Path Optimization of Container Multimodal Transportation Considering Differences in Cargo Time Sensitivity

The basic characteristic of container multimodal transport mode is to transport container goods by various modes of transport, which mainly involves in-station transit of containers, container sending operation and cargo handling operation steps. In the new era, the technical means of Internet of Things in the practice field of container multimodal transport has been comprehensively innovated and optimized, which fully ensures that each concrete implementation process and link of container multimodal transport can achieve more obvious transportation cost saving benefits and innovates the operation mode of container multimodal transport business. Therefore, the mode innovation and transformation of container multimodal transport must rely on the integration and support of the Internet of Things technology.

Artificial intelligence is a branch of computer science, which includes natural language, intelligent processing, and professional methods. Since the birth of artificial intelligence, the technology and application fields have continued to grow, and the application fields have also continued to expand. This article aims to study the application of artificial intelligence technology in the management information system of container multimodal transportation and to provide convenient and efficient operation methods for container multimodal transportation. This paper proposes the C-means clustering method. Through the research and development of the terminal management system, it has achieved great success in automation, intelligent planning, and integrated management. At the same time, the EDI system is adopted, which mainly uses the combination of GPS and GIS information platform Internet network technology. Therefore, when evaluating the operation of the multimodal transport virtual container under the control of coproduction, the DEA method is used to operate the multimodal virtual container. The situation is analyzed and evaluated, and the multimodal transport virtual container is determined through investment. The experimental results of this article show that the artificial intelligence system achieves the most efficient multimodal transport management with the most efficient system model, combined with the leading container multimodal transport virtual enterprise, to provide the best way of the management process for the development of the multimodal transport management information system. The intact rate of container cargo during transportation is as high as 99.7%.

The container multimodal transportation is an advanced organization manner, by which the coordinate organization and management among all kinds of transportation modes and among all participants of the whole network is an objective requirement. Coordinate planning is the basis of coordinate management. The graded structure of multimodal transportation planning is analyzed first. Then, the conceptual model of graded optimization is put forward. Besides, mathematical models are proposed for the hardcore of the conceptual model, container regular liner shipping and inland transshipment planning subproblems. By means of the analysis of the structural characteristics of the problem solution, two corresponding genetic algorithms are designed for the two subproblems. Simulation tests show the efficiency of the proposed models and algorithms.

To deal with the uncertainty of container multi-modal transport, a container multi-modal transport route optimization model based on the reliability is proposed. To deal with the problem that network has multiplicity and that node has weights in multi-modal transport, the paper pre-processes the multi-modal transport network to three-dimensional graph based on the transport mode, and uses the improved Dijkstra algorithm to get the K-th shortest path set which take transport time and transport cost as the main target. On this basis, we find out the reliability of the common paths, and figure out the path which has higher reliability. The simulation experiments take sea routes of China to Australia as the main route, prove the model and method can improve the reliability of transport, to a certain extent, can also reduce the loss of the consignor.

Summary. The article highlights the topical issue of development of container multimodal transport, which are a priority in the development of transport services, their use will increase the volume of cargo delivered to the destination by the most efficient modes of transport (their combination). Container transportation of goods significantly reduces the cost of delivery of goods from producers to consumers. Container transportation has become the most economical without transhipment technology, and, above all, in the intercontinental multimodal land-sea service. The aim of the work is to study the practical aspects, substantiate practical recommendations for the logistics of multimodal container transportation of goods in global supply chains. Conduct at comparative analysis of the implementation of multimodal container traffic in different containers, namely 20 and 40 feet. The analysis and calculations of the data of container transportation processes for the conditions of two international transport corridors: TRACECA and trans-Caspian international transport route are carried out. The cost of transportation by sea, although their number is small relative to the total share of routes, significantly affects the total cost of transportation. In order to stimulate freight traffic on these routes, member states have set discounts of up to 10% of the cost of the ferry fare. Studies suggest that the use of multimodal container traffic is currently economically feasible. The conducted research of practical aspects and substantiation of practical recommendations on logistics of multimodal container transportation of goods in global supply chains suggests that the use of container transportation is a way of delivery today using multimodal options for delivery of goods. An in-depth comparative analysis of the implementation of multimodal container traffic in different containers, namely 20 and 40 feet, allows us to conclude that the use of the Trans-Caspian International Transport Route (TMTM) with a 20-foot container is a more economically attractive option and can be recommended.

The Vehicle Routing Problem (VRP) introduced by Dantzing and Ranser (1959) is a prominent combinatorial optimization problem. Over the last several decades, many variants of the multi-constrained vehicle routing problem have been studied and a class of problems known as rich vehicle routing problem (RVRPs), has been formed. This work is about solving a variant of RVRP with dynamically changing orders and time windows constraints. In the real world application, during the working day, new orders often occur dynamically and need to be integrated into the routes planing. A Genetic Algorithm (GA) with a simple heuristic is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on Solomon's benchmark with different percentage of the orders revealed to the algorithm during operation time.

On the Dial-a-Ride with time windows (DARPTW) customer transportation problem, there is a set of requests from customers to be transported from an origin place to a delivery place through a locations network, under several constraints like the time windows. The problem complexity (NP-Hard) forces the use of heuristics on its resolution. In this context, the application of Genetic Algorithms (GA) on DARPTW was not largely considered, with the exception of a few researches. In this work, under a restrictive scenario, a GA model for the problem was developed based on the adaptation of a generic GA model from literature. Our solution applies data pre-processing techniques to reduce the search space to points that are feasible regarding time windows constraints. Tests show competitive results on Cordeau & Laporte benchmark datasets while improving processing times.

TDRS (Tracking and Data Relay Satellite) can be seen as a bridge between user spacecrafts and ground station. The TDRSS (Tracking and Data Relay Satellite System) single access link scheduling problem is an important application field of TDRSS. The important character of the scheduling problem lies in that there is time windows constraint between TDRS and user spacecrafts. Tasks can be completed only within given time windows. The paper builds the CP (constraint programming) model of the scheduling problem based on reasonable assumption. The CP model is solved with improved GA (genetic algorithm) based on path representation of valid genes. Computational experiments indicate that it is quite good to solve the scheduling problem by building the CP model and solving it with improved GA

A sharing logistics platform for less-than-truckloads could increase efficiency in city pickup and last-mile deliveries under time window constraints by pairing (matching) truck bidders and last-mile logistics requesters. The platform will propose new tours based on localization data, travel time and time-window constraints; it operates a matching algorithm to search for stable truck-sharing deals aiming to reduce the number of trucks in use as well as their operational costs. These sharing deals also reduce traveling distances and gas emissions. We simulate a model to understand the effects of fixed costs and variable costs in the operations, we account for reductions, and we provide some managerial insights. Additionally, we perform sensitivity analysis to consider the effect of delivery time windows’ relaxation and additional travel distance constraints to evaluate the proposed solutions under practical conditions.

In this paper, we consider a variant of vehicle routing problem with multiple trip and time windows. In this alternative problem, we think about the scheduling of trucks to a number of customers in the presence of fixed fleet and time windows constraints. This type of problem can be described as determining a number of vehicle trips minimising total travelled distance complying with the time windows and the multiple use of vehicles. We call this problem a multiple trips fixed fleet vehicle routing problems with time windows (MTFFVRPTW). To solve this variant, a new hybrid evolutionary algorithm for transport optimisation problems, called estimation of distribution algorithm with local search is used. The proposed algorithm integrates a double structure of distribution technique, which is used to introduce the variables dependency. To evaluate our approach we provide a number of experimentation to a modified Solomon's instances for the case of multiple trips. The results show that the hybrid proposed algorithm offer a good quality solution in the term of objective functions, also in the running time.

Self-evolution problem of an aircraft-engine assembly workshop with uncertain number of assembly times in knowledgeable manufacturing environment is studied in this article. The whole dynamic self-evolution process is decomposed into a series of static ones by using the rolling horizon procedure. A rolling rule, which is employed to select operations into rolling windows, is designed on the basis of the level priority of each operation. An algorithm for the self-evolution algorithm is then proposed. Due to the deterministic production information, a static decision sub-problem is needed to be solved at each decision point. A general mathematical model of sub-problems is built to minimize the production cost. For solving it, a bi-level genetic algorithm is proposed. In the lower level genetic algorithm, an improved decoding method is also designed by considering production features. Finally, simulation results demonstrate the feasibility and validity of the model and algorithms. Then, computational data indicate that the system with self-evolution capability pays lower costs for production.

Minimizing fuel consumption and carbon emissions through vehicle routing is a critical objective for logistics companies, especially in light of stringent greenhouse gas emission policies enforced by various countries. To address this challenge, numerous studies have proposed robust vehicle routing problem models. Many of these focus on heterogeneous vehicle routing with time window constraints, aiming to reduce fuel consumption and emission costs. However, most research treats delays in customer time windows merely as penalty costs, giving limited attention to the impact of these delays on CO 2 emissions and their associated costs. Such delays—often caused by traffic congestion and rerouting—are significant contributors to excess CO 2 emissions. This paper addresses this overlooked aspect by proposing a model that accounts for the additional fuel consumption and CO 2 emissions resulting from delayed deliveries. It explores uncertainty in delivery schedules by analyzing three critical traffic scenarios: (1) vehicles idling in standstill traffic, (2) vehicles moving in slow traffic, and (3) vehicles being rerouted because of traffic uncertainties—all of which exacerbate CO 2 emissions from late deliveries. We propose a heterogeneous vehicle routing problem with carbon penalty, which optimizes routing to minimize CO 2 emissions for both on-time and delayed deliveries. A mathematical model and a robust genetic algorithm are developed, with their effectiveness validated through practical scenarios. The results offer valuable insights for logistics providers seeking to enhance both sustainability and operational efficiency.

This research investigates the application of optimization methods to the Capacitated Vehicle Routing Problem with Time Windows in the context of bread distribution for the efficiency of different approach for managing large-scale goods delivery. Managing this distribution requires considering complexities such as travel distance, vehicle capacity, and time windows. Specifically, it compares the performance of ALNS, SA, and GA in minimizing total travel distance while adhering to strict delivery windows. The research is conducted across different cases, each distinguished by varying levels of demand, nodes, and time windows for each case. Based on four cases, ALNS is the most effective method among the three methods in optimizing bread distribution. It was averagely 33.02% more efficient than SA and 57.21% than GA for minimizing travel distance and offering a robust solution, improving delivery efficiency across different case scenarios.

In this paper vehicle routing problem with heterogeneous vehicle regarding the delivery time windows is examined . In the present case , each customer has a hard time window and a soft time window . Delivery outside the hardware time window is not possible. Delivery outside the soft time window earliness or delay costs imposed on the model . The mathematical model of the problem , innovative genetic algorithm for a given problem is developed . The results obtained from the sample solution by genetic algorithms , the exact solution of the model in the software Lingo is obtained and s uggests that genetic provided acceptable performance and low error gives an answer in a reasonable time .

The aging infrastructure of petroleum and natural gas pipelines poses a threat to national economies, necessitating precise defect detection for safety and efficiency. To enhance the accuracy of predicting pipeline defect sizes, this study introduces a magnetic leakage detection system, employing Backpropagation (BP) neural networks optimized with genetic algorithms. Traditional BP networks face challenges, including parameter determination and slow convergence, addressed through genetic algorithms' global search capabilities. Simulated data are generated using ANSYS software by using models of semi-circular defects in steel pipes, producing magnetic leakage signals of varying intensities. MATLAB was used to construct both standard BP and genetically optimized BP neural networks. Results show that the latter significantly reduces computational errors, demonstrating improved accuracy in defect dimension prediction. The approach contributes to overcoming non-uniqueness in the recognition process and the complex nonlinear relationship between magnetic signals and defect size parameters. The study offers a guided approach for selecting BP neural network parameters, enhancing practicality. Simulations validate the method's effectiveness, indicating low workload and high reliability. This research provides a meaningful advancement in the detection of defects in long-distance pipelines, impacting the safety and efficiency of petroleum and natural gas transportation.