scholarly journals A Deep Q-Learning Network for Ship Stowage Planning Problem

2017 ◽  
Vol 24 (s3) ◽  
pp. 102-109 ◽  
Author(s):  
Yifan Shen ◽  
Ning Zhao ◽  
Mengjue Xia ◽  
Xueqiang Du
Keyword(s):  
Planning Problem ◽  
Crane Scheduling ◽  
Online Searching ◽  
Q Learning ◽  
Learning Network ◽  
Reward Function ◽  
Algorithm Efficiency ◽  
Training Stage ◽  
Stowage Planning ◽  
Stowage Plan

Abstract Ship stowage plan is the management connection of quae crane scheduling and yard crane scheduling. The quality of ship stowage plan affects the productivity greatly. Previous studies mainly focuses on solving stowage planning problem with online searching algorithm, efficiency of which is significantly affected by case size. In this study, a Deep Q-Learning Network (DQN) is proposed to solve ship stowage planning problem. With DQN, massive calculation and training is done in pre-training stage, while in application stage stowage plan can be made in seconds. To formulate network input, decision factors are analyzed to compose feature vector of stowage plan. States subject to constraints, available action and reward function of Q-value are designed. With these information and design, an 8-layer DQN is formulated with an evaluation function of mean square error is composed to learn stowage planning. At the end of this study, several production cases are solved with proposed DQN to validate the effectiveness and generalization ability. Result shows a good availability of DQN to solve ship stowage planning problem.

Solving the 3D stowage planning problem integrated with the quay crane scheduling problem by representation by rules and genetic algorithm

2018 ◽  
Vol 65 ◽  
pp. 495-516 ◽  
Author(s):  
Anibal Tavares Azevedo ◽  
Luiz Leduino de Salles Neto ◽  
Antônio Augusto Chaves ◽  
Antônio Carlos Moretti
Keyword(s):  
Genetic Algorithm ◽  
Planning Problem ◽  
Scheduling Problem ◽  
Quay Crane Scheduling ◽  
Crane Scheduling ◽  
Stowage Planning

scholarly journals Group-Bay Stowage Planning Problem for Container Ship

2016 ◽  
Vol 23 (s1) ◽  
pp. 152-159
Author(s):  
Shen Yifan ◽  
Zhao Ning ◽  
Mi Weijian
Keyword(s):  
Hybrid Algorithm ◽  
Container Terminal ◽  
Planning Problem ◽  
Container Ship ◽  
Planning Model ◽  
Target Weight ◽  
The Core ◽  
Ship Owner ◽  
Stowage Planning ◽  
Stowage Plan

Abstract Stowage planning is the core of ship planning. It directly influences the seaworthiness of container ship and the handling efficiency of container terminal. As the latter step of container ship stowage plan, terminal stowage planning optimizes terminal cost according to pre-plan. Group-Bay stowage planning is the smallest sub problem of terminal stowage planning problem. A group-bay stowage planning model is formulated to minimize relocation, crane movement and target weight gap satisfying both ship owner and container terminal. A GA-A* hybrid algorithm is designed to solve this problem. Numerical experiment shown the validity and the efficiency.

scholarly journals Integrating Production Planning with Truck-Dispatching Decisions through Reinforcement Learning While Managing Uncertainty

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 587
Author(s):  
Joao Pedro de Carvalho ◽  
Roussos Dimitrakopoulos
Keyword(s):  
Reinforcement Learning ◽  
Discrete Event ◽  
Mining Operations ◽  
Fixed Sequence ◽  
Q Learning ◽  
Reward Function ◽  
Copper Gold ◽  
Mining Complex ◽  
Learning Reinforcement ◽  
Operational Plan

This paper presents a new truck dispatching policy approach that is adaptive given different mining complex configurations in order to deliver supply material extracted by the shovels to the processors. The method aims to improve adherence to the operational plan and fleet utilization in a mining complex context. Several sources of operational uncertainty arising from the loading, hauling and dumping activities can influence the dispatching strategy. Given a fixed sequence of extraction of the mining blocks provided by the short-term plan, a discrete event simulator model emulates the interaction arising from these mining operations. The continuous repetition of this simulator and a reward function, associating a score value to each dispatching decision, generate sample experiences to train a deep Q-learning reinforcement learning model. The model learns from past dispatching experience, such that when a new task is required, a well-informed decision can be quickly taken. The approach is tested at a copper–gold mining complex, characterized by uncertainties in equipment performance and geological attributes, and the results show improvements in terms of production targets, metal production, and fleet management.

A Decision Support System for Containership Stowage Planning

1989 ◽  
Vol 26 (01) ◽  
pp. 47-61
Author(s):  
D. J. Saginaw ◽  
A. N. Perakis
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Interactive Graphics ◽  
Maritime Industry ◽  
Automate Data ◽  
Plan Generation ◽  
Stowage Planning ◽  
Definition Of ◽  
Stowage Plan

The results of a project intending to design and develop a microcomputer-based, interactive graphics decision support system for containership stowage planning are presented. The objective was to create a working prototype that would automate data management tasks and provide computational capabilities to allow the stowage planner to continuously assess vessel trim, stability, and strength characteristics. The paper provides a complete description of the decision support system developed to meet this objective, including a definition of the containership stowage problem, and details on the design and development of the Automated Stowage Plan Generation Routine (ASPGR). The paper concludes with a discussion of issues relevant to the implementation of the system in the maritime industry.

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