scholarly journals Interterminal Truck Routing Optimization Using Cooperative Multiagent Deep Reinforcement Learning

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1728
Author(s):  
Taufik Nur Adi ◽  
Hyerim Bae ◽  
Yelita Anggiane Iskandar
Keyword(s):  
Reinforcement Learning ◽  
Waiting Time ◽  
Transportation Network ◽  
Environmental Implications ◽  
Routing Problem ◽  
Expansion Strategy ◽  
Routing Optimization ◽  
Truck Routing ◽  
Catch Up ◽  
Selection Of

Many ports worldwide continue to expand their capacity by developing a multiterminal system to catch up with the global containerized trade demand. However, this expansion strategy increases the demand for container exchange between terminals and their logistics facilities within a port, known as interterminal transport (ITT). ITT forms a complex transportation network in a large port, which must be managed efficiently given the economic and environmental implications. The use of trucks in ITT operations leads to the interterminal truck routing problem (ITTRP), which has been attracting increasing attention from researchers. One of the objectives of truck routing optimization in ITT is the minimization of empty-truck trips. Selection of the transport order (TO) based on the current truck location is critical in minimizing empty-truck trips. However, ITT entails not only transporting containers between terminals operated 24 h: in cases where containers need to be transported to a logistics facility within operating hours, empty-truck trip cost (ETTC) minimization must also consider the operational times of the transport origin and destination. Otherwise, truck waiting time might be incurred because the truck may arrive before the opening time of the facility. Truck waiting time seems trivial, but it is not, since thousands of containers move between locations within a port every day. So, truck waiting time can be a source of ITT-related costs if it is not managed wisely. Minimization of empty-truck trips and truck waiting time is considered a multiobjective optimization problem. This paper proposes a method of cooperative multiagent deep reinforcement learning (RL) to produce TO truck routes that minimize ETTC and truck waiting time. Two standard algorithms, simulated annealing (SA) and tabu search (TS) were chosen to assess the performance of the proposed method. The experimental results show that the proposed method represents a considerable improvement over the other algorithms.

scholarly journals Interterminal Truck Routing Optimization Using Deep Reinforcement Learning

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5794
Author(s):  
Taufik Nur Adi ◽  
Yelita Anggiane Iskandar ◽  
Hyerim Bae
Keyword(s):  
Reinforcement Learning ◽  
Transport Process ◽  
Time Window ◽  
Freight Transportation ◽  
Container Transport ◽  
Routing Optimization ◽  
The World ◽  
Truck Routing ◽  
Trucking Company ◽  
Significant Factors

The continued growth of the volume of global containerized transport necessitates that most of the major ports in the world improve port productivity by investing in more interconnected terminals. The development of the multiterminal system escalates the complexity of the container transport process and increases the demand for container exchange between different terminals within a port, known as interterminal transport (ITT). Trucks are still the primary modes of freight transportation to transport containers among most terminals. A trucking company needs to consider proper truck routing planning because, based on several studies, it played an essential role in coordinating ITT flows. Furthermore, optimal truck routing in the context of ITT significantly affects port productivity and efficiency. The study of deep reinforcement learning in truck routing optimization is still limited. In this study, we propose deep reinforcement learning to provide truck routes of a given container transport order by considering several significant factors such as order origin, destination, time window, and due date. To assess its performance, we compared between the proposed method and two approaches that are used to solve truck routing problems. The experiment results showed that the proposed method obtains considerably better results compared to the other algorithms.

Study on Path Selection of Emergency Rescue Based on GIS

2013 ◽  
Vol 864-867 ◽  
pp. 2804-2807 ◽  
Author(s):  
Hai Long Sun ◽  
Li Yan Yue ◽  
Sheng Yong Yao
Keyword(s):  
Transportation Network ◽  
Path Selection ◽  
Route Optimization ◽  
Ant Algorithm ◽  
Dynamic Vehicle Routing ◽  
Emergency Rescue ◽  
Routing Optimization ◽  
Urban Disaster ◽  
Vehicle Routing Optimization ◽  
Selection Of

In the process of urban disaster emergency rescue, vehicle route optimization has become a key problem of city emergency rescue. In order to improve the emergency rescue responsiveness, a dynamic vehicle routing optimization model is established, which takes dynamic transportation network as the research object and travel time as the goal. At the same time, an improved ant algorithm is used to solve the model, which achieves the desired objectives.

Enterprise distribution routing optimization with soft time windows based on genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Kaixian Gao ◽  
Guohua Yang ◽  
Xiaobo Sun
Keyword(s):  
Genetic Algorithm ◽  
Customer Satisfaction ◽  
Optimization Problems ◽  
Rapid Development ◽  
Time Windows ◽  
Theory And Practice ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
A Company

With the rapid development of the logistics industry, the demand of customer become higher and higher. The timeliness of distribution becomes one of the important factors that directly affect the profit and customer satisfaction of the enterprise. If the distribution route is planned rationally, the cost can be greatly reduced and the customer satisfaction can be improved. Aiming at the routing problem of A company’s vehicle distribution link, we establish mathematical models based on theory and practice. According to the characteristics of the model, genetic algorithm is selected as the algorithm of path optimization. At the same time, we simulate the actual situation of a company, and use genetic algorithm to plan the calculus. By contrast, the genetic algorithm suitable for solving complex optimization problems, the practicability of genetic algorithm in this design is highlighted. It solves the problem of unreasonable transportation of A company, so as to get faster efficiency and lower cost.

Interactive Spatial Decision-Support System for Multiobjective Hazardous Materials Location-Routing Problems

1997 ◽  
Vol 1602 (1) ◽  
pp. 101-109 ◽  
Author(s):  
João Coutinho-Rodrigues ◽  
John Current ◽  
João Climaco ◽  
Samuel Ratick
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Hazardous Materials ◽  
Spatial Decision Support System ◽  
Spatial Decision Support ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Selection Of

Hazardous materials (hazmat) logistics management has received increased attention in the past two decades. Important decisions in such management include the selection of sites for hazmat processing and storage, the selection of transportation routes from sources to processing facilities, and the determination of quantities of hazmat shipped over these routes. These decisions are frequently based on multiple criteria (e.g., cost, risk, equity). A personal computer–based, interactive spatial decision-support system was designed to assist decision makers with such problems. Although presented within the framework of a hazmat problem, the system’s components can be modified to analyse any multiobjective location, routing, or location-routing problem.

scholarly journals A Deep Reinforcement Learning Approach for Global Routing

2019 ◽  
Vol 142 (6) ◽  
Author(s):  
Haiguang Liao ◽  
Wentai Zhang ◽  
Xuliang Dong ◽  
Barnabas Poczos ◽  
Kenji Shimada ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Integrated Circuits ◽  
Printed Circuit Boards ◽  
Greedy Algorithms ◽  
Global Routing ◽  
Hydraulic Systems ◽  
Routing Problem ◽  
Future Data ◽  
Circuit Components ◽  
Optimization Mechanism

Abstract Global routing has been a historically challenging problem in the electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules for the printed circuit boards or integrated circuits. Similar routing problems also exist in the design of complex hydraulic systems, pipe systems, and logistic networks. Existing solutions typically consist of greedy algorithms and hard-coded heuristics. As such, existing approaches suffer from a lack of model flexibility and usually fail to solve sub-problems conjointly. As an alternative approach, this work presents a deep reinforcement learning method for solving the global routing problem in a simulated environment. At the heart of the proposed method is deep reinforcement learning that enables an agent to produce a policy for routing based on the variety of problems, and it is presented with leveraging the conjoint optimization mechanism of deep reinforcement learning. Conjoint optimization mechanism is explained and demonstrated in detail; the best network structure and the parameters of the learned model are explored. Based on the fine-tuned model, routing solutions and rewards are presented and analyzed. The results indicate that the approach can outperform the benchmark method of a sequential A* method, suggesting a promising potential for deep reinforcement learning for global routing and other routing or path planning problems in general. Another major contribution of this work is the development of a global routing problem sets generator with the ability to generate parameterized global routing problem sets with different size and constraints, enabling evaluation of different routing algorithms and the generation of training datasets for future data-driven routing approaches.

scholarly journals Modeling the Multicommodity Multimodal Routing Problem with Schedule-Based Services and Carbon Dioxide Emission Costs

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Yan Sun ◽  
Maoxiang Lang
Keyword(s):  
Carbon Dioxide ◽  
Carbon Dioxide Emissions ◽  
Transportation Network ◽  
Linearization Method ◽  
Mixed Integer ◽  
Multicommodity Flow ◽  
Routing Problem ◽  
Multimodal Transportation ◽  
Proposed Model ◽  
Freight Routing

We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network. This problem belongs to the operational level of service network planning. The following formulation characteristics will be comprehensively considered: (1) multicommodity flow routing; (2) a capacitated multimodal transportation network with schedule-based rail services and time-flexible road services; (3) carbon dioxide emissions consideration; and (4) a generalized costs optimum oriented to customer demands. The specific planning of freight routing is thus defined as a capacitated time-sensitive multicommodity multimodal generalized shortest path problem. To solve this problem systematically, we first establish a node-arc-based mixed integer nonlinear programming model that combines the above formulation characteristics in a comprehensive manner. Then, we develop a linearization method to transform the proposed model into a linear one. Finally, a computational experiment from the Chinese inland container export business is presented to demonstrate the feasibility of the model and linearization method. The computational results indicate that implementing the proposed model and linearization method in the mathematical programming software Lingo can effectively solve the large-scale practical multicommodity multimodal transportation routing problem.

Sign in / Sign up

Export Citation Format

Share Document