Transportation Scheduling Research Articles

Transportation and production collaborative scheduling optimization with multi-layer coding genetic algorithm for non-pipelined wells.

The marginal wells in low-permeability oil fields are characterized by small storage size, scattered distribution, intermittent production, etc. The construction of large-scale gathering pipelines has large investment. So the current production mode is featured by single well tank oil storage, oil tank truck transportation and manual tank truck scheduling. At present, oil well production and crude oil transportation scheduling mainly rely on manual formulation, which has poor coordination and seriously restricts the release of oil well production capacity and the reduction of transportation cost. A mixed-integer linear programming (MILP) model representation was proposed in our previous work. The scale of the built model variables is huge, and the model solution time is long. In this paper, a genetic algorithm based on multi-layer coding is proposed. The first layer of the design code is the driving path of the oil tanker, and the second layer is the crude oil loading and unloading amount and the cumulative time. The algorithm expands the search domain by flipping, exchanging and shifting the code. In the case analysis part, the exact algorithm and the genetic algorithm designed in this paper are used to solve cases of different scales (5, 10, 30 and 200 oil wells) respectively, and the correctness and effectiveness of the algorithm are verified. The results show that, compared with the exact algorithm, the genetic algorithm designed in this paper can quickly solve a feasible scheduling scheme under different oil well scales, especially in the case of large-scale (200 wells) oil well groups. The optimal one-time result in the calculation example (200 wells) takes 1062.3s to run, and the total driving distance of all tank trucks in the obtained feasible scheme is 11280km. This study can guide oilfields to quickly formulate dispatching plans and minimize travel distances in non-pipeline well tanker dispatching.

ANALYSIS OF DELIVERY DELAYS USING STATISTICAL PROCESS CONTROL AT 3PL COMPANY

This study aims to analyze the causes of delivery delays at PT XYZ. Delivery delays have become a critical issue as they can affect customer trust and result in potential losses for the company. The study identifies two main causes of delivery delays: shipping schedules and the aging (storage time) of goods in the warehouse. The method used in this research is Statistical Process Control, which involves several analytical tools such as P-chart, Pareto diagram, fishbone diagram, histogram, and scatter plot. The analysis results show that shipping schedules have a significant impact on delivery delays, with several delivery periods falling outside the upper control limit and lower control limit. Additionally, goods aging exceeding seven days also contribute to delays. Other influencing factors include human aspects (data entry errors), environmental factors (bad weather and changes in shipping schedules), methods (schedule mismatches), and measurements (inaccurate stock data). The study recommends several improvements to enhance delivery efficiency, including optimizing stock recording processes, strengthening work discipline, and improving human resource quality through operational training.

The Innovation and Transfer of Computer Science and Technology in Recent and Modern Railway Transportation

This article aims to discuss the influence of computer technology on the innovation and technology transfer of modern railway transportation. By historical analysis and empirical research, this article first reviews the evolution of railway transportation from steam locomotive to High-Speed Railways, and then compares and analyzes the performance of the improved multi-objective particle swarm optimization (MPSO) algorithm and traditional particle swarm optimization (PSO) algorithm in train operation scheduling through designing simulation experiments. The experiment selected the representative train operation data sets, and the first 25 and 15 data items are used as training sets respectively to predict the subsequent data items, and the prediction results of MPSO algorithm, traditional PSO algorithm and support vector machine algorithm are compared. The results show that MPSO algorithm is excellent in prediction accuracy, convergence speed and error control, and its prediction results are closer to the actual train running state, which verifies the effectiveness of computer technology in optimizing railway transportation scheduling.

Collaborative logistics: optimizing fixed-scheduled container trains

We propose a new logistics concept for rail-based container transportation where, instead of booking individual slots on specific trains (which is the current state of the art), customers only specify from where to where they want their containers to be delivered, as well as appropriate time windows for pickup and delivery. A containers actual transportation route is then chosen by a freight forwarder based on the availability and compatibility of freight trains. We provide an optimization algorithm based on an integer linear programming formulation that can be used to find globally optimal transportation schedules for a given set of containers. Our computational experiments show that this new paradigm can significantly reduce the number of trains required for serving a given container transportation demand, improving the logistic systems’ overall environmental and economic efficiency.

Emergency Capacity Pool to Respond to Unconventional Emergencies Based on Principal–Agent Theory

To address the conflict of interest between the government and enterprises regarding urban emergency transportation resources in unconventional emergencies and to enhance resource allocation and response efficiency. This paper proposes a collaborative government–enterprise model for emergency transport capacity reserves and develops an incentive model based on principal–agent theory. First, by comprehensively considering enterprise characteristics, high-quality enterprises are selected to collaborate with the government in building an emergency capacity pool of social vehicles. Second, to address potential conflicts of interest between the government and enterprises within the emergency capacity pool, this paper uses principal–agent theory to analyze the interest game process under information asymmetry, constructs a corresponding incentive model, and determines the government’s optimal incentive coefficient, the enterprise’s optimal actual capacity supply ratio, and the benefit distribution between both parties. Finally, numerical simulations and sensitivity analyses were used to verify the model’s applicability. The findings reveal that transport effort cost, economic requisition compensation, and government supervision cost influence the optimal decisions and outcomes in government–enterprise interactions. This study provides theoretical guidance and managerial insights for coordinating emergency transport scheduling between the government and enterprises during unconventional emergencies.

Flow Planning Sistem Trayek dan Manajemen Pengangkutan Sampah di Kelurahan Gurabati

Purpose: The urgency for empowerment is that there is no clear system and flow regarding waste transportation in the four RWs in Gurabati sub-district. The aim of empowerment is to plan Flow Planning for the route system and waste transportation management in Gurabati Subdistrict, so that the transportation system is designed with a clear transportation route planning flow so as to minimize residents throwing garbage in the sea and rivers. Methodology: The method of empowerment is to schedule waste transportation times and waste transportation service routes and enforce regulations issued by the Gurabati District government against people who throw garbage carelessly. Results: The result of this service is a fixed transportation schedule, namely three times a week with scheduled transportation times in the afternoon, namely 04.00 in the afternoon to 06.00 in the afternoon. Apart from that, rescheduling the waste transportation system in each RW. Scheduling of waste transportation to be transported by officers, in the morning and afternoon. And the need for strong regulations from the Gurabati sub-district government. Training is also supported to improve capabilities. Limitations: The limitations experienced in carrying out services are the very limited number of transport fleets and officers. If you look at its size, Gurabati Village itself consists of four RWs. Judging from the transportation system analysis, two RWs are needed consisting of one fleet and two waste transport workers. Meanwhile in the field, these four RWs are only served by one fleet so that the waste transportation service is less than optimal. Contribution: Community service activities in Burai show that the contribution of this service is increasing Sogrohogam Partners' knowledge regarding appropriate waste transportation service flows, so that the waste service flow can run well and everything in the Globalti sub-district can be transported as a whole.

AKS-YOLOv8: a lightweight and accurate network model for infrared ship detection

Abstract Combining infrared technology and artificial intelligence technology to achieve efficient ship detection is practically important for monitoring and scheduling of ships in harbours. Due to the infrared image texture features are fewer and the background is complex, these lead to the algorithm model having the problems of complex structure, high amount of memory consumption and not being easy to deploy. To address these problems, we propose a lightweight and accurate infrared ship detection model named AKS-YOLOv8, which is based on YOLOv8 design. Firstly, the Split Asymptotic Feature Pyramid Network (SA-FPN) is used in the neck network. It improves model to detect multi-scale infrared ship in large scenes while keeping the model lightweight. Secondly, we introduced the dynamic convolution module KernelWarehouse Dynamic Convolution (KConv) in the backbone network for enhancing the recognition of small targets in infrared images. And we also used FasterBlock instead of Bottleneck to reduce feature map redundancy and improve inference speed. Finally, we adopt Simple, Parameter-free Attention Module (SimAM) to improve the feature extraction ability for key information of infrared ships. To verify the effectiveness of AKS-YOLOv8, a comprehensive test is performed on the infrared ship target recognition database issued by Yantai iraytek Co., Ltd in comparison with some widely-used detection algorithms. The model precision of AKS-YOLOv8 is improved by 1%, and the parameters and model size are reduced by 37.2% and 32.1%, respectively. The results of the experiment indicate that AKS-YOLOv8 has successfully struck a favorable equilibrium between being lightweight and maintaining accuracy. It can provide a valuable technical reference for infrared ship detection technology.

Cooperative game theory based coordinated scheduling of production and transportation of parallel machines with deterioration effects

Abstract Cooperative game theory is used to study the production and transportation scheduling problem of parallel machines with deterioration effects. Jobs with an initial scheduling order are processed on parallel machines and then transported to customers. Penalties are incurred for early or late completion if the jobs are not completed on the due dates. Considering that jobs can rearrange the scheduling order in the coalition through cooperation to bring about cost savings, the cooperative game model is constructed by considering the jobs as players and taking the maximum cost savings of the coalition as the characteristic function. It is proven that when jobs have the same normal processing time and deterioration rate, the cooperative game has a non-empty core, and a core allocation can be obtained from the β-rule allocation method. A Dual Replay Buffer Double DQN (DRB-DDQN) algorithm is designed to get the optimal schedule of the coalition. Experiments demonstrate the effectiveness of the DRB-DDQN and β-rule allocation method in solving the game scheduling problem.

A machine learning method for the recognition of ship behavior using AIS data

The efficiency of maritime traffic management and the safety of ship navigation have become top priorities. This study introduces a ship behavior recognition method that utilizes the Extreme Gradient Boosting (XGBoost) classification model, in conjunction with the Sparrow Search Algorithm (SSA), to enhance proactive maritime traffic management. The method leverages Automatic Identification System (AIS) data as its primary source and involves several critical steps. Initially, the AIS data is preprocessed, and ship behaviors are encoded. Subsequently, the encoded behaviors are clustered using spectral clustering to create a labeled dataset. Then, this dataset is employed to train and validate the SSA-XGBoost classification algorithm for identifying ship behaviors. Finally, an example analysis is performed in the Yangtze River. The results indicate that the proposed method can accurately and swiftly identify ship behaviors, achieving an accuracy of 97.28%, precision of 96.97%, recall of 97.43%, and an F1 score of 97.19%, surpassing the performance of the existing algorithms. The findings have the potential to aid maritime supervision authorities in promptly assessing ship navigation statuses and provide a valuable reference for developing ship scheduling decisions.

Optimization of High-Speed Rail Express Transportation Scheduling Based on Spatio-Temporal-State Network Modeling

High-speed rail express is gradually gaining attention due to its low cost and high on-time performance. This paper studied the optimization of high-speed rail express transportation modes considering passenger capacity loss. A spatio-temporal network was constructed that includes the fixed passenger train schedule and the high-speed rail express transportation status. Based on this network, an optimization model for transportation schemes considering passenger capacity loss with non-splittable freight flows was developed. The model aims to minimize passenger loss and maximize net revenue from express transportation while comprehensively considering practical constraints such as transfer, loading and unloading, and carrying capacity. The Gurobi optimization solver was used to solve numerical cases, and the optimization results and solution effectiveness were comparatively analyzed under scenarios of sufficient and insufficient capacity.

Truck Transportation Scheduling for a New Transport Mode of Battery-Swapping Trucks in Open-Pit Mines

To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the selection of battery-swapping stations, and the impact of ambient temperature on battery capacity. The primary objective is to minimize the total haulage cost and total waiting time. Both a genetic algorithm and an adaptive genetic algorithm are applied to solve the proposed multi-objective scheduling optimization model. The aim is to identify an optimal scheduling solution without violating any model constraints. Results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively achieve truck transportation scheduling. However, the adaptive genetic algorithm surpasses the basic genetic algorithm, reducing the total transportation costs by 5.6% and total waiting time by 17.4%. It also reduces the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively. The proposed multi-objective scheduling optimization model successfully minimizes the waiting time and transportation costs of battery-swapping trucks while ensuring the completion of production tasks. This approach provides valuable technical support for improving the production and transportation efficiency of open-pit mining operations.

Joint scheduling of hybrid flow-shop with limited automatic guided vehicles: A hierarchical learning-based swarm optimizer

Transportation system in workshop is essential for high-efficient production scheduling. Due to the limited transportation resources, the joint scheduling of production and transportation has emerged as a pivotal issue in modern manufacturing. This paper investigates a joint scheduling of hybrid flow-shop with limited automatic guided vehicles (HFSP-LAGV), which extends the classical hybrid flow-shop scheduling by considering the limited number of the AGVs on the transportation resources. To solve such problem, a mixed integer linear programming (MILP) model is firstly built to formulate HFSP-LAGV. Then, a hierarchical learning-based swarm optimizer (HLSO) is proposed. An encoding and decoding method based on three dispatch rules is proposed. The framework of HLSO comprises a pyramid-based layering strategy, an inter-layer learning and an intra-layer learning. The pyramid-based layering strategy divides the swarm into several layers. In the inter-layer learning, the individuals in higher layers guide the evolution of individuals in lower layers to achieve the exploration of global area. In the intra-layer learning, an offline Q-learning-based local search is designed to implement the self-learning of elite individuals in higher layer to intensify the exploitation of the local area. A Q-learning model that has been pre-trained offline is used to guide the selection of appropriate operator of local search. Experimental results reveal the effectiveness of the designs and the superiority of HLSO over several well-performing methods on solving HFSP-LAGV.

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.

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.

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.

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