Advanced Navigation Route Optimization for an Oceangoing Vessel

2011 ◽  
pp. 149-155 ◽  
Author(s):  
E Kobayashi ◽  
T Asajima ◽  
N Sueyoshi
Keyword(s):  
Route Optimization

Route Optimization of Construction Machine by Deep Reinforcement Learning

2019 ◽  
Vol 139 (4) ◽  
pp. 401-408
Author(s):  
Shunya Tanabe ◽  
Zeyuan Sun ◽  
Masayuki Nakatani ◽  
Yutaka Uchimura
Keyword(s):  
Reinforcement Learning ◽  
Route Optimization ◽  
Construction Machine

Cross-provincial Cross-region Power Trading Optimization Modeling

2020 ◽  
Vol 13 (8) ◽  
pp. 1183-1189
Author(s):  
Jing-wen Chen ◽  
Yan Xiao ◽  
Hong-she Dang ◽  
Rong Zhang
Keyword(s):  
Network Flow ◽  
Optimal Allocation ◽  
Route Optimization ◽  
Regional Power ◽  
Power Resources ◽  
Gray Theory ◽  
Power Trading ◽  
Study Results ◽  
Multi Agent ◽  
Regional Economic Disparities

Background: China's power resources are unevenly distributed in geography, and the supply-demand imbalance becomes worse due to regional economic disparities. It is essential to optimize the allocation of power resources through cross-provincial and cross-regional power trading. Methods: This paper uses load forecasting, transaction subject data declaration, and route optimization models to achieve optimal allocation of electricity and power resources cross-provincial and cross-regional and maximize social benefits. Gray theory is used to predict the medium and longterm loads, while multi-agent technology is used to report the power trading price. Results: Cross-provincial and cross-regional power trading become a network flow problem, through which we can find the optimized complete trading paths. Conclusion: Numerical case study results has verified the efficiency of the proposed method in optimizing power allocation across provinces and regions.

Spatially Enabled Pipeline Route Optimization Model

2004 ◽  
Author(s):  
S. Raza Wasi ◽  
J. Darren Bender
Keyword(s):  
Decision Making ◽  
Optimization Problem ◽  
Environmental Design ◽  
Route Optimization ◽  
Design Criteria ◽  
Least Cost Path ◽  
Ahp Model ◽  
Spatial Decision Making ◽  
Pipeline Route ◽  
Analytical Tools

An interesting, potentially useful, and fully replicable application of a spatially enabled decision model is presented for pipeline route optimization. This paper models the pipeline route optimization problem as a function of engineering and environmental design criteria. The engineering requirements mostly deal with capital, operational and maintenance costs, whereas environmental considerations ensure preservation of nature, natural resources and social integration. Typically, pipelines are routed in straight lines, to the extent possible, to minimize the capital construction costs. In contrast, longer pipelines and relatively higher costs may occur when environmental and social considerations are part of the design criteria. Similarly, much longer pipelines are less attractive in terms of capital costs and the environmental hazard associated with longer construction area. The pipeline route optimization problem is potentially a complex decision that is most often undertaken in an unstructured, qualitative fashion based on human experience and judgement. However, quantitative methods such as spatial analytical techniques, particularly the least-cost path algorithms, have greatly facilitated automation of the pipeline routing process. In the past several interesting studies have been conducted using quantitative spatial analytical tools for finding the best pipeline route or using non-spatial decision making tools to evaluate several alternates derived through conventional route reconnaissance methods. Most of these studies (that the authors are familiar with) have concentrated on integrating multiple sources of spatial data and performing quantitative least-cost path analysis or have attempted to make use of non-spatial decision making tools to select the best route. In this paper, the authors present a new framework that incorporates quantitative spatial analytical tools with an Analytical Hierarchical Process (AHP) model to provide a loosely integrated but efficient spatial Decision Support System (DSS). Specifically, the goal is to introduce a fully replicable spatial DSS that processes both quantitative and qualitative information, balances between lowest-cost and lowest-impact routes. The model presented in this paper is implemented in a four step process: first, integration of multiple source data that provide basis for engineering and environmental design criteria; second, creation of several alternate routes; third, building a comprehensive decision matrix using spatial analysis techniques; and fourth, testing the alternative and opinions of the stakeholder groups on imperatives of AHP model to simplify the route optimization decision. The final output of the model is then used to carry out sensitivity analysis, quantify the risk, generate “several what and if scenarios” and test stability of the route optimization decision.

scholarly journals Tourist Route Optimization in the Context of Covid-19 Pandemic

2021 ◽  
Vol 13 (10) ◽  
pp. 5492
Author(s):  
Cristina Maria Păcurar ◽  
Ruxandra-Gabriela Albu ◽  
Victor Dan Păcurar
Keyword(s):  
Travelling Salesman Problem ◽  
Route Planning ◽  
Route Optimization ◽  
Travelling Salesman ◽  
Social Distancing ◽  
Tourist Destinations ◽  
Innovative Method ◽  
The Social ◽  
Immediate Response

The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can bring an important advantage in transforming a destination into a safer one in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought into spotlight the issue of overcrowded attractions inside a destination at certain moments. The method presented in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented is aimed to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating conformation with the social distancing measures imposed for Covid-19 control.

scholarly journals MTX optimization or adding bDMARD equally improve disease activity in rheumatoid arthritis: results from the prospective study STRATEGE

Rheumatology ◽  
2021 ◽  
Author(s):  
Cécile Gaujoux-Viala ◽  
Christophe Hudry ◽  
Elena Zinovieva ◽  
Hélène Herman-Demars ◽  
René-Marc Flipo
Keyword(s):  
Disease Activity ◽  
Biological Treatment ◽  
Current Practice ◽  
Treatment Strategies ◽  
Route Optimization ◽  
Treat To Target ◽  
Inadequate Response ◽  
Inclusion Criteria ◽  
Treatment Optimization

Abstract Objectives The STRATEGE study aimed to describe treatment strategies in current practice in RA bDMARD-naive patients with an inadequate response to MTX therapy, and to compare clinical efficacy of the different therapeutic strategies on disease activity after six months. Methods Main inclusion criteria of this prospective, observational, multicentre study were confirmed RA diagnosis, treatment by MTX monotherapy, and need for therapeutic management modification. Results The 722 patients included had a mean (S.D.) RA duration of 5.3 (6.7) years, a mean DAS28 of 4.0 (±1.1); they were all receiving MTX monotherapy, 68% oral, at a mean dose of 15.0 (4.1) mg/wk. Two major strategies were identified: (i) MTX monotherapy dose and/or route optimization (72%) and (ii) bDMARD initiation ± MTX (16%). MTX dosing was modified for 70% of patients, maintained (dose and route) for 28% of patients, and interrupted for 2%. bDMARDs were started when the MTX mean dose was 17.4 mg/wk, 56% parenterally; MTX was maintained concomitantly for 96% of patients. Six-month follow-up results adjusted by propensity score showed that both options were equally successful in improving disease activity and physical function, with 63% and 68% of good-to-moderate EULAR responses, respectively. Conclusion The STRATEGE study shows the importance of initial MTX treatment optimization before initiation of a biological treatment and emphasizes the importance of treat-to-target strategy.

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