Transportation Scheduling Research Articles

Short-term berth planning and ship scheduling for a busy seaport with channel restrictions

This paper addresses the short-term berth planning and ship scheduling issues for a busy seaport with channel restrictions, in which the channel width of the port can accommodate one large-sized ship only or two small-sized ships in its two-way navigation (inbound and outbound). A mixed integer linear programming (MILP) model is presented, in which the inbound/outbound movements of ships between multiple anchorages and multiple basins, and the shifting movements of ships between or within the harbor basins are explicitly taken into account. In view that the MILP model is usually computationally low-efficient for practical large-sized problems, we reformulate the MILP into an equivalent set-partitioning model and develop a column generation (CG) algorithm to solve this model. Several efficient methods are also proposed to improve the performance of the CG algorithm. The computational experiments for a real Chinese port show that the proposed CG algorithm significantly outperforms some existing methods (e.g., branch-and-cut and truncated column generation) and the port scheduling schemes adopted in reality. It can solve the instances with up to 35 inbound ships, 35 outbound ships, 6 shifting ships, and 43 berths within ten minutes, with an average optimality gap of 0.32%. Managerial insights for the port operators are also provided to guide the operations and management of the ports with channel restrictions.

Casualty transport scheduling considering survival probability and injury classification

The occurrence of disasters often causes a large number of wounded persons. The rapid rescue and transport of wounded persons for medical care is a key concern of emergency management. To maximize the number of survivors, we propose a new transport strategy based on injury classification, called the Red-Yellow-Green-Sequential (RYGS) transport strategy, which prioritizes the transport order by the degree of injury of the wounded persons. To solve this intractable problem, we propose an improved ant colony optimization algorithm (IACO) to solve it approximately. IACO not only involves a pheromone updating method according to the characteristics of the problem itself, but also combines the particle swarm optimization algorithm and the genetic algorithm to avoid local convergence. Through numerical studies, we demonstrate that IACO outperforms ACO under different transport strategies in different scales and scenarios. In addition, we demonstrate through numerical studies the superiority of RYGS among three transport strategies in different scenarios and scales. We also demonstrate that the combination of IACO and RYGS performs best in different scenarios and scales. Finally, we conduct a sensitivity analysis to analyze the impacts of the different medical resources on the objective for the problems with different combinations of scales and scenarios based on IACO. We also discuss the practical implications of the findings for emergency management.

An integrated production and transportation scheduling problem with order acceptance and resource allocation decisions

In the global market competition, coordinating decisions and integrating different aspects of supply chains would help managers promote the overall performance of systems, and reduce the overall costs. The integration of production and transportation is one of the essential considerations of manufacturers to achieve more efficiency levels. In this paper, an integrated production and transportation scheduling problem is addressed. The objective is to maximize the revenue of accepted orders and to minimize the sum of delivery cost, weighted tardiness cost, and fixed cost of vehicles, by considering resource allocation and resource-dependent processing times. The problem is regarded as strong NP-Hard. A Mixed Integer Linear Programming (MILP) is proposed. Also, two meta-heuristic solution approaches, based on Adaptive Genetic Algorithm (AGA) and Tabu Search (TS) are proposed to solve this problem. The computational experiments based on small-scale and large-scale analyses are provided to assess the effects of different parameters of the problems on both optimality and solving time criteria. It is deduced that the proposed AGA has a better performance and higher efficiency, rather than the proposed TS, in terms of optimality. The results indicate the capability of the proposed algorithms in solving the real-life problems in an efficient manner.

Static Integration Based on Stochastic P-Time Petri Nets of Maintenance Scheduling for Railway Transport Equipment

In transport systems, all equipment requires maintenance, which directly affects the machine’s availability and consequently the planned transport schedule. The purpose of this paper is to carry out a method for integrating recovery jobs in railway systems. The proposed method allows the insertion of preventive and corrective maintenance operations when the transport equipment is available in order to minimize periods of inactivity, avoid catastrophic scenarios, and maintain stability and safety of the studied networks. A computing algorithm, allowing insertion of the planned recovery tasks in periods of metro availability, without changing the initial scheduling solution, is established. Finally, we illustrate the implementation of the proposed approach on Tunisian Sahel railway transport networks.

Efficient online one-way traffic scheduling for restricted waterways

In Yangtze River traffic management, to manage ship sequencing and scheduling effectively and efficiently in restricted waterways has long been a challenging issue. This paper proposes a Sliding Window based Online ship Sequencing and Scheduling algorithm (OSS-SW) to tackle this problem. The OSS algorithm is capable of generating a more efficient ship sequence by introducing the ‘position shift’ concept which takes advantages of ships’ sailing time differences in the restricted waterway. The sliding window mechanism on the other hand is introduced to handle the traffic uncertainties and to reduce the computational complexity. The impact of the restricted waterway congestion on the performance of OSS-SW is also investigated. Further, the parameter settings of the proposed OSS-SW are investigated in detail. Both simulation studies and the real data applications confirm that the proposed OSS-SW algorithm outperforms the existing widely adopted Traffic Signal Revealing System (TSRS) and the Expert System based algorithm (FAHP-ES) in solving the ship sequencing and scheduling problem.

Machine Learning-Based Approach to Liner Shipping Schedule Design

Machine Learning-Based Approach to Liner Shipping Schedule Design

A simultaneous container assignment and ship scheduling optimisation model in container shipping

Maritime transportation is preferred over other transportation forms for trade and has seen a significant increase worldwide. One of the problems that often occur in maritime transportation is the high cost because vessels' availability and capacity will always be the same while the demand tends to increase and erratic for each period. A mathematical model was developed in this study to determine the optimal value of container cargo and shipping frequency simultaneously to minimise the shipping cost for each voyage. To solve the container cargo optimisation problem, we presented an integer linear program, while for the scheduling, we used the greedy algorithm. Microsoft Excel Solver and LINDO software were used to solve the problem in this study. We provided computational calculations to simulate our mathematical model. The results show that the developed model produces feasible solutions for all scenarios due to the objective function and the constraints. Overall, our scheduling results can reduce the shipping frequency. The optimal solution value of container cargo and the scheduling results for each scenario are different because the total number of transported containers for each scenario is different.

Stochastic Programming for Liner Ship Routing and Scheduling under Uncertain Sea Ice Conditions

It is anticipated that in the foreseeable future the Northern Sea Route (NSR) will be able to serve commercial shipping as an alternative transportation shortcut between East Asia and Europe, especially in the summer season. The sailing time, however, is heavily subject to the variation of sea ice conditions along this route. Any participating shipping company must consider how to mitigate the ill effects on itinerary planning caused by sailing time and cost uncertainty. Finding a good trade-off between the benefit from a tight schedule and the risk caused by an unexpected delay is a key element in relevant routing and scheduling decisions, and may be beyond the reach of traditional deterministic planning models. With the aim of maximizing profit over all possible shipping environment scenarios, this article proposes a two-stage stochastic nonlinear integer programming model for liner ship routing and scheduling with uncertain shipping time and cost, the nonlinearity of which arises from the coexistence of schedule-sensitive shipping demand and uncertain arrival time variables in the objective function. The model is converted into an equivalent linear integer programming counterpart by introducing a set of nominal delay variables, and Benders decomposition is applied to solve the linearized problem. Numerical experiments and sensitivity analyses are conducted to validate the efficacy and effectiveness of the model, the results of which suggest several managerial insights that can be used to guide liner ship route and schedule planning under uncertain shipping conditions.

Integrated production and transportation scheduling with order-dependent inventory holding costs

In this study, we consider an integrated production and transportation scheduling problem faced by make-to-order manufacturers that adopt a commit-to-delivery business mode and cooperate with third-party logistics providers to deliver processed products to customers. The third-party logistics providers typically offer multiple shipping modes chosen by the manufacturers to deliver products, each shipping mode with a shipping time guarantee and a shipping cost function that is non-increasing in shipping time and sub-additive, non-decreasing in shipping quantity. The problem involves inventory holding costs which not only depend on the time that the products spend in temporary storage but also depend on customer types. The problem is to determine an integrated production and shipping schedule that satisfies the committed delivery due date limitations for the customers, such that the total cost of shipping and inventory holding is minimized. We investigate two cases with and without split delivery. For both cases, we first show that both of them are ordinarily NP-hard, prove that there exist no polynomial-time approximation algorithms with constant worst-case ratios, propose exact algorithms to solve them, and finally design column generation-based heuristic algorithms to find feasible solutions. The computational results demonstrate that the heuristic algorithms are capable of generating near-optimal solutions efficiently. We also consider two interesting practical variants of the problem.

Intermodal service network design with stochastic demand and short-term schedule modifications

Service network design is a crucial planning task for intermodal operators on the tactical level as it defines the transportation schedule and the routing of commodities through the intermodal network. In many cases, the transportation demand over the planning horizon cannot be known with certainty, resulting in a stochastic service network design problem. Whereas current literature focuses on outsourcing as a recourse action when reacting to fluctuating demand patterns, we introduce a new variant of this problem in which an ad-hoc modification of the planned schedule for the transportation assets is possible. Although this recourse action is commonly applied in practice, a detailed investigation including high-performance solution procedures for this specific service network design problem has been missing until now. We present a two-phase matheuristic and respective computational experiments that demonstrate its superior performance compared to commercial solvers, including the capability of solving large stochastic instances. Moreover, by presenting the results of a real-world case study for a large intermodal rail operator, we illustrate substantial cost reductions compared to an expected value model and a stochastic model without consideration of schedule modifications. Our detailed analysis further reveals a different solution structure when schedule modifications are considered which explains the cost savings achieved.

A mathematical model for scheduling of transportation, routing, and cross-docking in the reverse logistics network of the green supply chain

Cross-docking refers to the practices of unloading materials from inbound vehicles and then loading them directly into outbound ones. Removing or minimizing warehousing costs, space requirements, as well as inventory utilization, cross-docking simplifies supply chains and makes them deliver goods to markets in a faster and more efficient manner. Accordingly, a mixed-integer linear programming ($MILP $) model is developed in the present study to schedule transportation routing and cross-docking in a reverse logistics network ($RLN$). Furthermore, different traffic modes are also considered to reduce fuel consumption, which reduces emissions and pollution. The proposed model is a multi-product, multi-stage, and non-deterministic polynomial-time that is an NP-hard problem. We use the non-dominated sorting genetic algorithm II ($NSGA-II$) to solve the model. A numerical example has been solved to illustrate the efficiency of the method.

Real-world trajectory sharing with local differential privacy

Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared. Local differential privacy enables data sharing in which users share a perturbed version of their data, but existing mechanisms fail to incorporate user-independent public knowledge (e.g., business locations and opening times, public transport schedules, geo-located tweets). This limitation makes mechanisms too restrictive, gives unrealistic outputs, and ultimately leads to low practical utility. To address these concerns, we propose a local differentially private mechanism that is based on perturbing hierarchically-structured, overlapping n -grams (i.e., contiguous subsequences of length n ) of trajectory data. Our mechanism uses a multi-dimensional hierarchy over publicly available external knowledge of real-world places of interest to improve the realism and utility of the perturbed, shared trajectories. Importantly, including real-world public data does not negatively affect privacy or efficiency. Our experiments, using real-world data and a range of queries, each with real-world application analogues, demonstrate the superiority of our approach over a range of alternative methods.

A heuristic optimization approach for multi-vehicle and one-cargo green transportation scheduling in shipbuilding

To actively respond to the call for green shipbuilding, block cooperative transportation has been particularly concerned in reducing carbon emission in the shipyard, and hence a “multi-vehicle and one-cargo” (MVOC) green transportation scheduling problem emerges. Aiming to solve this problem effectively and improve transportation efficiency and reduce energy consumption, a bi-objective mathematical model combined routing model with synchronization constraints is proposed to simultaneously minimize non-value-added transportation time cost and total CO2 emission. A Pareto-based multi-objective Tabu Search (MOTS) algorithm is then designed to solve the model, in which local improvements are developed to generate promising neighboring individuals. Experimental results show that the proposed MOTS algorithm can effectively solve the problem even on a large scale and outperform the classic algorithm of nondominated sorting genetic algorithm-II (NSGA-Ⅱ). It is hoped that this work enables an operation mode with high efficiency and low energy consumption and provides useful insights for flatcar transportation scheduling operators in the shipyard.

Wisata Sejarah, Warisan Budaya, dan E-Service: Study Kasus untuk Menilai Preferensi Pelaku Wisata Dimasa Pandemi Covid 19 di Yogyakarta

This paper discusses the use of in-depth FGDs to assess residents, tourists, and travelers' preferences for e-service electronic services that will increase access to historical tourism heritage and cultural tourism in Yogyakarta Special Region (Keraton Yogyakarta and Taman Sari) during the Covid Pandemic. 19. Several electronic e-services are mentioned, such as in social media FB, TW, IG and travel blogs. The implementation of this integration is closely related to information and communication technology (ICT). The results show that the need for customized information e-service services, profiling, booking systems, trip planner, guided tours, online practical information (transportation schedules, etc.) as well as virtual tours. In several findings, the FGD group expressed the need for various forms of electronic governance. Cellular devices and navigation systems are also high on the agenda in more technologically advanced cities.

Joint optimization of ship scheduling and speed reduction: A new strategy considering high transport efficiency and low carbon of ships in port

Ship emissions is one of the main components of port pollution. Inefficient waterway management may not only result in additional waiting time of ships, but also the aggravated emissions in port. With special consideration of combining scheduling optimization and speed reduction of ships in port, this paper proposed a new emission reduction method, called ship scheduling with speed reduction (SSSR). Performance of the SSSR method has been testified through comparisons between the new and traditional ship emission reduction methods based on the navigation safety under three different strategies. The effectiveness of the new ship emission reduction method has been testified through a series of experiments with practical data in port. Especially, under various stages in port, the emissions of ships can be reduced by 8.0%–11.9% and the traffic efficiency can be improved by 3.8%–6.2%.

A multi-stage approach for optimizing the three-echelon joint newspaper distribution network of two major publishers in São Paulo, Brazil

In this paper we address an optimization problem that arises in two- or three-echelon distribution network design whereby there is an interplay between the production and transfer schedule of semi-customizable products and their delivery under tight time windows. It is motivated by a real-world problem of optimizing the joint newspaper last-mile distribution network for SPDL (São Paulo Distribution and Logistics), a dedicated logistics provider established by the two major newspaper publishers in São Paulo, Brazil. Such collaborative logistics comprises servicing a very large number of customers in the urban setting of a large metropolitan area. The last-mile delivery is performed daily by a fleet comprising several hundreds of motorized vehicles (mainly motorcycles and light vans) that may originate either from a distribution center (that receives and consolidates different newspapers originating at the two printing sites for the delivery routes) or from informal transit points to where they are transferred, forming a multi-echelon delivery system. The resulting problem is complex not only due to its dimension but also as it involves three main types of decisions that should be taken simultaneously: the number and location of the distribution centers (DCs), the optimal synchronization of the transfer schedule of newspapers from the two printing sites to the selected DCs and the newspaper last-mile delivery. The main theoretical contribution of this work is a multi-stage solution approach whose core is a schedule-to-distribution model that allows integrating printing ramp-up constraints with the transfer shipment schedule and distribution routes. Together with a facility location model and a two-tier delivery districting, we could redesign the multi-echelon distribution operation in a more cost-efficient manner while ensuring the current service level to be maintained without service disruptions. This approach allowed us to obtain a new optimized network configuration and the corresponding delivery clusters with vehicle type choice as well as the optimal transfer schedule that yielded a significant cost reduction of nearly 18%.

Simultaneous production scheduling and transportation optimization from mines to port under uncertain material supply

Industrial mining complexes can be optimized using simultaneous stochastic optimization (SSO), which manages the risks associated with meeting production targets while capitalizing on the synergies that exist between the various components of the related mineral value chain. This paper introduces an extension of past SSO approaches for the long-term, allowing to simultaneously optimize the schedule of the production and the mines-to-port transportation of mining complexes under uncertain material supply. The inclusion of mine-to-port transportation scheduling facilitates the analysis of the mines-to-port equipment usage, while generating suitable mine production schedules. The proposed stochastic mathematical program formulation is applied to a two-mine, single-port iron ore mining complex. In doing so, it is shown that the related model is capable of producing optimal production schedules, minimizing deviations from products requirements, and delineating the yearly use of the mine-to-port transportation equipment.

A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry

This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO2 equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the ε-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.

A Proposed Framework for Identification of Indicators to Model High-Frequency Cities

A city is a complex system that never sleeps; it constantly changes, and its internal mobility (people, vehicles, goods, information, etc.) continues to accelerate and intensify. These changes and mobility vary in terms of the attributes of the city, such as space, time and cultural affiliation, which characterise to some extent how the city functions. Traditional urban studies have successfully modelled the ‘low-frequency city’ and have provided solutions such as urban planning and highway design for long-term urban development. Nevertheless, the existing urban studies and theories are insufficient to model the dynamics of a city’s intense mobility and rapid changes, so they cannot tackle short-term urban problems such as traffic congestion, real-time transport scheduling and resource management. The advent of information and communication technology and big data presents opportunities to model cities with unprecedented resolution. Since 2018, a paradigm shift from modelling the ‘low-frequency city’ to the so-called ‘high-frequency city’ has been introduced, but hardly any research investigated methods to estimate a city’s frequency. This work aims to propose a framework for the identification and analysis of indicators to model and better understand the concept of a high-frequency city in a systematic manner. The methodology for this work was based on a content analysis-based review, taking into account specific criteria to ensure the selection of indicator sets that are consistent with the concept of the frequency of cities. Twenty-two indicators in five groups were selected as indicators for a high-frequency city, and a framework was proposed to assess frequency at both the intra-city and inter-city levels. This work would serve as a pilot study to further illuminate the ways that urban policy and operations can be adjusted to improve the quality of city life in the context of a smart city.

Public Transportation Selection Application Based On Decision Support System

In Kuala Lumpur, there are various types of public transport such as bus and multiple types of rail transport. This facilitates the residents in Kuala Lumpur in terms of mobility and can reduce the level of traffic congestion around Kuala Lumpur. However, some people might get confused about the alternative routes when they are using the public transport. Next, road user might find information from maps and Internet which requires them to analyse and decide the routes by themselves. In addition, complex scheduling and routes of public transportation are hard to understand for the public. Besides, road users are not aware of better route and public transport available based on their requirement. In the end, they might be getting jam-packed on the road due to the limited information on the current available public transportation. Therefore, a decision support system is proposed to generate a type of public transport which suits with the criteria selected by the user. The system consists of five main modules and involves common users and system administrators. The application is written in Java programming language. Android Studio is used as software development and MySQL database. It is expected that this application will be able to help users to get public transportation options that suit the criteria they need.