scholarly journals Joint Optimization of Regular Charging Electric Bus Transit Network Schedule and Stationary Charger Deployment considering Partial Charging Policy and Time-of-Use Electricity Prices

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xinghua Li ◽  
Tianzuo Wang ◽  
Lingjie Li ◽  
Feiyu Feng ◽  
Wei Wang ◽  
...  
Keyword(s):  
Traffic Noise ◽  
Joint Optimization ◽  
Maintenance Cost ◽  
Adaptive Genetic Algorithm ◽  
Electricity Prices ◽  
Transit System ◽  
Transit Network ◽  
Bus Transit ◽  
Electric Bus ◽  
Network Schedule

Electric buses (EBs) have been implemented worldwide and exhibited great potential for air pollution reduction and traffic noise control. In regular charging scenarios, the deployment of charging facilities and the operational scheduling of the transit system is crucial to bus transit system management. In this paper, we proposed a joint optimization model of regular charging electric bus transit network schedule and stationary charger deployment considering partial charging policy and time-of-use electricity prices. The objective of the model is to minimize the total investment cost of the transit system including the capital and maintenance cost of EBs and chargers, the power consumption cost, and time-related in-service cost. A solving procedure based on the improved adaptive genetic algorithm (AGA) is further designed and a transit network at inner Anting Town, Shanghai, with 8 individual bus routes and 867 daily service trips is adopted for the model validation. The validation results illustrated that the methodology considering the partial charging policy can arrange the charging schedule adaptive to the time-of-use electricity prices. Compared with the benchmark of single line separate scheduling, the proposed model can yield 3 million RMB investment saving by highly utilizing EBs and battery chargers.

scholarly journals Enabling In-Depot Automated Routing and Recharging Scheduling for Automated Electric Bus Transit Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lei Wang ◽  
Wanjing Ma ◽  
Ling Wang ◽  
Yongli Ren ◽  
Chunhui Yu
Keyword(s):  
Autonomous Vehicles ◽  
Job Shop ◽  
Optimal Solution ◽  
Transit System ◽  
Transit Systems ◽  
Bus Transit ◽  
Electric Bus ◽  
Minimum Number ◽  
First Come First Serve ◽  
High Level

The bus transit system is promising to enable electric and autonomous vehicles for massive urban mobility, which relies on high-level automation and efficient resource management. Besides the on-road automation, the in-depot automated scheduling for battery recharging has not been adequately studied yet. This paper presents an integrated in-depot routing and recharging scheduling (IDRRS) problem, which is modeled as a constraint programming (CP) problem with Boolean satisfiability conditions (SAT). The model is converted to a flexible job-shop problem (FJSP) and is feasible to be solved by a CP-SAT solver for the optimal solution or feasible solutions with acceptable performance. This paper also presents a case study in Shanghai and compares the results from the FJSP model and the first-come first-serve (FCFS) method. The result demonstrates the allocation of routes and chargers under multiple scenarios with different numbers of chargers. The results show that the FJSP model shortens the delay and increases the time conservation for future rounds of operation than FCFS, while FCFS presents the simplicity of programming and better computational efficiency. The multiple random input test suggests that the proposed approach can decide the minimum number of chargers for stochastic charging requests. The proposed method can conserve the investment by increasing the utilization of automated recharging devices, improving vehicles’ in-depot efficiency.

A novel fast-charging stations locational planning model for electric bus transit system

Energy ◽  
2021 ◽  
Vol 224 ◽  
pp. 120106
Author(s):  
Xiaomei Wu ◽  
Qijin Feng ◽  
Chenchen Bai ◽  
Chun Sing Lai ◽  
Youwei Jia ◽  
...  
Keyword(s):  
Planning Model ◽  
Transit System ◽  
Bus Transit ◽  
Electric Bus ◽  
Fast Charging ◽  
Charging Stations

scholarly journals Bus Transit Network Structure Selection With Multiple Objectives

2021 ◽  
Vol 12 (4) ◽  
pp. 1-13
Author(s):  
K. Ramacandra Rao ◽  
Subhro Mitra ◽  
Joseph Szmerekovsky
Keyword(s):  
Network Structure ◽  
Public Transportation ◽  
Rational Design ◽  
Ad Hoc ◽  
Planning Process ◽  
Transit System ◽  
Transit Network ◽  
Structure Selection ◽  
Bus Transit ◽  
Bus Transportation

Bus transportation is the essential mode of public transportation available for intra-district movements in India. The planning of different stages of bus transportation planning is usually done in an ad-hoc manner on the basis of the experience of the operators. For a rational design of the bus transit system, it is essential to take into account the objectives of different interest groups. Selection of an appropriate network structure is an essential part of the planning process. In this paper, a model developed for generating a number of alternative network structures using link deletion concept is presented. One of these alternatives can be selected on the basis of the trade-off between the user and operator objectives. The model has been applied to a case study of bus transit network of Visakhapatnam region in Andhra Pradesh.

Accessibility Scenario Analysis of a Hypothetical Future Transit Network: Social Equity Implications of a General Transit Feed Specification–Based Sketch Planning Tool

2017 ◽  
Vol 2671 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Andrew Guthrie ◽  
Yingling Fan ◽  
Kirti Vardhan Das
Keyword(s):  
Planning Process ◽  
Social Equity ◽  
Social Benefit ◽  
Planning Tool ◽  
Transit Planning ◽  
Transit System ◽  
Transit Systems ◽  
Transit Network ◽  
Accessibility Analysis ◽  
Transit Accessibility

Accessibility analysis can have important implications for understanding social equity in transit planning. The emergence and the increasingly broad acceptance of the general transit feed specification (GTFS) format for transit route, stop, and schedule data have revolutionized transit accessibility research by providing researchers with a convenient, publicly available source of data interoperable with common geographic information system (GIS) software. Existing approaches to GTFS-based transit analysis, however, focus on currently operating transit systems. With major transit expansions across the nation and around the world increasing in number and ambition, understanding the accessibility impacts of proposed projects in their early planning stages is crucial to achieving the greatest possible social benefit from these massive public investments. This paper describes the development of a hypothetical transit network based on current GTFS data and proposed 2040 transit improvements for the Twin Cities region of Minneapolis–Saint Paul, Minnesota, as well as its use as a sketch planning tool in exploring the proposed system’s impacts on access to job vacancies from historically disadvantaged areas. This research demonstrates the importance of accessibility analysis in planning a transit system that increases opportunity for marginalized workers and concludes by calling for broader, easier access to accessibility analysis for practitioners and community groups to refine the early stages of the transit planning process and democratize an increasingly crucial transit planning tool.

scholarly journals Integrated Tomato Picking and Distribution Scheduling Based on Maturity

2020 ◽  
Vol 12 (19) ◽  
pp. 7934
Author(s):  
Anqi Zhu ◽  
Bei Bian ◽  
Yiping Jiang ◽  
Jiaxiang Hu
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Joint Optimization ◽  
Adaptive Genetic Algorithm ◽  
Maturity Model ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Order Processing ◽  
Supply Chain Efficiency ◽  
Distribution Scheduling

Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.

Multiagent Learning-Based Approach to Transit Assignment Problem

2005 ◽  
Vol 1926 (1) ◽  
pp. 96-105 ◽  
Author(s):  
Mohammed Wahba ◽  
Amer Shalaby
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Assignment Problem ◽  
System Performance ◽  
Transit Assignment ◽  
Peak Period ◽  
Transit System ◽  
Transit Network ◽  
Passenger Travel ◽  
Transit Routes

This paper presents an operational prototype of an innovative framework for the transit assignment problem, structured in a multiagent way and inspired by a learning-based approach. The proposed framework is based on representing passengers and their learning and decision-making activities explicitly. The underlying hypothesis is that individual passengers are expected to adjust their behavior (i.e., trip choices) according to their experience with transit system performance. A hypothetical transit network, which consists of 22 routes and 194 stops, has been developed within a microsimulation platform (Paramics). A population of 3,000 passengers was generated and synthesized to model the transit assignment process in the morning peak period. Using reinforcement learning to represent passengers’ adaptation and accounting for differences in passengers’ preferences and the dynamics of the transit network, the prototype has demonstrated that the proposed approach can simultaneously predict how passengers will choose their routes and estimate the total passenger travel cost in a congested network as well as loads on different transit routes.

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