Arrival Time Uncertainty Research Articles

Integrating arrival time estimation in truck scheduling: an explorative study in grocery retailing

ABSTRACT This paper studies a new managerial approach that integrates predictive trucks’ estimated time of arrival (ETA) into truck scheduling to deal with the truck arrival time uncertainty. The approach exploits ETA to reschedule trucks by the up-to-date information and aims to minimize the trucks’ waiting time. The approach was applied in a real case. Results quantified the impact of adopting ETA for truck scheduling, also showing how the ETA accuracy and peak truck arrival rate affect the total trucks’ waiting time. The paper enriches both truck scheduling literature, investigating a new managerial approach to deal with truck arrival time uncertainty, which greatly threatens the synchronization of logistics flows, and Industry 4.0 literature, showing how real-time data availability can change traditional decision-making processes. Insights for the approach application are discussed, together with the economic, environmental, and social benefits by supply chain actors (i.e. carriers, warehouse managers, and drivers).

Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm

As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications.

Cross-Pacific Vessel Estimated Time of Arrival and Next Destination Prediction with Automatic Identification System Data

With the increase in global trade uncertainty and supply chain disruptions, accurately predicting the estimated time of arrival (ETA) of container vessels can effectively help carriers, terminals, and freight forwarders improve operational efficiency. The Asia-North America route has been recently under stress because of strikes and trade wars between the U.S. and China. Voyages are subject to multiple external factors leading to uncertainty in arrival times. This is especially true for cross-Pacific voyages, where long distances without intermediate port visits allow for a large feasible set of trajectories and vessel speed profiles. Large errors in ETA prediction not only hinder the effective planning and execution of other stakeholders but also lead to significant fluctuations in the types and quantities of goods arriving at the port, thereby hindering port competitiveness and efficient multimodal transportation. Existing literature focuses on estimating ETA and next positions for dense, compact areas at the vicinity of ports. We propose and evaluate model framework based on artificial neural networks (ANN) fed by automatic identification system (AIS) historical data to predict the next destination and ETA for cross-Pacific routes for cases where ETA from the captain is missing in the AIS data. Results show our model can effectively predict next destination and ETA of vessels, achieving a mean absolute error value of 4 h when the vessel is 1,500 nmi away from the port. For comparison, the ANN submodules are replaced with gradient boosted trees, providing similar results. We terminate by highlighting the challenges found to improve the model.

The impact of container call size - evidence from simulation modelling

We present an analysis of the impact of large container ship call sizes on the operational efficiency of container terminals as well as on hinterland modes of transport. We use standard container terminal simulation software, that can represent a realistic terminal operation. We include dynamic crane productivity, arrival time uncertainty, and deepsea vessel prioritisation in our model.Our experiments investigate the impact of introducing larger call sizes of container ships, increasing the uncertainty in arrival times of deepsea ships, and the impact of the phenomenon of split calls that are employed by the largest container ships in certain regions in the world. We differentiate between the impact on terminal operations, land-based hinterland traffic, and hinterland traffic over water, through barges and feeder vessels.Our results show that introducing large ships with large container call sizes comes with benefits and drawbacks. Benefits can be found in planning advantages for both the deepsea ships and barges and feeders handled in the terminal. Drawbacks occur as a result of operational peaks in subsystems such as the quay cranes, the stack and the land side gates. Higher uncertainty deteriorates the performance of the terminal, but the impact on the hinterland modes of transport is greater than on the large deepsea vessels. The impact of the split call is that it brings all the disadvantages of large vessels, but none of the benefits.Our analysis shows the operational impact of large call sizes on terminal operations and hinterland transport operations, based on container terminal simulations with a higher degree of realism than previous work. In addition, we offer, for the first time, insights in the impact of split calls of container ships at container terminals.

Integrated planning model for two-story container ports

This study introduces a novel two-stage stochastic programming model tailored for the unique infrastructure of two-story container ports, a design that significantly enhances land productivity and operational efficiency. Our model, addressing uncertainties in vessel arrival times and container loads, incorporates advanced features like innovative quay cranes, rooftop solar panels, and dense container storage. An efficient metaheuristic is designed for large-scale applications, demonstrating a 25.63% reduction in operational costs compared to traditional decision rules. We also distill key managerial insights, notably the advantage of adopting solar power technology with consistent energy output throughout the day, offering practical guidance for port operators contemplating vertical expansion strategies.

Optimal Service Time Windows

Because customers must usually arrange their schedules to be present for home services, they desire an accurate estimate of when the service will take place. However, even when firms quote large service time windows, they are often missed, leading to customer dissatisfaction. Wide time windows and frequent failures occur because time windows must be communicated to customers in the face of several uncertainties: future customer requests are unknown, final service plans are not yet determined, and when fulfillment is outsourced to a third party, the firm has limited control over routing procedures and eventual fulfillment times. Even when routing is performed in-house, time windows often do not receive explicit consideration. In this paper, we show how companies can communicate reliable and narrow time windows to customers in the face of arrival time uncertainty when time window decisions are decoupled from routing procedures. Under assumptions on the shape of arrival time distributions, our main result characterizes the optimal policy, identifying structure that reduces a high-dimensional stochastic nonlinear optimization problem to a root-finding problem in one dimension. The result inspires a practice-ready heuristic for the more general case. Relative to the industry standard of communicating uniform time windows to all customers, and to other policies applied in practice, our method of quoting customer-specific time windows yields a substantial increase in customer convenience without sacrificing reliability of service. Our results show that time windows should be tailored to individual customers, time window sizes should be proportional to the service level, larger time windows should be assigned to earlier requests and smaller time windows to later requests, larger time windows should be assigned to customers further from the depot of operation and smaller time windows to closer customers, high quality time windows can be identified even with limited data, and cost savings afforded by routing efficiency should be measured against potential losses to customer convenience. Funding: M. W. Ulmer’s work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Emmy Noether Programme, [project 444657906]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0004 .

Using Solar Orbiter as an Upstream Solar Wind Monitor for Real Time Space Weather Predictions

AbstractCoronal mass ejections (CMEs) can create significant disruption to human activities and systems on Earth, much of which can be mitigated with prior warning of the upstream solar wind conditions. However, it is currently extremely challenging to accurately predict the arrival time and internal structure of a CME from coronagraph images alone. In this study, we take advantage of a rare opportunity to use Solar Orbiter, at 0.5 au upstream of Earth, as an upstream solar wind monitor. In combination with low‐latency images from STEREO‐A, we successfully predicted the arrival time of two CME events before they reached Earth. Measurements at Solar Orbiter were used to constrain an ensemble of simulation runs from the ELEvoHI model, reducing the uncertainty in arrival time from 10.4 to 2.5 hr in the first case study. There was also an excellent agreement in the Bz profile between Solar Orbiter and Wind spacecraft for the second case study, despite being separated by 0.5 au and 10° longitude. The opportunity to use Solar Orbiter as an upstream solar wind monitor will repeat once a year, which should further help assess the efficacy upstream in‐situ measurements in real time space weather forecasting.

Research on the scheduling method of ground resource under uncertain arrival time

We present a two-stage scheduling approach including proactive and reactive scheduling to solve the ground resource scheduling problem with uncertain arrival time. In the first stage, an integer programming model is constructed to minimize the delay and transfer costs. After solving this model, we obtain a baseline scheduling plan that considers the service arrival time uncertainty. In the second stage, the feasibility of the subsequent benchmark plan is evaluated based on the current state of the services and resources. The reactive scheduling model is enabled when trigger conditions are met. Moreover, an improved adaptive large neighborhood search is designed to solve the proactive scheduling model effectively. Real data from an international airport in South China is used as a test case to compare different scheduling strategies. The results show that it is difficult to handle the uncertainty of the problem with the benchmark plan that simply considered buffer time. Compared with rolling time-domain scheduling, the average transfer cost of the scheduling strategy proposed in this paper increased slightly, but the average service delay cost can be reduced significantly. Algorithm-wise, instances of different scales are designed to verify the effectiveness of the improved adaptive large neighborhood search algorithm. The efficiency of the algorithm scheme is better than that of the Gurobi solver scheme in medium to large-scale problems. Therefore, the forward and reactive strategies can better handle the uncertainty of airport ground protection services as they can simultaneously guide the allocation and utilization of airport ground protection resources.

Ferry Scheduling Optimization Considering Arrival Time Uncertainty and In-Place Time Differences

The aircraft ferry service is an important link in the transfer of travelers between the remote parking stand and the terminal. By analyzing the process of inbound and outbound ferry service, the relationship between the in-place time of each ferry on the same flight is clarified. A ferry service splitting method considering the difference in in-place time is proposed. Based on this, a dynamic programming scheduling model for ferries is innovatively developed. Considering the impact of flight arrival and departure uncertainty on the in-place time of ferry service, the model is transformed into a dynamic programming stochastic model with opportunity constraints. The optimization objective of this model is to minimize the number of ferries used. To describe the model more easily, a sample average approximation technique is introduced to transform the stochastic model into a deterministic model with confidence. A genetic algorithm based on Monte Carlo random sampling is designed to solve the model. The experimental results based on the actual operation data of an airport in South China show that the dynamic splitting method considering the difference in the in-place time of the ferry can improve the resource utilization efficiency; the scheduling scheme based on the dynamic planning stochastic model has better robustness.

The Optimization of Bus Departure Time Based on Uncertainty Theory—Taking No. 207 Bus Line of Nanchang City, China, as an Example

In order to optimize the bus departure time considering uncertain factors, this paper constructed an uncertain bi-level programming model for departure frequency and scheduling of a bus line. The uncertainty of passenger arrival and bus operation time were taken into account, combined with actual operation conditions. Nanchang 207 bus line was taken as an example to optimize the departure frequency and scheduling in the morning peak hour. The optimal departure frequency in the morning peak hour is 12 times. The overall index value of the route’s non-uniform scheduling during peak hours increased by 0.06 and 9.23% compared with uniform scheduling. The analysis results show that the effect of the non-uniform scheduling is obvious. The issue of bus line departure frequency and scheduling has a positive effect on improving the efficiency of public transportation, reducing operating costs and promoting the sustainable development of the public transportation system. This paper provides a theoretical support for bus operators to optimize route operations.

A novel robust exact decomposition algorithm for berth and quay crane allocation and scheduling problem considering uncertainty and energy efficiency

In container terminals, electricity price variation has a tangible impact on the operating cost of port equipment, especially the quay crane (QC). Accordingly, significant operational cost savings could be realized via careful planning. This study addresses, for the first time, berth allocation and QC assignment and scheduling problem (BACASP) considering energy price variations. A novel mathematical model is developed to minimize both energy costs and vessel tardiness. Furthermore, we consider the uncertainty in QC processing time and vessel arrival time, based on which we propose a robust formulation that optimizes the worst-case scenario. This study also highlights why robustness is essential for cost-cutting considering the energy prices in the BACASP. The model is unable to achieve optimality using CPLEX, even for small instances. Thus, a novel exact decomposition algorithm is proposed to solve the problem over a reasonable computational time. Its novelty lies in four proposed strengthening procedures designed and embedded within the algorithm. To test the algorithm, several instances are generated based on the literature and real data of a port partner. Then, several experiments are conducted to highlight the impact of implementing the four strengthening procedures and demonstrate how considering energy constraints within the BACASP leads to significant cost savings.

Using conjoint analysis to incorporate heterogeneous preferences into multimodal transit trip simulations

AbstractUrban transportation systems involve thousands of individuals making choices between routes with multiple modes and transfers. For transportation system simulations to produce realistic results, modelers need to incorporate these users and their choices. Choice‐based conjoint surveys provide an attractive solution for obtaining flexible utility models that can be used to predict choices for a wide variety of trips. In this study, we demonstrate an example using conjoint survey data of commuter mode choice in the Washington, D.C. metro area (N = 1651). We sample commuters who primarily drive and those that take transit. We examine preferences for different types of multimodal trips, including those with intramodal and intermodel transfers. We find that trips involving a bus transfer are the least preferred while both drivers and transit users both value metro similarly to driving. We also find that walking during transit trips is an important barrier, with the travel time penalty for walking being 60% higher than that of time in a vehicle. Our findings highlight the significance of accounting for differences in modal transfer types in transportation system simulations. Reducing arrival time uncertainty was not a significant factor in commuter mode choice, and commuters' value of time was similar across all vehicle types, suggesting that increasing the relative speed of transit modes may only have a marginal effect on commuter substitution away from personal vehicles.

Optimal resilient operation of smart distribution network in the presence of renewable energy resources and intelligent parking lots under uncertainties

Due to climate changes, the occurrence of low-probability but high-impact disasters such as hurricanes, earthquakes, thunders and etc are increasing. So in the occurrence of these disasters, the resilient operation of smart distribution networks (SDNs) needs to be more investigated by researchers. To address this challenge, in this paper, the optimal resilient operation of the SDN in the presence of renewable energy resources (RERs) and intelligent parking lots (IPLs) is investigated. A hybrid stochastic-IGDT approach is developed to handle the uncertainties of the proposed model. So to do this, the stochastic method is used to modeling of RERs generation, electricity price, and EVs (arrival time, departure time, and initial energy) uncertainties. Also, due to the high impact of load uncertainties on the resilient operation of SDN, the information gap decision theory (IGDT) approach is applied to mitigate the uncertainties of load. In this paper, backup diesel generators (BDGs), battery energy storage systems (BESSs), and demand response programs (DRPs) are suggested to improve the resilient operation of SDN. In order to show the efficacy of the proposed model, four case studies are evaluated on a modified IEEE-33 bus test system. The numerical results indicate, in the risk-averse strategy, the SDN is robust against 7.2% increasing active load by having 10% more operation cost, and the resiliency of the system decrease 3.05% for the worst scenario. On the other side, in the risk-taker strategy, with decreasing 7.4% in active load, the operation cost decrease 10%, and the resiliency of the system increase 4.93%.

Quantitatively Understanding Transit Behavior from the Rider’s Point of View

Arrival time uncertainty is a major source of negative perception by riders, yet how this uncertainty manifests in the rider’s experience is not well-studied. While operators constantly make efforts to improve reliability, and real-time arrival predictions reduce uncertainty for riders in transit, it is also possible to lessen frustration by better informing riders of system behavior beforehand. This work introduces a new method for understanding transit behavior through an analysis of historical arrival time data from San Francisco. The results identify impacts of timeliness on rider experience, such as that average wait time is minimized by showing up five minutes early, or that a five-minute transfer window will be successful 80 percent of the time. Categories of rider experience also are discovered, such as between daytime and evening users. More importantly, it is demonstrated how operators and trip planners can make use of this method to improve rider experience.

Two-stage optimization of airport ferry service delay considering flight uncertainty

As an important part of the ground service for flights parked at remote stands, the scheduling optimization of ferry vehicles is the key factor to improve the apron support capacity. Considering the uncertainty of actual arrival time and departure time of flights, a two-stage optimization model for flight on-time service rate and service delay time is proposed in this paper. At the first stage, a capacity network with four types of nodes and five types of arcs is constructed. By setting appropriate arc capacity and cost parameters, a programming model based on the minimum cost flow is constructed to optimize the number of flights that can be served on time by a limited number of ferry vehicles. At the second stage, a new capacity network and a time-space network combined with a heuristic algorithm are constructed, and integer linear programming models are proposed to optimize the delay time of unserved flights in the first stage. The efficiency of the proposed method is verified by flight data from Beijing Capital International Airport and further analysis is carried out. This study can provide a decision-making reference for the scheduling optimization of airport ferry vehicles.

The impact of adding the Northern sea route to the Belt and Road Initiative for Europe: A chain cost approach

In this paper, the possibility of adding the North Sea Route (NSR) route to the Belt and Road Initiative (BRI) is researched whereby the main aim is to determine if it is possible to set up a container service via the NSR route that could attract cargo from the existing maritime routes via Suez and the land route. In order to make the analysis, a model which is able to calculate the total generalised chain cost for a supply chain is used and updated.This analysis shows that it is possible to set up such a competitive service compared to the land bridge and the Suez Canal Route (SCR) for cargo that has a high value of time. For these specific cargo types, it is possible to attract cargo for the NSR from the SCR at equal costs, but with an average time saving of 10%. Comparing the BRI or land bridge to the SCR, there is a cost increase of 20% and a time decrease of almost 65%. Considering the rather strict limitation in capacity, it should be noted that a single NSR service of eight 5,400 TEU vessels already offers around half the capacity of the land bridge. The uncertainty in arrival times, however, would remain an issue in the NSR service, but with ice diminishing, this risk will decrease as well.

Ride-sourcing Service Pricing and Cancellation Penalty under Service Arrival Time Uncertainty

This study investigates ride-sourcing service pricing and order cancellation penalty in a duopoly market under service arrival time uncertainty. Order cancellation is wasteful to the service provider and society as a whole due to the incurred unproductive congestion and therefore, should be minimized either by penalty or regulation. Certain cancellations are inevitable, due to customer trip changes; others, however, arise from ordering from two service providers, necessitating the need for cancellation. We model the service arrival time perception via an inhomogeneous Poisson process and derive the cancellation behavior in a stochastic manner. A game-theoretic approach is then developed to formulate the competitive market equilibrium. For comparison, we also study the monopoly market as a benchmark. The monopoly market is found to be better in terms of order cancellations and service waiting times, but worse in trip fares for large markets. Based on these results, we propose possible regulatory interventions to reduce wasteful order cancellations.

Dynamic Appointment Rescheduling of Trucks under Uncertainty of Arrival Time

The uncertainty of the arrival time of trucks has increased the complexity of terminal operations. The truck appointment system (TAS) cannot respond to this problem in time, which can easily cause appointment invalidation and reduce the efficiency of truck operations and terminal operations. This paper comprehensively considers the related constraints of truck re-scheduling costs, gate waiting costs, and idle emission costs. With the goal of minimizing the comprehensive operating costs of truck companies and port companies, a dynamic appointment rescheduling model for external trucks based on mixed integer nonlinear programming is established. This paper designs an adaptive quantum revolving door update mechanism and proposes a double-chain real quantum genetic algorithm. The simulation experiment results show that compared with the traditional scheduling, the truck dynamic appointment rescheduling model can effectively reduce the comprehensive operating costs of the truck company and the port company and alleviate the congestion of the port. The probability that the truck cannot arrive at the port on time, the advance time for the truck to confirm the arrival time, and the length of time that the external truck cannot arrive at the port on time have a significant impact on the cost of the reschedule of the TAS. This paper favorably supports the manager’s operational decision-making.

Cooperative scheduling optimization for ground-handling vehicles by considering flights’ uncertainty

The ground-handling vehicle services of airports are facing significant challenges due to the uncertainty of actual flight arrival time, leading to low resource utilization efficiency and unreasonable service sequence. Moreover, there are different properties (service objects and service orders) of ground-handling vehicles that are not only challenging for modeling schedules of various vehicles but also increase the difficulty when dealing with coordination optimization among all different kinds of ground-handling vehicles. In this paper, the refuellers and shuttle buses would be integrated to provide a more realistic and coordinated optimization schedule for ground-handling vehicles’ dispatching. Specifically, multi-objective mixed integer programming is developed to optimize the priority of services and corresponding service time window by minimizing service costs and time losses. Then, the uncertainty of the flights’ actual arrival time is described by the method of chance constraints. Subsequently, the proposed model is utilized at the Beijing Capital Airport to indicate the effectiveness of the proposed modeling framework. Results demonstrate that significant service performance gains (reduced by 66% in time exceeding the planned departure time, 45% in extra waiting time of vehicles, 40% in flights not departing on schedule, and increased by 31% in the absorption of delay time, etc.), which demonstrates its application in solving schedule problems among other airports.

Intuitionistic fuzzy multi-objective milk-run modelling under time window constraints

Milk-run is a delivery method allowing to move small quantities of a large number of different items with predictable lead times from various suppliers to a customer. The main goal is to minimize the transportation cost by minimizing the travel distance and by maximizing vehicle capacities. The effects of uncertainties in arrival times of vehicles and loading times of shipments should also be considered in modeling the milk-run problems. In this paper, a multi-objective linear programming model, an ordinary fuzzy multi-objective linear programming model and an intuitionistic fuzzy multi-objective linear programming model are proposed for the milk-run modeling under time window constraints. The proposed approaches are applied on the real-life data of Borusan Logistics which is one of the largest logistics firms in Turkey and the results are presented.