field of Graph Labeling research. In this paper, Antimagic Labeling for Benes and Butterfly networks has been determined. The findings can be utilized to determine the Antimagic Labeling in Interconnection networks where the routing servers acting as its vertices and the connection between them acts as edges to get the best possible solution for building a network with computational power and the ability to send and receive messages over cabled or wireless communication links.

Integration of Google Maps API with mathematical modeling for solving the Real-Time VRP

Freight transportation is the backbone of urban economies and plays a critical role in the smooth functioning of cities. As such, devising efficient methods for freight transportation planning is of paramount importance. One of the most crucial aspects affecting the efficiency of freight transport is the variability in travel speeds, impacted by factors such as traffic congestion. While traditional approaches often rely on GPS technologies and associated routing services—which can be expensive—for planning, these methods also necessitate frequent re-optimization due to ever-changing traffic conditions. To address these challenges, we introduce a novel solution that integrates real-time traffic data into daily vehicle route planning. Specifically, our method incorporates Google Maps API for traffic congestion estimation and utilizes a Mixed Integer Linear Programming (MILP) model to determine optimal routes for an entire day. We tested our methodology in a major U.S. city and found that it outperforms conventional approaches by up to 18% in terms of routing time, underscoring its practical relevance and efficiency.

Preventive and proactive planning of PaaS maintenance service teams

This paper employing the Product-as-a-Service (PaaS) paradigm provides a model integrating preventive and proactive management strategies to maintain the required service level availability in MultiFunction Device (MFD) offerings. The problems of resource allocation and servicing problems implied by these strategies focus on planning leasing offers that meet an acceptable failure risk level and routing service teams participating in maintenance missions. The aim is to allocate services that provide balanced risk to suppliers and renters, reduce delays, and prioritize urgent cases by routing a fleet of technical vehicles responsible for delivering and receiving service teams without the constraint of requiring the exact vehicle for both operations. The conducted computer experiments demonstrated the effectiveness of the adopted reference model, allowing for increasing PaaS availability and minimizing service costs.

DDSR: A delay differentiated services routing scheme to reduce deployment costs for the Internet of Things

DDSR: A delay differentiated services routing scheme to reduce deployment costs for the Internet of Things

Capacity and demand forecasting in Urban Air Mobility - A simulation of air transportation in Hamburg

This paper presents an innovative approach to urban air transportation planning through the development of a "digital twin" representation encompassing airspace and infrastructure elements, including route structures, existing airports, and future vertiports. The primary objective is to formulate a model that mirrors the dynamics of contemporary and prospective urban air mobility (UAM), driven by the focus on sustainability and electrification in the transportation sector. Given the multifaceted advancements in both personal aerial vehicles (PAVs) and unmanned aerial vehicles (UAVs), along with the emergence of cutting-edge electric aircraft, a multitude of scenarios become feasible. The air transport system we focus on, is the urban area of Hamburg, Germany, and its two inner-city airports and a prototype vertiport. Anticipating a growth in demand for air transport services for the nearly 1.9 million current residents of Hamburg over the next two decades, we assume various feasible scenarios, which in turn would result in an increasing energy demand. Our SIMMOD simulation model utilizes flight schedules for each airport in Hamburg, to which flights are incrementally added until practical capacity is reached at 80% above baseline levels. Our findings reveal that, as demand increases at Hamburg Airport and average operational delay approaches the level-of-service (LoS) threshold of 4 minutes per flight, the necessity for expanded air traffic and ground infrastructure becomes evident. Electric grids should be dimensioned according to these demands. Our results suggest that the capacity of the Hamburg airspace is currently limited by the main airport, and that the Airbus production facility in Hamburg-Finkenwerder can substantially augment its capacity by constructing a parallel taxiway alongside its runway. Moreover, vertiport capacity primarily is limited by factors such as UAV parking and charging positions, drone speeds, turnaround times, available airspace, degree of autonomy and the length of service routes.

Системный анализ проблем управления поисково–спасательными работами в акватории Санкт-Петербурга

The study carried out a systematic analysis of the problems of managing search and rescue operations typical for the waters of St. Petersburg, taking into account its specifics. Approaches are proposed to solve problematic issues in the field of search and rescue operations in the city’s water area. The creation of new rescue stations and the reconstruction of existing ones, the construction of an educational and training complex, and the development of a system to support management decision-making when responding to incidents in the water area are justified. Conclusions are drawn about the need for further scientific research related to the scientific substantiation of the choice of locations for new rescue stations, optimization of routes for rescue services in the current operational situation in the waters of St. Petersburg.

Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

SummaryIndustrial transport system refers to the movement of goods, raw materials, and finished products within and between industrial facilities such as factories, warehouses, and distribution centers. It includes a variety of modes of transport such as trucks, forklifts, conveyor belts, and automated guided vehicles (AGVs). Context‐aware route service management refers to system that can dynamically manage and optimize the routes taken by vehicles in transportation system based on contextual information. Our proposed framework for industrial transport systems and smart intelligent autonomous vehicles utilizes cluster‐based context‐aware routing (CCAR), which includes several significant contributions. Firstly, modified elephant herding optimization (MEHO) algorithm is used to efficiently group autonomous vehicles for routing. Secondly, an improved gravitational search (IGS) algorithm is used for cluster head (CH) selection responsible for transferring context information between vehicles (V2V). Thirdly, a deep hybrid multi‐graph neural network (DHMG‐NN) is designed for optimal neighboring roadside unit (RSU) node selection through different design constraints to ensure data dissemination between vehicles and infrastructure (V2I). Finally, we validate the effectiveness of our CCAR framework using various simulation scenarios. Our framework achieves a reduction of up to 78% in computation time and an improvement of up to 25% in customer satisfaction ratio and 15% higher than the overall performance ratio compared to the existing frameworks.