A Multi-Objective Bus Rapid Transit Energy Saving Dispatching Optimization Considering Multiple Types of Vehicles

Yang, Xinfeng; Liu, Lanfen

doi:10.1109/access.2020.2989334

Cited by 22 publications

(10 citation statements)

References 30 publications

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“…The initialization is an important process in GA, serving the role of randomly initializing the solution in the feasible region [32]. In what follows, the feasible regions of two decision variables in each period are analyzed.…”

Section: Initializationmentioning

confidence: 99%

Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

Liu

Yang

2021

Int J Comput Intell Syst

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The diversity of products and fierce competition make the stability and production cost of manufacturing industry more important. So, the purpose of this paper is to deal with the multi-product aggregate production planning (APP) problem considering stability in the workforce and total production costs, and propose an efficient algorithm. Taking into account the relationship of raw materials, inventory cost and product demand, a multi-objective programming model for multi-product APP problem is established to minimize total production costs and instability in the work force. To improve the efficiency of the algorithm, the feasible region of the planned production and the number of workers in each period are determined and a local search algorithm is used to improve the search ability. Based on the analysis of the feasible range, a genetic algorithm is designed to solve the model combined with the local search algorithm. For analyzing the effect of this algorithm, the information entropy strategy, NSGA-II strategy and multi-population strategy are compared and analyzed with examples, and the simulation results show that the model is feasible, and the NSGA-II algorithm based on the local search has a better performance in the multi-objective APP problem.

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Section: Initializationmentioning

confidence: 99%

Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

Liu

Yang

2021

Int J Comput Intell Syst

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“…Researchers have applied various approaches such as genetic algorithm (Yang and Liu (2020), Sun et al (2020), Santos et al ( 2016)), simulated annealing (Zhou et al (2020)), and column generation (Li (2014)) in the domain of energy-efficient bus scheduling with either liquid-fuel buses or electric buses. But only few research efforts (e.g., Santos et al (2016), Zhou et al (2020)) focused on transit networks operating mixed-fleets of buses.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

Sivagnanam

Ayman

Wilbur

et al. 2021

AAAI

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Affordable public transit services are crucial for communities since they enable residents to access employment, education, and other services. Unfortunately, transit services that provide wide coverage tend to suffer from relatively low utilization, which results in high fuel usage per passenger per mile, leading to high operating costs and environmental impact. Electric vehicles (EVs) can reduce energy costs and environmental impact, but most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging, while serving an existing fixed-route transit schedule. We present an integer program for optimal assignment and scheduling, and we propose polynomial-time heuristic and meta-heuristic algorithms for larger networks. We evaluate our algorithms on the public transit service of Chattanooga, TN using operational data collected from transit vehicles. Our results show that the proposed algorithms are scalable and can reduce energy use and, hence, environmental impact and operational costs. For Chattanooga, the proposed algorithms can save $145,635 in energy costs and 576.7 metric tons of CO2 emission annually.

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“…The result shows that the energy demand for the chosen scenario decreased by 12.14%. On the other hand, Yang and Liu [25] presented a mechanical model for the energy consumption of different vehicles depending upon driving style, road condition, and vehicle types.…”

Section: Theoretical Framework Of Related Researchmentioning

confidence: 99%

Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

et al. 2020

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Public transportation is a vital service provided to enable a community to carry out daily activities. One of the mass transportations used in an area is a bus. Moreover, the smart transportation concept is an integrated application of technology and strategy in the transportation system. Using smart idea is the key to the application of the Internet of Things. The ways to improve the management transportation system become a bottleneck for the traditional data analytics solution, one of the answers used in machine learning. This paper uses the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithm for the best prediction of travel time with a lower error rate on a case study of a university shuttle bus. Apart from predicting the travel time, this study also considers the fuel cost and gas emission from transportation. The analysis of the experiment shows that the ANN outperformed the SVM. Furthermore, a recommender system is used to recommend suitable routes for the chosen scenario. The experiments extend the discussion with a range of future directions on the stipulated field of study.

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A Multi-Objective Bus Rapid Transit Energy Saving Dispatching Optimization Considering Multiple Types of Vehicles

Cited by 22 publications

References 30 publications

Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

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