Transportation Scheduling Research Articles

Joint Optimization of Cost and Scheduling for Urban Air Mobility Operation Based on Safety Concerns and Time-Varying Demand

As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, eVTOL performance, and passenger maximum waiting time as constraints. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm was utilized to obtain the optimal solution. The optimal solution obtained in this study can serve 138,610,575 passengers during eVTOLs’ entire lifecycle (15 years) with a total cost of CNY 368.57 hundred million, with the cost of CNY 265.9 per passenger. Although it is higher than the driving cost, it saves 1–1.5 h and thus has high cost effectiveness during rush hours.

Designing and Implementing a Public Urban Transport Scheduling System Based on Artificial Intelligence for Smart Cities

Many countries encourage their populations to use public urban transport to decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times and low economic efficiency for public urban transport companies when buses carry few passengers. This article proposes a Public Urban Transport Scheduling System (PUTSS) algorithm for allocating a public urban transport fleet based on the number of passengers waiting for a bus and considering the efficiency of public urban transport companies. The PUTSS algorithm integrates artificial intelligence (AI) methods to identify the number of people waiting at each station through real-time image acquisition. The technique presented is Azure Computer Vision. In a case study, the accuracy of correctly identifying the number of persons in an image was computed using the Microsoft Azure Computer Vision service. The proposed PUTSS algorithm also uses Google Maps Service for congestion-level identification. Employing these modern tools in the algorithm makes improving public urban transport services possible. The algorithm is integrated into a software application developed in C#, simulating a real-world scenario involving two public urban transport vehicles. The global accuracy rate of 89.81% demonstrates the practical applicability of the software product.

A quadratically constrained mixed-integer non-linear programming model for multiple sink distributions

Rising traffic congestion and fuel costs pose significant challenges for supply chains with numerous retailers. This paper addresses these challenges by optimizing transportation routes for processed tomatoes within a long-haul and intercity distribution network. We use the heterogeneous capacitated vehicle routing problem framework to create a new quadratically constrained mixed-integer non-linear programming model that aims to meet demand at multiple destinations while minimizing transportation costs. Our model incorporates real-time data and route optimization strategies that consider traffic conditions based on freight time and route diversions for expedited deliveries. It aims to devise an optimal transportation schedule that minimizes fuel, operational, and maintenance costs while ensuring efficient delivery of tomato paste. By applying this model to a real-world case study, we estimate a significant 27.59% reduction in transportation costs, dropping them from GH¢20,270 ($1,638.91) to GH¢14,676 ($1,186.61) on average. This highlights the effectiveness of our strategy in lowering costs while maintaining smooth and efficient deliveries.

MobCovid: Confirmed Cases Dynamics Driven Time Series Prediction of Crowd in Urban Hotspot.

Monitoring the crowd in urban hot spot has been an important research topic in the field of urban management and has high social impact. It can allow more flexible allocation of public resources such as public transportation schedule adjustment and arrangement of police force. After 2020, because of the epidemic of COVID-19 virus, the public mobility pattern is deeply affected by the situation of epidemic as the physical close contact is the dominant way of infection. In this study, we propose a confirmed case-driven time-series prediction of crowd in urban hot spot named MobCovid. The model is a deviation of Informer, a popular time-serial prediction model proposed in 2021. The model takes both the number of nighttime staying people in downtown and confirmed cases of COVID-19 as input and predicts both the targets. In the current period of COVID, many areas and countries have relaxed the lockdown measures on public mobility. The outdoor travel of public is based on individual decision. Report of large amount of confirmed cases would restrict the public visitation of crowded downtown. But, still, government would publish some policies to try to intervene in the public mobility and control the spread of virus. For example, in Japan, there are no compulsory measures to force people to stay at home, but measures to persuade people to stay away from downtown area. Therefore, we also merge the encoding of policies on measures of mobility restriction made by government in the model to improve the precision. We use historical data of nighttime staying people in crowded downtown and confirmed cases of Tokyo and Osaka area as study case. Multiple times of comparison with other baselines including the original Informer model prove the effectiveness of our proposed method. We believe our work can make contribution to the current knowledge on forecasting the number of crowd in urban downtown during the Covid epidemic.

Optimizing Road–Rail Multimodal Transport Schedule for Emergency Response with Congestion and Transshipment Sequence Selection

The current literature mainly uses hub capacity or transport route selection to manage the congestion of emergency multimodal transport and pays less attention to transshipment scheduling. This paper proposes an integrated optimization problem of transport routes and transshipment sequences (ITRTSP) and constructs a hybrid flow shop scheduling model to describe it. Based on this model, a recursive method is proposed to calculate the minimum waiting times that cargoes consume in queues at hubs, given the transport routes and transshipment sequences. Furthermore, a memetic algorithm is designed with route selection as the outer layer and transshipment sequence selection as the inner layer for solving ITRTSP. Compared with existing achievements, the model and algorithms can quantify the dependency between transshipment sequence selection and emergency transport time in multimodal transport network settings. The model and algorithms are applied to solve some real-scale examples and compared with the first-come-first-served (FCFS) rule commonly used in the current literature. The results indicate that the makespan is reduced by up to approximately 4.2%, saving 33.68 h. These findings demonstrate that even with given hub capacities and transport routes, congestion can still be managed and the schedule optimized through transshipment scheduling, further improving emergency transport efficiency.

Implementation of smart mobility and relationship with transportation planning and regional development: A case study of South Tangerang city

Urbanization and suburbanization have led to high population growth in certain city regions, resulting in increased population density and mobility. Therefore, there is a need for a concept to address congestion, public transportation, information and communication systems, and non-motorized vehicles. Smart mobility is a concept of urban development as part of the smart city concept based on information and communication technology. Through this concept, it is expected that transportation services will be easily accessible, safe, comfortable, fast, and affordable for the community. This research aims to analyze smart mobility and its relationship with regional transportation planning and the development of South Tangerang, as well as to design a policy strategy model for the planning and development of South Tangerang with smart mobility. The research method used in this study is a mixed method, including analyzing the relationships and weighting of relationships between variables using the Cross Impact Multiplication applied to a classification (MICMAC) matrix. Multi-criteria decision analysis (MCDA) with Promethee software is also used to obtain the necessary policies. The results of this research indicate that the measurement of relationships between variables shows that smart mobility influences regional transportation planning, smart mobility affects regional development, and regional planning affects regional development. This research also provides alternative policies that policymakers should implement in a specific order. First, ensure the availability of public transportation; second, improve public transportation safety; third, enhance public transportation security; fourth, improve public transportation routes; fifth, provide real-time information access; sixth, improve transportation schedules; and seventh, increase the number of bicycle lanes.

Fleet repositioning in the tramp ship routing and scheduling problem with bunker optimization: A matheuristic solution approach

This paper investigates an important planning problem faced by dry bulk shipping operators, referred to as the Tramp Ship Routing and Scheduling Problem with Bunker Optimization (TSRSPBO). The problem is to maximize the overall profit of a fleet of vessels by selecting cargoes and determining ship routes and schedules. We consider this problem under a set of practically relevant features such as flexibility in cargo quantities, as well as bunkering decisions on where to procure fuel and how much. As a particularly novel feature, we address the regional allocation of vessels at the end of the planning period to be well prepared for meeting (uncertain) future demand. To incorporate this, we consider the TSRSPBO as a two-stage stochastic programming problem, where cargo selection, routing, and bunkering decisions are solved in the first-stage problem, and the recourse cost of fleet repositioning is considered in the second stage. We present arc flow and path flow formulations, where the latter employs a priori generation of feasible routes as input. For solving realistically sized instances, we propose a matheuristic based on an Adaptive Large Neighborhood Search (ALNS) framework that iteratively generates columns and solves the path flow model. Computational experiments based on real data show that this matheuristic finds high-quality solutions for large test instances with 120 cargoes, 30 vessels, and ten bunker ports in less than one hour. Also, considering the TSRSPBO as a two-stage stochastic problem achieves the highest profits and is solved almost as quickly as the deterministic problem variant.

Joint Ship Scheduling and Speed Optimization for Naval Escort Operations to Ensure Maritime Security

Joint Ship Scheduling and Speed Optimization for Naval Escort Operations to Ensure Maritime Security

Strategy and Impact of Liner Shipping Schedule Recovery under ECA Regulation and Disruptive Events

Strategy and Impact of Liner Shipping Schedule Recovery under ECA Regulation and Disruptive Events

Peranan PT IMC Ship Management Selaku Auditor Internal Terhadap Safety Management System di MV. Dewi Shinta Manggala

Internal audit activities are carried out to meet and implement the Safety Management System (SMS) rules so that safety on board ships is implemented in accordance with the ISM Code. In addition, an internal audit conducted by a shipping company can minimize the findings of an external audit of the Safety Management System (SMS). The purpose of this research is to find out the implementation process, obstacles and solutions for the implementation of MV internal audit. Dewi Shinta Manggala. The method carried out in this study is qualitative descriptive. Data collection was carried out by in-depth interviews, observations, and documentation. The results of the study show that the obstacles faced in the obstacles experienced in the internal audit process are the unsuitable time between the ship's schedule and the auditor resulting in delays, crew ratings that are not optimal in implementing the Safety Management System (SMS), and the crew's understanding of the Safety Management System (SMS) on board the ship which is considered to be still lacking. So that the efforts that must be made, namely the company's internal parties communicate well with each other, the company gives strict sanctions to crews who do not optimally implement SMS, the company requires crews to attend SMS training.

Round-trip multimodal transportation routes planning for foldable vehicle racks

ABSTRACT Over the past decade, the expansion of China’s auto exports and pulp imports has presented challenges to the international shipping sector. This study examines the features inherent in the novel paradigm employed by COSCO Shipping Specialized Carriers. Specifically, it involves the transportation of Foldable Vehicle Racks (FVRs) using multipurpose pulp carriers on the outbound trip, and the conveyance of pulp alongside empty FVRs on the return voyage. Building upon this foundation, we propose a dual-objective route planning model and construct a round-trip multimodal transportation network. Subsequently, we calculate the optimal scheme, encompassing shipping routes, changeover nodes, and transport modes, using a hybrid genetic and ant colony algorithm. The paper scrutinizes the computational results, validates the effectiveness of the hybrid algorithm, and analyzes the scheme variations across different parameter scenarios, along with conducting a sensitivity analysis. The optimal solution calculated in this paper is found to reduce transit time by 24.77% while improving logistics benefits by 4.11% compared to the firm’s existing shipping schedule. Furthermore, we explore alternative optimal logistics routes in various parameter variation scenarios and considering benefit-time preferences. Eventually, six managerial insights are proposed for the sustainable development of COSCO’s FVR and pulp business based on the experimental results.

Bike Sharing Demand Forecasting Based on the Informer Model

Advancements in machine learning and artificial intelligence have been instrumental in enhancing demand forecasting for shared transportation modes, significantly benefiting urban traffic management and promoting a balanced supply-demand dynamic. This study harnesses the deep learning Informer model to forecast bike-sharing demand, integrating multiple factors to provide an empirical foundation for transport scheduling and redeployment, thereby contributing to the advancement of the field. Our experimental analysis reveals that the Informer model outperforms LSTM in predictive accuracy, as evidenced by improved R² scores and MSE values, reflecting gains in training efficiency and predictive precision. Despite these advances, predictions for extreme demand values, particularly during peak usage periods, indicate potential areas for refinement. Future research directions are oriented towards optimizing model accuracy by leveraging multimodal data for a comprehensive analysis and enhancement of traffic management strategies. This approach aims not only to better predict demand and alleviate congestion but also to deliver more optimized solutions for businesses and convenience for users. Furthermore, the model's application extends beyond bike-sharing to other forms of shared mobility, such as electric scooters, potentially aiding in reducing carbon emissions and furthering the development of the sharing economy and environmental sustainability.

Improving train services to increase student mobility and safety on the Purwakarta-Karawang route

Transportation is essential for the mobility of workers and students, yet many prefer motorcycles over public transportation like trains due to inadequate schedules and limited train availability. This choice contributes to Indonesia's high motorcycle usage and associated traffic accident risks. This study focuses on the Purwakarta-Karawang train route and its impact on student mobility at Universitas Singaperbangsa Karawang. Employing a qualitative method with descriptive data collection and SWOT analysis, this research gathers student feedback on current train schedules and their impact on daily commutes. Findings indicate that increasing the number of train schedules could significantly improve student mobility and reduce the reliance on motorcycles, thereby decreasing traffic accidents. Moreover, better train schedules could enhance access to educational and employment opportunities. The study suggests that the local government should consider expanding and optimizing train services to address these issues. This research contributes to the broader field of transportation studies by highlighting the importance of efficient public transport schedules in reducing traffic accidents and supporting student mobility. It emphasizes the need for policy adjustments to improve Indonesia's public transportation infrastructure and services by proposing practical solutions.

Two incentive policies for green shore power system considering multiple objectives

Shipping significantly contributes to air pollution and greenhouse gas emissions, accounting for more than 80% of global trade. Consequently, governments have been focused on air pollution reduction for many years. The adoption of shore power systems has increased, recognized for their effectiveness in reducing emissions. This study introduces multi-objective mixed-integer programming models to investigate the impacts of government-subsidy-based and berthing-priority-based incentive policies on various types of expense benefits from the perspectives of different stakeholders. This paper aims to optimize shore power equipment deployment, berth allocation, and ship scheduling while balancing environmental benefits, costs associated with shore power, and operational efficiency. We design an improved NSGA-II with a two-stage solution algorithm to solve the studied problem, which is more suitable for large-scale case calculations. Following the case study, we offer insights for stakeholders to navigate the complexities of sustainable port development and promote the adoption of shore power systems.

An exploratory study of approaches to onboard training in different countries

ABSTRACT This study explores how current shipboard/onboard training arrangements vary among countries and what informs these differences. The study – employing qualitative and quantitative research approaches in the form of an online survey and in-depth semi-structured interviews – collected data on onboard training arrangements from 95 MET institutions from 44 countries. From the data analysis, onboard training arrangements were categorised into seven dominant types based on regional and institutional differences with respect to four elements: substitution by simulator training, integration into the academic programme, schedule, and type of ships used. The analysis further explored the different rationales for the diverse approaches to onboard training and the dominance of the approaches in different regions. Although not explicitly mentioned by research participants, a variety of learning theories can explain the reasons underpinning the choice of particular onboard training strategies and these are discussed. Given the fundamental differences in onboard training strategies identified in this study, future research should first examine the issues and difficulties that confront the implementation of onboard training and the factors/trends that actually and/or potentially influence such implementation. Secondly, future research should explore the development of a model that can comprehensively address the challenge of onboard training across countries and do so with current trends in mind, in particular, the increasing fidelity and abilities of simulation for seafarer education and training as well as developments in ship operation with increasing levels of automation.

Simulation-Based Assessment of Hyperconnected Megacity Parcel Logistics

Background: The concept of Hyperconnected Megacity Parcel Logistics (HMPL) was introduced in 2018 and aims to enhance the efficiency, responsiveness, resilience, and sustainability of parcel movements in megacities. However, evaluating such fundamental solutions presents challenges and requires a comprehensive understanding of all stakeholders and decisions involved. Methods: This study introduces a discrete-event agent-based simulation platform that encompasses critical stakeholders and addresses various levels of decision-making. This platform provides an opportunity to evaluate key decisions within an HMPL structure. Results: To demonstrate the capability of the simulator, we assess the impact of package routing and consolidation strategies facilitated by HMPL compared to traditional practices. Preliminary findings suggest that increased interconnection among nodes in HMPL reduces transit times, thereby enabling tighter customer delivery services. However, examining different consolidation heuristics reveals potential trade-offs between handling and shipping costs under fixed shipment schedules, prompting further investigation into dynamic shipment services. Conclusions: The findings of this study suggest that the benefits of innovative approaches in a complex environment, such as parcel logistics, cannot be evaluated in isolation from other decisions. Accurate assessment of the ultimate outcomes and underlying trade-offs requires multi-faceted models that incorporate all key variables.

Fleet Repositioning, Flag Switching, Transportation Scheduling, and Speed Optimization for Tanker Shipping Firms

In response to the European Union (EU)’s sanctions on Russian oil products, tanker shipping firms may adopt two strategies to reoptimize their shipping networks. The first strategy is to switch the flag states of tankers that are not eligible to operate on certain routes. The second strategy is to reposition tankers based on their flag states, i.e., moving those tankers that are eligible from other groups to specified routes. To help tanker shipping firms minimize the total operating cost during the planning horizon in the context of EU oil sanctions, including costs of fleet repositioning, flag switching, and fuel, this study investigates an integrated problem of fleet repositioning, flag switching, transportation scheduling, and speed optimization considering the dynamic relationships among fuel consumption, speed, and load. By formulating the problem as a nonlinear integer programming model and applying various linearization techniques to convert the nonlinear model into a linear optimization model solvable by off-the-shelf linear optimization solvers, this study demonstrates the practical application potential of the proposed model, with the longest solution time of less than two hours for a numerical instance with seven routes. Furthermore, through sensitivity analyses on important factors including unit fuel prices, crude oil transportation demand, and the tanker repositioning cost, this study provides managerial insights into the operations management of tanker shipping firms.

Optimized transportation scheduling for precast concrete components considering heterogeneous vehicle-size matching

Recently, prefabricated construction has been vigorously promoted, resulting in high demand for precast concrete (PC) components. The transportation scheduling optimization problem of PC components with various kinds from multiple projects arises. Unlike conventional cargo, PC components are characterized by shape heterogeneity, large volume, and strict delivery time limits. Based on three characteristics, a heterogeneous fixed fleet vehicle routing problem (HFFVRP) for PC components is introduced, where heterogeneous vehicles, allocation of PC components to size-matching vehicles, and hybrid time windows are considered. Then a two-stage solution strategy based on the improved ant colony optimization (ACO) and Dijkstra algorithm is designed to obtain optimal vehicle routes under minimum transportation costs. The results indicate that the improved ACO-Dijkstra algorithm outperforms in obtaining optimal transportation plans for heterogeneous vehicles compared with manual decision-making and other heuristic algorithms. Sensitivity analysis denotes that utilizing heterogeneous vehicles contributes to reductions in transportation costs, and vehicle configuration should be adjusted along with demand scales. The proposed model and algorithm extend the theoretical basis of construction industrial applications.

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm.

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model's effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

Ship scheduling problem in an anchorage-to-quay channel with water discharge restrictions

The optimization of ship scheduling in water-land transshipment plays a crucial part in enhancing transportation efficiency and alleviating traffic congestion on inland waterways, particularly when the service capacity of locks is insufficient. This study focuses on the ship scheduling problem in an anchorage-to-quay channel, where the navigation of ships is influenced by the water discharge during flood seasons. A new mixed-integer linear programming (MILP) model is formulated for this problem, in which the interconnected decision of ships’ navigation and berthing activities, the variation of water discharge in the waterway channel, and the change of navigation scenarios (i.e., normal or flood impact) are explicitly taken into account. Then a multi-population genetic algorithm (MPGA) is applied to solve this model. Numerical experiments conducted for the Three Gorges Dam (TGD) demonstrated that the proposed heuristic method can tackle the ship scheduling problem within a reasonable time, which is also effective in assisting ships to match acceptable water discharge time window by slightly adjusting the departure time of ships at the anchorage or quay. Furthermore, the proposed method can ensure the navigational safety of ships and improve the efficiency of ship transportation in different scenarios, and quantitatively evaluate the water discharge impact.