scholarly journals Multi-Objective Optimization of Differentiated Urban Ring Road Bus Lines and Fares Based on Travelers’ Interactive Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2301
Author(s):  
Xueyan Li ◽  
Xin Zhu ◽  
Baoyu Li
Keyword(s):  
Reinforcement Learning ◽  
Traffic Management ◽  
Programming Model ◽  
Solution Algorithm ◽  
Travel Choice ◽  
Agent Model ◽  
Ring Road ◽  
Multi Objective ◽  
Multi Agent ◽  
Upper Level

This paper proposes a new multi-objective bi-level programming model for the ring road bus lines and fare design problems. The proposed model consists of two layers: the traffic management operator and travelers. In the upper level, we propose a multi-objective bus lines and fares optimization model in which the operator’s profit and travelers’ utility are set as objective functions. In the lower level, evolutionary multi agent model of travelers’ bounded rational reinforcement learning with social interaction is introduced. A solution algorithm for the multi-objective bi-level programming is developed on the basis of the equalization algorithm of OD matrix. A numerical example based on a real case was conducted to verify the proposed models and solution algorithm. The computational results indicated that travel choice models with different degrees of rationality significantly changed the optimization results of bus lines and the differentiated fares; furthermore, the multi-objective bi-level programming in this paper can generate the solution to reduce the maximum section flow, increase the profit, and reduce travelers’ generalized travel cost.

scholarly journals Scalarized Q Multi-Objective Reinforcement Learning for Area Coverage Control and Light Control Implementation

2018 ◽  
Vol 16 (2) ◽  
pp. 72-82
Author(s):  
Akkhachai Phuphanin ◽  
Wipawee Usaha
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Area Coverage ◽  
Coverage Area ◽  
Lighting Control ◽  
Multi Objective ◽  
Coverage Control ◽  
Control Schemes ◽  
Multi Agent ◽  
Testbed Experiments

Coverage control is crucial for the deployment of wireless sensor networks (WSNs). However, most coverage control schemes are based on single objective optimization such as coverage area only, which do not consider other contradicting objectives such as energy consumption, the number of working nodes, wasteful overlapping areas. This paper proposes on a Multi-Objective Optimization (MOO) coverage control called Scalarized Q Multi-Objective Reinforcement Learning (SQMORL). The two objectives are to achieve the maximize area coverage and to minimize the overlapping area to reduce energy consumption. Performance evaluation is conducted for both simulation and multi-agent lighting control testbed experiments. Simulation results show that SQMORL can obtain more efficient area coverage with fewer working nodes than other existing schemes.  The hardware testbed results show that SQMORL algorithm can find the optimal policy with good accuracy from the repeated runs.

Park-and-Ride Network Design: A Goal Programming Approach

2014 ◽  
Vol 1030-1032 ◽  
pp. 2065-2068
Author(s):  
Xin Yuan Chen ◽  
Zhi Yuan Liu ◽  
Wei Deng
Keyword(s):  
Network Design ◽  
Goal Programming ◽  
Design Problem ◽  
Programming Model ◽  
Search Algorithm ◽  
Network Design Problem ◽  
Programming Approach ◽  
Multi Objective ◽  
Park And Ride ◽  
Upper Level

The paper addresses a park and ride network design problem in a bi-model transport network in a multi-objective decision making framework. A goal programming approach is adopted to solve the multi-objective park and ride network design problem. The goal programming approach considers the user-defined goals and priority structure, which are (i) traffic-efficient goal, (ii) total transit usage goal, (iii) spatial equity goal. This problem is formulated as a bi-level programming model. The upper level programming leads to minimize the deviation from stated goals in the context of a given priority ranking. While the lower level programming model is a modal split/traffic assignment model which is used to assess any given park and ride scheme. A heuristic tabu search algorithm is then adopted to solve this model.

scholarly journals Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39974-39982 ◽  
Author(s):  
Yuandou Wang ◽  
Hang Liu ◽  
Wanbo Zheng ◽  
Yunni Xia ◽  
Yawen Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Workflow Scheduling ◽  
Multi Objective ◽  
Multi Agent

scholarly journals Multi-Objective Human Resource Allocation Approach for Sustainable Traffic Management

2020 ◽  
Vol 17 (7) ◽  
pp. 2470 ◽  
Author(s):  
Soumendra Nath Sanyal ◽  
Izabela Nielsen ◽  
Subrata Saha
Keyword(s):  
Resource Allocation ◽  
Human Resources ◽  
Human Resource ◽  
Traffic Management ◽  
Optimal Allocation ◽  
Road Traffic ◽  
Programming Model ◽  
Binary Programming ◽  
Multi Objective ◽  
Road Segments

Efficient human resource deployment is one of the key aspects of road traffic management for maintaining the lifelines of any metropolitan city. The problem becomes relevant when collaboration between human resources with different skills in day-to-day operations is necessary to maintain public and commercial transport, manage various social events and emergency situations, and hence reduce congestion, injuries, emissions, etc. This study proposes a two-phase fuzzy multi-objective binary programming model for optimal allocation of five different categories of human resources to minimize the overall operational cost, maximize the allocation to accident-prone road segments, minimize the number of volunteer personnel and maximize the direct contact to reduce emissions and road traffic violations, simultaneously. A binary programming model is formulated to provide an efficient individual manpower allocation schedule for multiple road segments at different shifts. A case study is proposed for model evaluation and to derive managerial implications. The proposed model can be used to draw insights into human resource allocation planning in traffic management to reduce road traffic congestion, injuries and vehicular emissions.

scholarly journals Adaptive Optics control with Multi-Agent Model-Free Reinforcement Learning

2022 ◽  
Author(s):  
Bartomeu Mulet ◽  
Florian Ferreira ◽  
Eduardo Quinones ◽  
Damien Gratadour ◽  
Mario Martin
Keyword(s):  
Reinforcement Learning ◽  
Adaptive Optics ◽  
Agent Model ◽  
Model Free ◽  
Multi Agent

Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems

2014 ◽  
Vol 41 (2) ◽  
pp. 551-562 ◽  
Author(s):  
Leonardo Anjoletto Ferreira ◽  
Carlos Henrique Costa Ribeiro ◽  
Reinaldo Augusto da Costa Bianchi
Keyword(s):  
Reinforcement Learning ◽  
Multi Objective ◽  
Multi Agent
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